CHARACTERIZATION OF ACTIVE JOINT COUNT TRAJECTORIES IN
JUVENILE IDIOPATHIC ARTHRITIS
By
Roberta Berard MD FAAP FRCPC
A thesis submitted in conformity with the requirements for the degree of
Master of Science (Clinical Epidemiology and Health Care Research)
Graduate Department of Health Policy, Management and Evaluation
University of Toronto
© Copyright by Roberta Berard 2011
ii
Characterization of active joint count trajectories in juvenile idiopathic arthritis
Roberta Berard, Master of Science (Clinical Epidemiology and Health Care Research)
Graduate Department of Health Policy, Management and Evaluation
University of Toronto
2011
Abstract
Aim: To describe the longitudinal active joint count (AJC) trajectories in juvenile idiopathic
arthritis (JIA) and to examine the association of baseline characteristics with these trajectories.
Methods: A retrospective cohort study at two Canadian centres was performed. The longitudinal
trajectories of AJC were described using latent growth curve modeling (GCM). Latent GCM is
a novel technique that aims to classify individuals into statistically distinct groups based on
individual response patterns so that individuals within a group are more similar than individuals
between groups. The trajectory classes are each defined by a longitudinal growth curve. The
association of baseline characteristics stratified by trajectory group was examined by univariate
methods.
Results: Data were analyzed for 659 children diagnosed with JIA between 1990/03-2009/09. A
maximum of 10 years of follow-up data were included in the analysis. Participants were
classified into 5 statistically and clinically distinct AJC trajectories by latent GCM.
Conclusions: Using a novel longitudinal statistical method we were able to classify patients with
JIA based on their pattern of AJC over time. These results should be interpreted in light of
clinical significance. The trajectory classes will need to be examined for their relationship to
important genetic and biological predictors. Identification of patterns of disease course is
important in working towards the development of a clinically relevant outcome-based
classification system in JIA.
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Acknowledgments
I would like to thank my thesis advisory committee, Drs. Claire Bombardier, Rae Yeung,
Brian Feldman and George Tomlinson for their invaluable guidance, encouragement and
mentorship. I am so grateful for the opportunity to have worked on this exciting project under
their tutelage. I would like to thank Ms. Xiuying Li for the hours she spent with me merging and
cleaning my dataset. I am indebted to Drs. Kiem Oen and Alan Rosenberg and their research
teams for the countless hours of data collection and cleaning without which this thesis would not
have been possible. Thank you to the reviewers of my thesis, Drs. Taunton Southwood and
Rahim Moineddin, for taking the time and interest to critically evaluate my work.
Thank you to my friends who have always stood by me through my years of training.
Special thank you to my dear friend Bindee for proof-reading my thesis. To my family, who
have always supported me in my academic pursuits and have shown patience beyond words.
And to my boys, Pete and Dylan, for their unconditional love and support always.
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Table of Contents
Page
ABSTRACT ii
ACKNOWLEDGEMENTS iii
TABLE OF CONTENTS iv
LIST OF TABLES vi
LIST OF FIGURES vii
LIST OF APPENDICES viii
LIST OF ABBREVIATIONS ix
1. INTRODUCTION AND THESIS OVERVIEW 1
2. BACKGROUND 3
2.1 Juvenile idiopathic Arthritis 2.1.1 Epidemiology and Burden of Illness 3
2.1.2 Classification Criteria in JIA 4
2.1.3 Challenges with the clinical application of the International League 4
of Associations for Rheumatology criteria
2.1.4 Difficulties in defining disease course and outcomes in JIA 5
2.1.5 Predictors of Outcomes in JIA 7
2.1.6 Emerging biological evidence for etiologic heterogeneity 8
2.1.7 Summary 8
2.2 Growth Curve modeling 10
2.2.1 Conventional growth modeling 10
2.2.2 Latent variable growth curve modeling 12
2.2.3 Latent class growth analysis versus growth mixture modeling 15
2.2.4 Latent growth variable modeling – model selection 16
2.2.5 Latent growth variable modeling – clinical sensibility 17
2.2.6 Summary 18
3. RATIONALE AND RELEVANCE 20
4. OBJECTIVES AND RESEARCH HYPOTHESIS 22
5. METHODS 23
5.1 Study design and overview 23
5.2 Research Ethics Approval 23
5.3 Study Population 23
5.4 Data Management 24
5.5 Trajectory descriptive variable 25
5.6 Cohort descriptive variables 26
5.7 Analysis 27
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6. RESULTS 34
6.1. Study population 34
6.1.1. Follow-up times 34
6.1.2. Missing Visits 38
6.1.3. Cohort baseline characteristics 38
6.2. Univariate description of the AJC 40
6.3. Characteristics of patients with no active joint disease 40
6.4. Model Building 42
6.4.1. Selection of the distribution 42
6.4.2. Selection of the best fitting model 43
6.4.2.1. Fit indices 43
6.4.2.2. Classification quality 51
6.4.2.3. Clinical sensibility 53
6.4.2.4. Association of baseline characteristics with trajectories 57
7. DISCUSSION 61
7.1. Overview and strengths of study 61
7.2. Key findings 61
7.2.1. Successful application of a novel longitudinal data modeling technique 61
7.2.2. Clinical interpretation of the trajectory groups and characteristics of the 62
trajectory group members
7.3. Limitations 64
7.3.1. Limitations of the Mplus software 64
7.3.2. Limitations of the study design 65
7.3.3. Limitations of the use of the AJC as outcome measure 67
7.4. Future directions 67
8. REFERENCES 69
9. APPENDICES 73
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List of Tables
Page
Table 1. Interpretation of the 2(∆ Bayesian Information Criterion) in model selection 31 Table 2. Comparisons of average active joint count in patients who continue to 37
the next visit to those whom drop out. Table 3. Cohort demographics 39 Table 4. Univariate description of the active joint count at each visit 40 Table 5. Comparison of the baseline demographics of the subgroup of patients 41
with no active joint disease with those with active joint disease
Table 6. Fit statistics for latent curve growth analysis for the normal distribution 42
Table 7. Fit statistic for latent curve growth analysis for the Poisson distribution 42
Table 8. Fit statistics for latent curve growth analysis for the negative binomial 43
distribution Table 9. Fit statistics for latent curve growth analysis for the zero-inflated negative 43
binomial distribution Table 10. Fit statistics for the latent curve growth analysis and growth mixture models 46
under the zero-inflated negative binomial distribution Table 11. Average posterior probabilities for five-class solution 52
Table 12. Average posterior probabilities for four-class solutions 53
Table 13. Summary of model qualities 59
Table 14.Characteristics of study participants, stratified by trajectory 60
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List of Figures
Page
Figure 1. Growth curve model 11
Figure 2. Latent curve growth analysis 14
Figure 3. Growth mixture model 14
Figure 4. Kaplan-Meier curve of the proportion of completed visits over time 35
Figure 5. Mean active joint count at visit N by follow-up status at visit N+1 37
Figure 6. Four-class latent curve growth analysis 47
Figure 7. Four-class growth mixutre model(variance of the non-inflated 47
intercept estimated) Figure 8. Four-class growth mixture model(variance of the non-inflated intercept 48
and slope estimated) Figure 9. Five-class latent curve growth analysis 48
Figure 10. Five-class growth mixture model (variance of the non-inflated 49
intercept estimated)
Figure11. Five-class growth mixture model (variance of the non-inflated intercept 49
and slope estimated) Figure 12. Five-class growth mixture model (non-inflated intercept and slope 50
estimated for groups 2 and 5) Figure 13. Six-class latent curve growth analysis 50
Figure 14. Five-class cubic latent curve growth analysis 51
Figure 15. Five-class latent growth curve analysis Persistent high class (9.8%) 54
Figure 16. Five-class latent growth curve analysis. Moderate increasing class (10%) 55
Figure 17. Five-class latent growth curve analysis. Persistent moderate class (18.5%) 55
Figure 18. Five-class latent growth curve analysis. Persistent low class (43.6%) 56
Figure 19. Five-class latent growth curve analysis. No joint activity class (18.2%) 56
Figure 20. Five-class latent growth curve analysis. Growth curves for all five classes 57
based on the estimated means from the model.
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List of Appendices
Page
Appendix A: Comparison of the ILAR, JRA and JCA criteria 73
Appendix B: ILAR Classification of JIA: Second Revision, Edmonton, 2001 74
Appendix C: Data abstraction form 76
Appendix D. Flow chart of patients and exclusions 80 Appendix E. Frequency of AJC at each visit. 81
Each unit of timeframe is 6 months of follow-up.
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List of abbreviations - clinical
ACR American college of rheumatology
ACRPedi30 Pediatric American college of rheumatology 30 response
AJC active joint count
CHAQ Child health assessment questionnaire
DMARD disease-modifying antirheumatic drug
ESR erythrocyte sedimentation rate
EULAR European league against rheumatism
HLA human leukocyte antigen
ILAR International league of associations for rheumatology
JIA juvenile idiopathic arthritis
JRA juvenile rheumatoid arthritis
RF Rheumatoid factor
RNA ribonucleic acid
ROM range of motion
List of Abbreviations – statistical
AIC Akaïke’s information criterion
BIC Bayesian information criterion
GCM growth curve modeling
GMM growth mixture modeling
LCGA Latent curve growth analysis
LMR LRT Lo-Mendell-Rubin likelihood ratio test
LRT likelihood ratio test
ssABIC sample size adjusted Bayesian information criterion
ZINB zero-inflated negative binomial
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1. INTRODUCTION AND THESIS OVERVIEW
Juvenile idiopathic arthritis (JIA) is the most common rheumatic disease of childhood. It
is a chronic illness that fluctuates symptomatically over years. In the literature, there is a paucity
of reliable indicators of prognosis and outcome in this disease. This is due, in part, to the
heterogeneity in clinical phenotype, and consequently in the classification system nomenclature
as well as to the lack of a universal definition of remission and outcome. Most importantly, all
of the outcome studies to date have defined an outcome at a fixed point in time. We argue that
this is not the correct approach to a definition of outcome in a chronic, relapsing and remitting
disease. A more appropriate outcome may be the disease course itself. An understanding of the
disease course and its relationship to distal outcomes and genetic and biologic predictors is
crucial in order to correctly define outcome states in JIA.
The aim of this study was to use a novel longitudinal data analysis technique (latent
growth curve modeling) applied to active joint count (surrogate for disease activity) to
characterize the disease course in JIA. We recognize that JIA is a multidimensional disease and
there are other important patient and disease-related factors that are important determinants of
disease activity. If these methods are successful, then other components may be tested.
This thesis is organized as follows: chapter 2 lays out the background relating to
JIA and growth curve modeling. With regard to JIA, challenges with the classification criteria,
difficulties in defining predictors and outcomes and emerging biological evidence for etiologic
heterogeneity will be discussed. Conventional growth curve modeling will be presented in
contrast to latent growth curve modeling. Two types of latent growth curve modeling will be
reviewed: latent class growth analysis and growth mixture modeling. Finally, an approach to
selection of a latent growth curve model based on statistical and clinical sensibility will be
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discussed. Given the framework and background developed in chapter 2, chapters 3 and 4
present the rationale, relevance and study objectives. Chapter 5 will expand in detail on the
methods used and chapter 6 will present the results. Finally, a discussion of the results, study
limitations, implications and future directions is presented in chapter 7.
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2. BACKGROUND
2.1 Juvenile idiopathic Arthritis
2.1.1 Epidemiology and Burden of Illness
Juvenile idiopathic arthritis (JIA) is the most common rheumatic disease of childhood. It
is estimated to affect at least 1 in 1000 Canadians under the age of 16, making JIA one of the
most prevalent chronic diseases among children in our country. A recent Canadian pediatric
surveillance program determined an annual incidence rate of 7/100, 000 [1]. A community-
based prevalence study in Australia demonstrated a prevalence of 400 per 100, 000 children [2].
Point prevalence estimates of 52 per 100,000 in Saskatchewan and 32 per 100,000 in Manitoba
have been calculated however there was marked imprecision in the estimates and a reporting bias
(subspecialty reporting only) which likely has resulted in an underestimation of the true burden
of illness in Canada [3].
Although JIA is rarely fatal, it is a chronic illness that can be associated with serious
physical disability for many affected children due to joint damage. Additional morbidity
associated with JIA can be due to its treatment and effect on growth and development. Children
with inadequately treated or recalcitrant JIA may have chronic pain, mood disturbances, and
difficulty with peer relationships, school performance and attainment of educational and
vocational goals [4-9].
In addition to the significant personal cost, an increase in utilization of health care
services has been demonstrated. In a 2007 Canadian study of 155 consecutive clinic attendees,
the total difference in annualized average direct medical costs for children with JIA versus
controls was $ 1,686 (95%CI $875 to $2,500) [10].
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2.1.2 Classification Criteria in JIA
JIA is not a single disease but rather a diagnosis that applies to arthritis of unknown
origin, persisting for more than 6 weeks and with onset before the age of 16 years[11]. The
most recent proposed classification criteria (International league of associations for
rheumatology (ILAR): second revision, 2001) were developed to delineate relatively
homogeneous, mutually exclusive categories of idiopathic childhood arthritis to aid in the
conduct of research [11]. These criteria addressed the heterogeneity in nomenclature and criteria
between European (juvenile chronic arthritis (JCA)) [12] and North American (juvenile
rheumatoid arthritis (JRA)) classification systems (Appendix A) [13].
This expert, consensus-based classification system is based on clinical characteristics
during the first six months of disease. It is purported to define categories of clinically
homogeneous groups of patients that may demonstrate, to some extent, etiologic and pathogenic
homogeneity and predictability of response to therapies [14-15]. The seven mutually exclusive
categories are systemic arthritis, oligoarthritis, rheumatoid factor (RF) positive polyarthritis, RF
negative polyarthritis, psoriatic arthritis, enthesitis related arthritis and undifferentiated arthritis
(Appendix B)[11]. The ILAR criteria have been recognized as a “work in progress” since their
inception - a framework to which novel biologic predictors of outcome or disease course may be
added.
2.1.3 Challenges with the clinical application of the ILAR criteria
Strictly speaking, the purpose of the ILAR classification criteria in juvenile arthritis is
descriptive [16]. Although this tool was designed to discriminate between individuals, there are
difficulties in applying the criteria inherent in its design. The category of “undifferentiated
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arthritis” encompasses patients that fulfill criteria in no category or in two or more categories
[11]. This subtype represents between 11 and 21% of patients in studies reporting application of
the criteria [17-18]. There is particularly little known about this subtype as it is poorly
characterized in studies to date. This subgroup also poses unique difficulties for treatment trials
and may bias the results towards finding no effect of treatment when in fact there is one. A
heterogeneous group of patients in a treatment trial is undesirable as any potential efficacy may
be negated based on the inclusion of subjects that have a biological or genetic justification for a
response/lack of response.
Although the subtypes are descriptive terms, certain subtypes confer prognostic
information (i.e. RF positive polyarthritis patients are known to have a more severe disease
course) [19-21]. However information regarding expected disease course within subtypes is
lacking. While some of the subsets clearly identify distinct disease entities that are more
adequately characterized (RF positive polyarthritis, systemic arthritis), others still show marked
clinical variation within a subtype. Therefore, for individual patients, there is a limited ability to
advise or predict the expected disease course[22].
In summary, classification of patients at baseline or after the first six months is not
sufficient to determine outcome and does not adequately inform regarding the course.
2.1.4 Difficulties in defining disease course and outcomes in JIA
Traditional outcomes in JIA include joint damage on radiographs, persistent disease
activity, loss of function and effects on quality of life [9, 19, 23-27]. Outcomes have generally
been evaluated at a fixed-time point and have been modeled as a continuous or dichotomous
variable or are analyzed as a time to a defined event (i.e. remission, disability).
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A few selected outcome studies highlight the historical challenges in reporting outcomes
in JIA. Data from a Canadian multicentre cohort study demonstrated that 10 years following a
diagnosis of JRA, [13] the probability of remission (defined as two years off medication) was
37% in systemic, 47% in oligoarthritis, 23% in polyarthritis RF negative and 6% in polyarthritis
RF [24]. This is in contrast to a multicentre cohort study in Italy and the USA in which after a
median of 7.7 years following the diagnosis of JIA (only 5 categories considered) [11], 40% of
patients were in remission (defined as six months off medication). However at two years off
medications (the more rigid criteria defined by Oen et al.[24]) only 28% of patients were in
remission. When subtypes were considered, remission was achieved in 60% with systemic
arthritis, in 53% with persistent oligoarthritis, 33% with extended oligoarthritis, 31% with RF
negative polyarthritis and 0% with RF positive polyarthritis. A German population-based cohort
study reporting on long-term outcomes of 260 patients with JIA, used the American college of
rheumatology (ACR) definition of remission [28] and a cutoff of “no medications” for 2 months.
This study found 47% with systemic arthritis, 73% with persistent oligoarthritis, 12% with
extended oligoarthritis, 30% with RF negative polyarthritis and 0% with RF polyarthritis were in
remission.
These three papers highlight a few key issues. First, there has been wide variability in the
reported definitions of remission, classification of patients and outcome intervals. This has
likely been a barrier to the interpretability of this information for clinicians and dissemination to
patients. Second, even within subtypes, variations in disease duration before remission,
disability and radiographic joint damage are evident [29-31] which provides further evidence for
heterogeneity. The historical classification of childhood arthritis does not effectively capture the
clinical and outcome variability present in this population. Third, the current statistical methods
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for reporting outcome studies measure an outcome at only one time for each patient. Patients'
courses vary so a single measurement may not represent the long-term experience with the
disease given our knowledge of the chronic and fluctuating nature of this illness.
In summary, the difficulties related to defining disease course and outcome are related in
part to heterogeneity in definitions but also to incorrectly identifying an outcome at one point in
time instead of capturing the effect of the disease over time.
2.1.5 Predictors of Outcomes in JIA
The identification of reproducible indicators of outcome (remission, disability, persistent
disease activity and radiologic damage) has been a challenge [19, 23, 27]. Studies to date have
shown inconsistent results likely related to differences in definitions (in subtypes of arthritis and
remission), small sample sizes, varied length of follow-up and lack of consideration for the effect
of medication on outcome. The inconsistency is also in part due to the use of the consensus-
based ILAR classification system which has defined the subtypes based on subjective
combinations of clinical and laboratory features. We proposes that future studies of predictors
should focus on predicting the disease course.
The traditional patient characteristics evaluated in prognostic studies include: age at
onset, sex, race, onset type of JIA, total number and distribution of affected joints, persistence of
systemic features at ≥ 3 months, and uveitis [19-20, 32-34]. More recently, evidence from two
longitudinal cohort studies demonstrated that a delay in diagnosis of JIA may be an important
predictor of outcome [35-36]. Further investigation of the association of a delay in diagnosis to
prognosis is warranted and may prove to be an important predictor of response to treatment and
outcome. Traditional laboratory investigation evaluated in prognostic studies have included
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rheumatoid factor, anti-nuclear antibodies, HLA-B27, ESR, and in some cases white blood cell
count, hemoglobin and platelets [19-20, 32, 37-40]. Until now, classification systems have not
integrated these traditional prognostic factors. In addition, in the last 10 years, there is growing
evidence for both HLA and non-HLA genetic associations with subtypes of arthritis and outcome
in JIA, which have also not been addressed with the current classification systems [17, 27, 41-
42].
2.1.6 Emerging biological evidence for etiologic heterogeneity
In addition to the evidence for genetic association with subtypes of arthritis, there is a
growing body of literature to support biological variability among the subtypes of JIA. In 2009,
a stimulating editorial entitled “Can molecular profiling predict the future in JIA” highlighted the
significant findings in this field [43]. In the last two years, distinct protein expression profiles in
synovial fluid mononuclear cells have been found to be predictive of a more severe course in
oligoarthritis [44]; also specific RNA gene expression patterns in peripheral blood
polymorphonuclear cells can distinguish RF positive and RF negative polyarthritis [45]. Thus,
biologic evidence supports the notion that JIA is a heterogeneous disease and phenotypic
variability may ultimately be explained, at least in part, on the basis of molecular differences.
2.1.7 Summary
In summary, childhood arthritis is a chronic illness with relapses and remissions that
often continues into adulthood and is a source of morbidity for both the child and family. JIA is
an umbrella term that encompasses several etiologically distinct diseases. There are deficiencies
in our approach to the classification of childhood arthritis that has led to difficulties in defining
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and reporting of outcomes and predictors of disease course. These difficulties result at least in
part from the multiple revisions and lack of unified criteria as well as the fact that the criteria
remain consensus-based and incorporate only clinical and basic laboratory factors. There is also
plausible biologic evidence for phenotypic heterogeneity that will need to be incorporated into
future classification schemes in an attempt to identify and characterize truly homogeneous
groups of patients.
There is clearly a need for a new multifaceted approach to classification of childhood
arthritis that incorporates genetic, immunologic and biochemical information. The existing
literature about disease course and outcome has several limitations as outlined above. The use of
statistical techniques that quantify outcome based on a patients’ status at a fixed single point in
time contribute at least in part to these limitations. These are not the optimal methods to evaluate
disease outcome when the disease is chronic and fluctuating as in JIA. In order to adequately
characterize the disease course, novel statistical methods to examine longitudinal disease
activity, which properly characterizes the course of this illness, need to be explored.
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2.2 Growth Curve modeling
In this study we aim to characterize disease course so we may work towards the
development of predictive models that identify disease course as the outcome. This is in contrast
to traditional prognostic models that generally predict an outcome at a fixed time point. To
characterize the disease course, we propose the use of longitudinal growth curve modeling
techniques. There are three longitudinal methods that will be described in sections 2.2.1, 2.2.2
and 2.2.3 these are conventional growth curve modeling (GCM), latent curve growth analysis
(LCGA) and growth mixture modeling (GMM).
2.2.1 Conventional growth modeling
In conventional GCM, it is assumed that a sample is drawn from a single
population characterized by an underlying set of growth parameters (i.e. intercept (mean starting
value), slope and quadratic terms). These parameters describe the average course of an outcome
over age or time (i.e. a growth curve). Individual variation in developmental trajectories is
captured by random effects [46]. For example, if all subjects have a growth curve that has the
same shape, but some have higher than average levels and some have lower than average levels,
we would add a random intercept to the model. If the shape was also different across subjects,
we might consider allowing a random slope. A theoretical conventional growth curve model is
presented in Figure 1.
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Figure 1. Growth Curve model. The bold line represents the population mean of the outcome
over time. The faint lines represent the individual variation.
We are however, often interested in and deal with samples from multiple populations (i.e.
males and females with a disease, patients without the disease). One can simultaneously model
the course of disease in multiple observed populations using a different growth model for each
population or by including covariates that reflect how the growth curves differ across
populations. However these approaches require a priori knowledge of individuals’ group
membership.
When group membership is not known or if one’s hypothesis is to determine whether
there are distinct sub-populations within a single population, conventional GCM is not sufficient.
GCM in a latent variable framework allows for post-hoc identification and description of
longitudinal change within unobserved sub-populations[47].
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2.2.2 Latent variable growth curve modeling
The objective of latent variable growth curve modeling is to measure and explain
differences across population members in their developmental trajectories. While the growth
curve modeling approach attributes all heterogeneity between subjects to random variation
around a common mean growth curve, the defining characteristic of the latent variable growth
curve model is that it identifies groups of subjects with different mean growth curves. In the last
10 years there has been a marked increase in the use of the latent growth modeling techniques.
A recent review found that in the PSYC INFO database, between the years of 2000 and 2008, the
number of publications per year increased from 8 to 80 [48]. Historically this methodology has
been used in the fields of social and psychological sciences particularly to examine how social
behaviors unfold over time and to study personality development. There is growing interest in
allopathic medicine as this methodology may be extended to capture heterogeneity in treatment
responses and to further our understanding of the growth and development of medical illnesses
[49-50].
Latent variable growth curve modeling is an elaboration based on a class of statistical
models call finite mixture models [51]. One application of finite mixture models is the post-hoc
identification of subpopulations based on measured characteristics. Classification of individuals
into distinct groups based on individual response patterns so that individuals within a group are
more similar than individuals between groups is the desired output.
In contrast to conventional GCM, the latent growth curve modeling approach allows for
differences in the average values of growth parameters across unobserved subpopulations. This
is accomplished using latent trajectory classes (i.e. categorical latent variables), which allow for
13
different groups of individual growth trajectories to vary around different means [52]. The
results are growth models for each latent class, each with its unique estimates of variances.
Another way of relating conventional GCM to latent variable modeling is in terms of
person and variable-centered approach to data analysis [53]. The focus of GCM is on
relationships among variables (identify significant predictors of outcome and describe how
dependent and independent variables are related). Latent variable modeling uses a person-
centered approach focusing on the relationship between individuals. This type of modeling can
be used for data at one point in time (cross-sectional). An example of a finite mixture model as
applied to cross-sectional data is a study performed by Thomas et al. whereby a novel
classification of JIA was proposed using a latent class analysis (see section 3). This same
approach can be applied to longitudinal data. When finite mixture models are applied to
longitudinal data, the purpose is to identify groups with similar growth curves. Within this class
of models, there are two distinct approaches. The first assumes that everyone in the same group
has exactly the same true growth curve. This is called the LCGA. The second approach allows
that subjects within a group have true growth curves that vary around the mean for the group.
This is called GMM. A theoretical LCGA and GMM are presented in Figures 2 and 3. The
sections below provide more details on LCGA and GMM.
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Figure 2. Latent curve growth analysis. Each line represents the growth curve for that class. No
individual variability is accounted for with this type of model.
Figure 3. Growth mixture model. Each bold line represents the growth curve for that class.
Individual variability is represented by the faint lines around each bold line.
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2.2.3 Latent class growth analysis versus growth mixture modeling
LCGA is a special type of GMM, whereby the variance estimates for the growth factors
within each class are assumed to be fixed at zero. By this assumption, all individual growth
trajectories within a class are the same. The LCGA framework has been developed by Nagin
and colleagues [54]. A SAS procedure (PROC TRAJ) has been developed to implement this
method [55-56]. GMM on the other hand uses both continuous latent variables (random effects)
and categorical latent variables (trajectory classes) in the model. Nagin noted that a LCGA may
find a k + m-class solution where a GMM may find a k-class solution given that in GMM, the
intra-class variability is captured by random effects which may produce a more parsimonious
model [57].
In terms of deciding which technique to choose, there is no clear choice. The decision
should be based on the underlying hypothesis regarding the heterogeneity of the study
population. Computationally, the LCGA models sometimes converge more easily and produce a
simpler model than the GMM. Muthen has suggested that both approaches should be used, with
LCGA as a first step to qualitatively asses the number of classes and location of curves. If large
variability is observed for the plots of the individual values around the estimated, class specific
mean curves, it is then proposed to relax the variances in a GMM and reevaluate model fit
indices [58]. Estimating additional growth factors (in addition to the intercept and slope), for
example a quadratic term, will add computational burden, so it is not unusual to see the variance
of the quadratic term fixed to zero to aid in convergence during GMM [52]. Mplus is the
software commonly used for GMM and since LCGA models are a special case of GMM, the
specification of LCGA models is easy with Mplus software[59].
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2.2.4 Latent growth variable modeling – model selection
The aim of using latent growth curve modeling in our study was to statistically derive
homogeneous subsets (classes) based on the pattern of longitudinal disease activity. Determining
the number of homogenous classes is important to further our understanding of disease course in
JIA. An ideal number of homogenous classes is that which adequately describes clinically
meaningful groups that differ (based on disease progression, features at presentation or response
to therapy). Identification of a large number of small subsets of patients would not be helpful to
clinicians in guiding practice nor would too few.
In the latent growth curve modeling field, statistical determination of the number of latent
classes is an active area of research and debate. There is not one commonly accepted statistical
indicator for deciding on the number of classes in a study population. The commonly used log-
likelihood ratio test cannot be used to test nested latent class models as this test assumes a chi-
square distribution which is not the case in mixture models [60].
Currently, Mplus provides three criteria for assessment of the optimal number of classes
in a GMM: (1) the model with the smallest Bayesian information criterion (BIC) defined as -2
ln(L) + p ln(n) (where ln(L) is the log-likelihood, p is the number of parameters and n is the
sample size) [61]; (2) the model with the smallest sample size adjusted BIC (BIC where
n=(n+2)/24)[62-63]; (3) a significant (p<0.05) Lo-Mendell-Rubin (LMR) adjusted LRT statistic
comparing a model with k classes to a model with k-1 classes[64] . A lower BIC and ssABIC
and a significant LMR-LRT indicate the k-class model is preferred over the k-1 class model.
More recently, further simulations have demonstrated that while the BIC performed best among
the information criteria-based indices, the bootstrapped LRT proved to be a better indicator in
determining the number of classes across all of the models considered for the likelihood-based
17
tests [52]. Nyland et al. have suggested the model with a low BIC value and a significant
LMR p-value comparing the k and k-1 class model should initially guide analysis. As the
computation burden greatly increases in requesting the BLRT, it has been suggested that the
initial steps of model exploration be done with the BIC and LMR p-values to arrive at possible
solutions and then once a few plausible models have been identified, that these models be
reanalyzed with the BLRT[60].
Other considerations include successful model convergence, a high entropy value (near
1.0), no less than 1% of the total count of the sample in a class, and high posterior probabilities
of classification into the latent classes[61]. The entropy value quantifies the uncertainty of
classification of subjects into latent classes. Entropy values range from 0 to 1, with 0
corresponding to randomness and 1 to a perfect classification[62]. In terms of posterior
probabilities, a value of <0.7 is considered a poor fit and >0.9 considered an excellent
classification [57]. As it is not possible to know with certainty to which group a subject belongs,
the posterior probability is used. This probability is computed based on post-model estimations
using parameters estimated from the model and is the probability of class membership in the
groups that make up the model (see section 5.7.3.3).
Importantly, criteria for statistical selection of the number of latent classes and clinical
relevance and utility of the classes should be considered when deciding on the number of latent
trajectory classes.
2.2.5 Latent growth variable modeling – clinical sensibility and danger of overextraction
As noted above, the number of classes should be determined by a combination of factors
in addition to fit indices; including one’s research question, parsimony, theoretical justification
18
and interpretability [63-64]. Bauer and Curran demonstrated in a simulation analysis that a
seemingly modest specification error (i.e. incorrect distribution) may results in the overextraction
of groups in GMM analysis [64]. It is important to be conscious that the trajectory groups are
not literally distinct entities but approximations. Once trajectory groups are identified, it is
important to determine if they differ based on preexisting characteristics, subsequent outcomes,
their response to treatment, or their relationship to trajectories for other outcomes or behaviors
[48]. Essentially, if they differ in none of these characteristics, then the grouping does not serve
a useful purpose.
In particular, when selecting the number of classes to describe patterns of disease course;
the trajectories must be evaluated to ensure they fit with the physicians’ clinical judgment about
disease course. Clinical utility is also important in terms of the classes’ ability to predict distal
outcomes or their relationship to important genetic or biological markers. A model may be
selected based on additional knowledge of its relationship to etiology or outcome even if it does
not meet all statistical criteria (i.e. small class size).
2.2.6 Summary
When the hypothesis pertains to identifying heterogeneity within a study population,
traditional GCM is inadequate. Conventional GCM assume that the growth trajectories of all
individuals can be described using a single set of growth parameters. Novel longitudinal
modeling techniques (GMM and LCGA) allow for differences in growth parameters across
unobserved subpopulations using latent trajectory classes. LCGA estimates a mean growth curve
for each class, but no individual variation around the mean growth curve is allowed. GMM, on
the other hand, combines the features of a random effects model and LCGA by estimating both
19
mean growth curves for each class and individual variation around these curves by estimating
growth factor variances for each class. Selection of the best fitting model is a combination of
both clinical judgment and statistical fit indices. These new techniques are relatively in their
infancy however, showing promise for the examination of study populations comprised of
apparently (but undefined) heterogeneous subjects such as in JIA.
20
3. RATIONALE AND RELEVANCE
In order to adequately characterize childhood arthritis, novel statistical techniques are
required. A study performed 10 years ago by Thomas et al. further demonstrated that there are
unanswered questions regarding the classification of patients with childhood arthritis[65]. Using
cross-sectional data, they reported their use of a latent class analysis to explain the statistically
observed relationship of clinical and laboratory variables to underlying subtypes of JIA. A 7
class model was identified. There was some overlap with ILAR criteria but not complete
agreement. Latent class analysis provided a means for objective analysis of the pattern of
symptom profiles. It was determined that the patterns of joint involvement and ANA were
important in determining the latent classes; these two predictors are not present in the current
classification. In addition, 3 HLA associations were differentially expressed amongst the groups
[66]. This study presented a novel approach to objectively classifying patients into
homogeneous groups based on a cross-sectional symptom profile that identified classes and
predictors distinct from ILAR.
As childhood arthritis is a chronic illness with a relapsing and remitting course, we
believe an objective statistical technique applied to longitudinal data is required to further our
understanding of disease progression patterns. No attempt has been made to classify JIA patient
subgroups based on longitudinal disease course using an objective statistical approach. Growth
mixture modeling is an ideal methodology to characterize the subpopulations in this
heterogeneous disease.
Improved characterization of the disease course and identification of homogenous groups
of patients based on progression patterns is needed. Identification of homogeneous groups of
patients based on disease course will further our understanding of the observed differences in
21
etiology and response to treatment between groups. In addition, a well-characterized disease
course provides a platform upon which novel genetic and biologic information can be evaluated.
Identification of predictive factors for a more severe disease course would allow tailoring of
therapy to those patients at risk of adverse outcomes to prevent joint damage and mitigate
exposure of potentially toxic therapies to those with a mild course. Knowledge of disease course
(and its predictors) will also allow clinicians to better counsel patients regarding expected course
and outcome. Our exploratory analyses will be an important first step in the development of an
outcome-based novel classification system for patients with childhood arthritis.
22
4. OBJECTIVES AND RESEARCH HYPOTHESIS
4.1 Primary objective
The primary objective of this study was to identify statistically and clinically distinct
trajectories of disease activity in children with juvenile arthritis.
4.2 Secondary objectives
The secondary objective of this study was to identify baseline clinical and laboratory
characteristics associated with the identified trajectories of disease activity.
4.3 Research hypothesis
Through the use of a novel statistical approach to longitudinal data modeling, we will
identify distinct patterns of longitudinal disease activity (using active joint count as a surrogate)
in juvenile arthritis in additional to clinical and laboratory characteristics associated with these
trajectories.
23
5. METHODS
5.1 Study design and overview
This was a retrospective cohort study of children with JIA. Data were obtained from 2
Canadian pediatric rheumatology centres (Royal University Hospital in Saskatoon and Health
Sciences Centre, Arthritis Centre, Winnipeg). Clinical data were evaluated to identify distinct
longitudinal active joint count trajectories in this population.
5.2 Research Ethics Approval
Research Ethics Board approval for this study was obtained from The Hospital for Sick
Children, Royal University Hospital, Saskatoon, Saskatchewan, Health Sciences Centre, Arthritis
Centre, Winnipeg Manitoba, and the University of Toronto, Toronto.
5.3 Study Population
5.3.1 Inclusion and Exclusion Criteria
Eligible patients for the study had a confirmed diagnosis (by a pediatric rheumatologist)
of JRA or JIA. Patients were excluded from the study for any of the following reasons: fewer
than three visits with the rheumatologist, no first visit documented in the medical record,
incorrect original diagnosis or the diagnosis of JRA/JIA was made more than 90 days before the
first visit with the rheumatologist. A cutoff of 90 days was chosen to limit the time of potential
medication exposure prior to the first visit with the rheumatologist (study entry).
24
5.3.2 Data collection
A data extraction sheet was developed, version September 17, 2008. (Appendix C). An
ACCESS database was created using the data extraction sheet as a template. All data were
entered directly into the database. Study visit data were collected every six months (± 2 months)
from the time of the first visit until the last visit with the rheumatologist or the end of the data
collection (April 16, 2009 for Saskatoon and July 31, 2009 for Winnipeg).
5.4 Data management
The dataset was examined for extreme observations. In the event of inconsistencies or
outliers, the records were sent back to the site of origin for verification. In addition, the
following variables were created/verified from the dataset:
1) Age at diagnosis (date of diagnosis – date of birth): When age was >16 or <1 at onset of
symptoms, records were verified.
2) Diagnostic delay (date of diagnosis – date of onset of symptoms: For values of >1096
days (3years) or <0 (indicating a data error) from onset of symptoms to diagnosis, records
were verified.
3) Family history of HLA-B27 associated diseases: If the first-degree family history was
positive for one of: ankylosing spondylitis, acute anterior uveitis, reactive arthritis
(Reiter’s syndrome) or inflammatory bowel disease, this variable was coded as “yes.”
4) Diagnosis by ILAR criteria: The ILAR criteria were assigned by the site for the
Saskatoon subjects. The Winnipeg subjects were classified using the ACR JRA criteria.
The diagnoses were reassigned to ILAR by the author using the clinical data in the
25
database. Where there was not sufficient information or a discrepancy was noted, the
records were sent to the site for verification.
5) Active joint counts: In instances where the AJC was 0 at the first visit, the records were
sent for verification. AJC >50 at any visit were also verified. All joint counts were
generated by Dr. K. Oen (Winnipeg) or Dr. A. Rosenberg (Saskatoon) only.
6) Medications: 5 categories of medications were retained for analysis. DMARD
(methotrexate, leflunomide, sulfasalazine, cyclosporine), non-steroidal anti-inflammatory
drugs, corticosteroids (oral and intravenous), intra-articular steroids and biologics
(etanercept, infliximab, anakinra, adalimumab).
5.5 Trajectory descriptor variable - “outcome variable”
The active joint count (AJC) is the main outcome variable. An active joint is defined as
either an effused joint or a joint with loss of range of motion (ROM) with stress pain. The AJC
was chosen as the trajectory descriptor variable because it is a well-recognized marker of disease
activity and is related to damage. It is one of the core set variables used to define improvement
used in assessment of outcomes in clinical trials in juvenile arthritis [67]. A recent Italian study,
demonstrated that in later disease (≥ 10 years), the AJC is moderately correlated with both the
number of joints with restricted range of motion (r = 0.63) and the Childhood Health Assessment
Questionnaire (functional ability assessment) (r = 0.54)[68]. Additionally, an Italian study
examining prognostic factors for radiographic progression, radiographic damage and disability in
JIA reported that at the last follow-up (median 4.5 years, range 2-13.5) in 94 patients with JIA,
the yearly radiographic progression was correlated with the number of active joints (r = -0.41,
26
p<0.0001) [69]. Increased risk of radiographic progression has clearly been related to
cumulative joint inflammation in adult rheumatoid arthritis [70-71].
The inter-examiner reliability for determination of an active joint is generally suboptimal;
the reported kappa values vary depending on the joint but the range is from 0.35-0.65 [72-73].
There is no reported kappa value for the total AJC. Despite this limitation, both in clinical
practice and research, the joint count is the main measure of disease activity. In the context of
this retrospective cohort study, the outcome measure was assessed by the same staff
rheumatologist at each time so the intra-examiner reliability is presumed to be excellent.
Although there may have been variability between examiners, it is unlikely that this will result
in significant misclassification of patients into different ILAR subtypes based on the number of
active joints detected. In addition, there has been the same single rheumatologist in Winnipeg
and Saskatoon for the last 30 years, which spans the entire data collection period.
Furthermore, the AJC is a reliably recorded and easily accessible marker of disease
activity present in the medical charts that could be abstracted for a retrospective study. We
recognize that JIA is a multidimensional disease and there are other important patient and
disease-related factors that are important determinants of disease activity. If these methods are
successful, other components may be tested.
5.6 Descriptive variables
Important baseline demographic and clinical variables were extracted from the charts:
age, sex, race according to Statistics Canada definitions, individual components of the ILAR
subtype criteria, ILAR diagnosis, and autoantibody status (ANA, RF, HLA-B27). All
information was coded as yes/no or by number (categorical variables). The individual
27
components comprising the subtypes of the ILAR criteria were examined, as the ILAR subtypes
by themselves are not sufficient to explain the variability in course and outcome witnessed
within subtypes. In addition, the work by Thomas et al. using LCA demonstrated factors other
than the ILAR criteria (ANA, pattern of joint involvement) were important in determining the
classes [65]. Medications were re-coded into 5 categories as noted above. Medications were
coded in the database as “yes” or “no” at the time of the visit. If an intra-articular steroid
injection occurred between visits, this was indicated as “yes” at the visit following the injection.
5.7 Analysis
5.7.1 Statistical Software
Univariate analyses were conducted with SAS version 9.2 for Windows (SAS institute,
Inc., Carey, NC). Latent curve growth analysis and growth mixture models were conducted with
Mplus version 6.0 (Muthen and Muthen, 1998-2010) [59, 65]. Statistical significance was
defined as a p-value < 0.05.
5.7.2 Descriptive Statistics
To describe the study population characteristics mean (standard deviation) for continuous
variables and proportions for categorical variable were used. When the distribution of the
variable was skewed, median values (interquartile range) were used. A Kaplan-Meier plot was
used to summarize the number of completed visits per patient.
28
5.7.3 Model building
5.7.3.1 Estimation procedure
In Mplus, parameters are estimated by maximum-likelihood estimation using the
expectation maximization (EM) algorithm [74]. EM is an iterative method that alternates
between performing an expectation (E) step, which computes the expectation of the log-
likelihood, evaluated using the current estimate for the latent variables, and maximization (M)
step, which finds parameters maximizing the expected log-likelihood found on the E step. These
parameter estimates are then used to determine the distribution of the latent variables in the next
E step. In other words, the EM algorithm proceeds in an iterative fashion with parameter
estimates from the current step being compared to those from the previous step until the
difference between the estimates becomes smaller than a specified criterion, suggesting that the
program has converged on a maximum of the likelihood function. For multimodal log-
likelihoods (log-likelihoods with more than one mode) it is possible that the EM algorithm will
converge on a local maximum of the observed data likelihood function and that the global
maximum lies elsewhere. Thus, to ensure that the algorithm has converged on a global
maximum, it is suggested that mixture models be fitted using many random starts. Successful
convergence of the EM algorithm is indicated when the same maximum likelihood value is
reached for different sets of starting values [78]. For our analysis, a set of 100 random starting
values in the initial stage and 25 optimizations in the final stage was used. This resulted (for the
majority of models) in replication of the optimal maximum-likelihood value and normal
termination of the models.
29
In determining the number of classes, three model characteristics were evaluated. The
first was the statistic fit indices, the second the classification quality and third the clinical
usefulness [53].
5.7.3.2 Outcome distribution and fit indices
In order to find the model that was statistically robust, a series of quadratic models were
fit sequentially. LCGA was performed as the first step to determine major types of trajectories.
To determine the appropriate distribution of the outcome variable to be used for GMM,
both continuous and count distributions were explored. The normal distribution is defined by a
mean and variance. The Poisson distribution is a count distribution defined by a single
parameter which is both the mean and variance, that is mean = variance. The negative binomial
distribution is an overdispersed Poisson distribution. Overdispersion implies that there is more
variability around the model’s fitted values than is consistent with a Poisson distribution. The
negative binomial regression addresses the issue of overdispersion by including a dispersion
parameter in the estimation procedures [75]. The negative binomial model can be thought of as a
Poisson model with an additional parameter that allows the variance to exceed the mean.
Overdispersion and an excess of zeros are frequently seen with real-life count data. Zero-
inflated count models provide a parsimonious and powerful way to model this type of situation.
Such models assume that the data are a mixture of two separate data generation processes: one
generates only zeros and the other is either a Poisson or a negative binomial data-generating
process. The mean in a zero-inflated count model depends on 2 factors: the probability that the
mean is zero and the mean when it is not zero [75]. The zero-inflated portion provides a way of
modeling the excess zeros and the negative binomial addresses the overdispersion that would be
30
seen if using a Poisson model alone. Both a zero-inflated Poisson and zero-inflated negative
binomial distribution were explored.
The BIC was used to compare the models to determine the number of classes and the
distribution that best described the data. As outlined in section 1.2.4, of the information criterion
based fit indices, the BIC performed the best. The penalty for the BIC is the logarithm of the
number of subjects multiplied by the number of parameters estimated in the model. The idea of
the penalty is to compensate for the increase in the number of parameters in the model (i.e.
model complexity). A good model, according to BIC, has a high likelihood value without using
many parameters (resulting in a low BIC). The distribution with the optimal fit indices and
which was consistent with the univariate distribution of the outcome variable was selected to
continue with the analysis. All models were run with intercept, linear and quadratic terms to
allow for more flexibility in estimating the shape of the trajectories. In instances where the
linear model is sufficient to define the trajectory, the quadratic term would be estimated to be
near 0 (by Mplus).
In order to determine the number of latent classes, models were run sequentially starting
with two-classes. The k+1 model was compared with k model using the LMR LRT in addition
to examining the BIC values. A 2(∆BIC) >2 was considered evidence that the k+1 model was
better than the k model [76]. The interpretation of the 2(∆BIC) as the degree of evidence
favoring the k+1 to the k model is presented in Table 1 [76]. A p-value for the LMR LRT <0.05
was evidence that the k+1 model was a superior fit than the simpler model. Once the k and k+1
models were identified, the BLRT was used to compare the fit.
31
Table 1. Interpretation of the 2(∆ Bayesian Information Criterion) in model selection
2(∆BIC*) Evidence against H0 (k model)
0 to 2 Not worth mentioning
2 to 6 Positive
6-10 Strong
>10 Very strong
*BIC = Bayesian information criterion
After determining the best fitting model using LCGA, GMM was explored. It has been
suggested that the GMM method may allow identification of a more parsimonious model with
one fewer class than a LCGA model with the same order of polynomial[57]. Theoretically,
given our knowledge of the biologic heterogeneity of JIA, allowing for intra-class variability
using a GMM would seem appropriate. The GMM was performed first allowing variability only
in the intercept growth parameter and then with variability in both the intercept and slope
parameters for the non-inflated portion (mean function amongst those who do not have 0
counts) of the model only. The model fit was again assessed using the BIC to compare to the
LCGA analysis for the same number of classes.
5.7.3.3 Classification quality
The classification quality can be determined by examining the posterior probabilities.
Using LCGM, it is not possible to determine definitively an individual’s group membership.
However, it is possible to calculate the probability of his or her membership in the groups that
make up the model. This probability is the called the posterior group membership probability
32
because it is computed based on post-model estimations using parameters estimated from the
model. This probability is computed as follows [77]:
Where (a) p(j|Yi) is the estimated probability that subject i is in latent class j, given the observed
trajectory Yi ; p(Yi|j) is the estimated probability of observing individual i’s actual behavioral
trajectory, Yi, given membership in class j ; (c) πj is the estimated proportion of the population in
class j; and (d) J is the number of latent classes. Each subject has some probability of being in
groups 1 to J and individuals are assigned to groups on the basis of the maximum posterior
probability assignment rule. That is, they are assigned to the group for which their posterior
probability membership is the largest. These calculations are done internally by Mplus.
Entropy was also used as a classification index. This index is used to quantify the
uncertainty of classification of subjects into latent classes. Entropy values range from 0 to 1, with
0 corresponding to randomness and 1 to perfect classification [62].
5.7.3.4 Clinical usefulness
In addition to the formalized criteria above (5.7.3.2 and 5.7.3.3), the aim was to achieve a
model that was sensible given our knowledge of the clinical course of juvenile arthritis. The
usefulness of a model in clinical practice was determined by examining the shape of the
trajectories for similarity and the number of individuals in each class. The shapes and clinical
usefulness of the curves were evaluated by experienced pediatric rheumatologists on the
investigative committee (Drs. Brian Feldman and Rae Yeung). A final model was selected based
33
on the statistical and clinical criteria to use in further analysis. The final model needs to be
evaluated for its relationship to known predictors and distal outcomes to assess its clinical
usefulness. The final model in this study does not represent the “final” answer but rather a
possible solution.
5.7.4 Model Verification
To ensure that the best solution for the final selected model corresponded to the global
optimum rather than a local maximum likelihood solution, the number of random starting values
sets was increased to 300 and 50 final optimizations. Replication of the maximum likelihood
value was considered evidence that the global optimum was obtained.
5.7.5 Association of baseline characteristics with class membership
Class membership for each participant was determined based on the most likely latent
class for each individual given the fitted model (output from Mplus).
To examine the characteristics of the subjects within each trajectory, a chi-square test of
proportions was used for categorical variables and an analysis of variance was used for
continuous variables. A variable with >20% missing values was not included in this analysis.
34
6. RESULTS
6.1 Study population
The initial cohort and flow chart of exclusions is included in Appendix D (Figure I). There
were 1074 subjects eligible for the study. Three hundred and forty-four subjects were excluded
as they had <3 visits with the rheumatologist (55 from Winnipeg, 289 from Saskatoon). No
demographic information was available for these subjects for comparison to study subjects. Data
was collected on 730 subjects; two were excluded as no first visit was documented, 15 for age at
onset of symptoms >16 years, 54 for diagnosis made >90 days before the first visit with the
rheumatologist and one patient was excluded for an incorrect diagnosis. Data from the
remaining 659 subjects (361 from Saskatoon, 298 from Winnipeg) were used for analysis.
6.1.1 Follow-up times
The study participants had variable lengths of follow-up. The maximum length of follow-
up was 23.5 years for 1 patient and the minimum was 18 months (as specified by the exclusion
criteria). There was a steady decline in the total number of subjects still being seen at each
subsequent visit. An arbitrary cut off of 10 years (visit number 20) was chosen as the time period
over which outcomes would be used in the growth curve modeling. At the 10-year time point,
there were 81 visits recorded. The Kaplan-Meier curve in Figure 4 depicts the distribution of the
number of completed visits (i.e. last visit recorded per patient). The median number of
completed visits was nine (4.5 years).
35
Figure 4. Kaplan-Meier plot of the number of completed visits.
As the follow-up time varied among subjects, we sought to examine if longer follow-up
times were related to a lower (or higher) AJC. To examine this, the AJC of study completers
was compared to those patients who dropped out (non-completers). For each patient an average
joint count over time was computed. A completer was defined as a participant who was >18 at
the last visit or a visit occurred <240 days (6 months + 2 months) before the close of the study
(end of data abstraction). A non-completer was defined as a participant who was <18 at the last
visit and did not have a visit within 240 days of the end of the study. There was a total of 38.9%
(256/659) of participants who completed the study. A small but statistically significant
(1=6months)
36
(p=0.002) difference in the mean joint count between the completers (2.5± 4.1) and non-
completers (1.6 ± 2.8) was found.
Secondly, we examined if those who dropped out at a given visit tended to have a lower
(or higher) AJC that those who did not drop out. The purpose was to see if being an early or late
non-completer had any effect on the AJC. We compared the AJC of subjects who dropped out by
the next visit with the AJC of subjects who continued through to the next visit. The difference in
AJC of those who dropped out versus those who continued is shown in Table 2. Overall, there
was a statistically significant difference in the mean AJC at about one-third of the visits; with
those who dropped out being lower than those who continued to the next visit. There is marked
random variation (large standard deviation) and it is unlikely the differences are clinically
significant. Figure 5 depicts the mean AJC at each visit for dropped out or continued to the next
visit. From this figure, there is no obvious trend apart from the later visits (16-20) where it
appears the mean AJC is higher among those who continued, however the number of subjects is
small.
37
Table 2. Comparisons of average active joint count in patients who continue to the next visit to
those whom drop out.
Visit Mean AJC (SD) Mean AJC (SD) p-value
Continue to next visit Drop out
1 2.7 (6.0) No drop outs
2 2.4 (5.8) No drop outs
3 3.6 (7.6) 1.5(2.6) 0.0001
4 1.9 (5.0) 1.7(4.6) 0.7984
5 2.2 (5.8) 2.1(6.0) 0.9227
6 2.0 (5.9) 1.5 (3.4) 0.3478
7 1.9 (5.2) 1.3 (3.6) 0.3205
8 1.9 (4.7) 1.0 (2.4) 0.0496
9 1.9 (5.1) 1.9 (3.7) 0.9274
10 2.3 (5.6) 2.3 (4.8) 0.9891
11 2.7 (7.1) 2.3 (5.6) 0.6888
12 3.3 (8.1) 1.9 (5.5) 0.2546
13 2.2 (5.3) 0.6 (1.9) 0.0021
14 1.7 (4.0) 2.3 (7.4) 0.7264
15 2.0 (5.3) 2.2 (4.7) 0.8784
16 2.1 (4.7) 1.2 (2.6) 0.2148
17 2.4 (5.7) 1.6 (4.3) 0.496
18 2.4 (7.1) 0.6 (1.5) 0.042
19 3.4 (9.9) 0.2 (0.4) 0.0102
SD= standard deviation; AJC = active joint
Figure 5. Mean active joint count at visit N by follow-up status at visit N+1
(1=6 months)
38
6.1.2 Missing Visits
Of the total 8324 visits entered into the database, there were 1318 missing visits (15.8%).
The missing visits were visits that were expected (6 ± 2 months) during the period of follow-up
but did not occur. These values were assumed to be missing at random (MAR), and handled that
way by the Mplus software.
6.1.3 Cohort baseline demographics
The baseline demographics of the 659 study participants are presented in Table 3. Of the
35.7% of participants with the oligoarthritis subtype, 12.8% (30/235) had an extended course.
Medications started before or at the first visit included non-steroidal anti-inflammatory drugs
(NSAIDs) for 394 (60%), DMARD for 20 (3%), oral corticosteroids for 18 (3%) and
intraarticular steroid injections for 70 (11%). No patient was taking biologic therapy at the first
visit. During the follow-up period, 615 (93%) of patients were treated with NSAIDs, 223 (34%)
with intraarticular injections, 94 (14%) with oral corticosteroids, 208 (32%) with DMARD and
34 (5%) with biologic therapies.
39
Table 3. Cohort Demographics*
Characteristic Result
Female sex (n)(%) 402 (61)
ANA positive (n)(%) 286 (45.5)
Age at diagnosis (mean)(SD) 8.9 (4.9)
Diagnostic delay (median)(25th
%-75th
%) 4.9 months (2.2-12.3)
ILAR diagnosis (n)(%)
Systemic arthritis 45 (6.8)
Oligoarthritis 235 (35.7)
Polyarthritis – RF negative 87 (13.2)
Polyarthritis – RF positive 23 (3.5)
Psoriatic arthritis 54 (8.2)
Enthesitis-related arthritis 134 (20.3)
Undifferentiated 81 (12.3)
Individual ILAR criteria (n)(%)
Systemic Fever 44 (6.7)
Psoriasis 46 (8.9)
Dactylitis 33 (7.9)
RF positive 34 (5.6)
Lumbosacral back pain 147 (28.0)
Enthesitis 148 (31.8)
HLA-B27 positive 93 (16.6)
1st degree family history of psoriasis 99 (17.6)
Family history of HLA-B27 associated disease 91 (16.3)
Ethnicity (n)(%)
Caucasian
America∆
Asia
Africa & Caribbean Islands
Other
Missing data
338 (51.3)
53 (8.0)
5 (0.8)
2 (0.3)
3 (0.5)
258 (39.2)
ESR at first visit (median)(25th
%-75th
%) 21.00 (9.0-40.5)
CRP at first visit (median)(25th
%-75th
%) 4.00 (0-12.0)
N= number of subjects; SD=standard deviation; ∆
=Aboriginal Inuit, Aboriginal North American
Indian, Latin American; * The number of subjects with complete data varies by variable
40
6.2 Description of the active joint count
The univariate descriptions of the trajectory descriptor variable (AJC) are presented in
Table 4. This variable did not follow a normal distribution. For all visits, the skewness and
kurtosis values were greater than 2 and tests for non-normality were highly significant (p<0.001).
The distribution was right-skewed and the preponderance of zero values is seen in the bar charts
of the AJC at each visit (Appendix E).
Table 4. Univariate description of the active joint count at each visit
Visit AJC (mean)(SD)
AJC (median)(25
th%-75
th%)
Visit AJC (mean)(SD)
AJC (median)(25
th%-75
th%)
1 2.7 (6.0) 1 (0-2) 11 2.6 (6.9) 0 (0-2)
2 2.4 (5.8) 1 (0-2) 12 3.1 (7.8) 0 (0-2.5)
3 3.4 (7.4) 1 (0-3) 13 1.9 (4.9) 0 (0-1)
4 1.9 (5.0) 0 (0-1) 14 1.8 (4.6) 0 (0-1)
5 2.2 (5.8) 0 (0-1) 15 2.1 (5.2) 0 (0-2)
6 1.9 (5.6) 0 (0-1) 16 2.0 (4.5) 0 (0-2)
7 1.8 (5.0) 0 (0-1) 17 2.3 (5.5) 0 (0-2)
8 1.8 (4.5) 0 (0-1) 18 2.0 (6.4) 0 (0-1)
9 1.9 (4.9) 0 (0-1) 19 2.8 (9.0) 0 (0-2)
10 2.3 (5.5) 0 (0-1) 20 1.9 (4.7) 0 (0-2)
SD = standard deviation; AJC = AJC
6.3 Characteristics of patients with no active joint disease
There were 95 (14.4%) of subjects with no active joint disease documented throughout
their follow-up time. These participants were followed for fewer visits than those with active
joint disease. They tended to have a longer delay to diagnosis, were older and had a higher
proportion of diagnosis of enthesitis-related arthritis (69.5%) and systemic arthritis (10.5 %)
subtypes of JIA than would have been expected based on the size of the group (Table 5). At the
first visit, none of the patients with no joint disease was taking DMARD, had intraarticular
steroids or was taking biological therapy. Fifty-four (56.8%) were taking NSAIDs and four
41
(4.2%) were taking oral corticosteroids; this is not statistically significantly different from the
group with active joint disease (60.2% on NSAIDs, p=0.5721 and 2.5% for corticosteroids,
p=0.3118).
Table 5. Comparison of the baseline demographics of the subgroup of patients with no active
joint disease with those with active joint disease*
Variable Joint disease
(n=564)
No joint disease
(n=95)
p-value
Female sex (n)(%) 364 (64.5) 38(40) <0.0001
ANA positive (n)(%) 272 (48.2) 14 (14.7) <0.0001
Age at diagnosis (mean)(SD) 8.4 ± 4.9 12.0±3.4 <0.001
Diagnostic delay (median)(25th%-75th%) 8.7±11.8 months 19.4±17.3 months <0.001
Number of visits (median)(25th%-75th%) 10 (6-15) 6 (4-9) <0.0001
ILAR diagnosis (n)(%) <0.0001
Systemic arthritis
Oligoarthritis persistent
RF negative polyarthritis
RF positive polyarthritis
Psoriatic arthritis
Enthesitis related arthritis
Undifferentiated
35 (6.2)
233 (41.3)
86 (15.2)
23(4.1)
47 (8.3)
68 (12.1)
72 (12.8)
10 (10.5)
2 (2.1)
1 (1.1)
0
7 (7.4)
66 (69.5)
9 (9.5)
Individual ILAR criteria (n)(%)
Systemic Fever 35 (6.2) 9 (9.5) 0.2508
Psoriasis 37 (6.6) 9 (9.5) 0.6213
Dactylitis 33 (26.3) 0 <0.0001
RF positive 34 (6.0) 0 0.0119
Lumbosacral back pain 87 (15.4) 60 (6.3) <0.0001
Enthesitis 76 (13.5) 72 (7.6) <0.0001
HLA-B27 positive 72 (12.8) 21 (22.1) 0.0473
1st degree relative with psoriasis 88 (15.6) 11 (11.6) 0.1470
Family hx HLA-B27 associated
disease
56 (9.9) 35 (36.8) <0.0001
ESR at first visit (mean)(SD) 31.0±27.3 22.7±26.4 0.0199
CRP at first visit (mean)(SD) 23.5±59.0 4.7±12.0 0.0011
N=number of subjects; SD=standard deviation;
* The number of subjects with complete data varies by variable
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6.4 Model Building
6.4.1 Selection of the distribution
A series of quadratic LCGA models were fitted testing the normal, negative binomial,
Poisson and zero-inflated negative binomial (ZINB) distributions (Tables 6-9). A zero-inflated
Poisson model did not fit the data. When this distribution was used, no modeling attempts
converged on a replicated log-likelihood value irrespective of the number of random starts. To
determine the distribution that fit the data optimally, the BIC values were compared.
Table 6. Fit statistics of the latent growth curve analysis for the normal distribution
Number of
classes
AIC BIC ssABIC Entropy LMR LRT
p-value
2 39276.2 39397.4 39311.7 0.991 0.1519
3 38779.9 38919.1 38820.7 0.969 0.6234
4 38498.3 38655.5 38544.4 0.975 0.4625
5 38268.5 38443.6 38319.8 0.971 0.2261
AIC = Akaike’s information criterion; BIC=Bayesian information criterion; ssABIC =
sample size adjusted BIC; LMR LRT = Lo-Mendell-Rubin likelihood ratio test
Table 7. Fit statistic of the latent curve growth analysis for the Poisson distribution
Number of
classes
AIC BIC ssABIC Entropy LMT LRT
p-value
2 35856.2 35887.6 35865.4 0.991 0.0272
3 33194.7 33244.1 33209.2 0.974 0.2712
4 31283.3 31350.7 31303.1 0.969 0.0727
5 30586.8 30672.2 30611.8 0.958 0.6542
AIC = Akaike’s information criterion; BIC=Bayesian information criterion;
ssABIC = sample size adjusted BIC; LMR LRT = Lo-Mendell-Rubin likelihood ratio test
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Table 8. Fit statistics of the latent curve growth analysis for the negative binomial distribution
Number of
classes
AIC BIC ssABIC Entropy LMR LRT
p-value
2 20591.3 20712.5 20626.8 0.874 0
3 20326.7 20465.9 20367.5 0.766 0
4 20238.2 20395.4 20284.3 0.714 0.3364
5 20163.7 20338.8 20215.0 0.791 0.0054
AIC = Akaike’s information criterion; BIC=Bayesian information criterion; ssABIC = sample
size adjusted BIC; LMR LRT = Lo-Mendell-Rubin likelihood ratio test
Table 9. Fit statistics of the latent curve growth analysis for the zero-inflated negative binomial
distribution
Number of
classes
AIC BIC ssABIC Entropy LMT LRT
p-value
2 20530.6 20665.3 20570.1 0.889 0
3 20206.3 20359.0 20251.0 0.789 0
4 20106.2 20276.9 20156.2 0.734 0
5 20005.2 20193.8 20060.4 0.730 0.0482
AIC = Akaike’s information criterion; BIC=Bayesian information criterion; ssABIC = sample
size adjusted BIC; LMR LRT = Lo-Mendell-Rubin likelihood ratio test
The distribution with the best fit to the data, based on fit indices (lowest BIC = 20665.3 for 2
class model) and distribution of the active joint count (section 6.2), was a zero-inflated negative
binomial distribution.
6.4.2 Selection of the number of classes
6.4.2.1 Fit indices
The fit statistics for the LCGA and GMM examined under the ZINB distribution are
presented in Table 10. Quadratic models for 2-6 classes were fit to the data. Considering the
BIC (20193.8) and estimated proportion for the class sizes (0.1-0.44) based on the posterior
probability the five-class LCGA (Figure 9) provides the best solution for the data. The four-class
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LCGA model (Figure 6) also provides a reasonable solution (BIC 20276.9 class size (proportion)
0.1-0.44). However in choosing the five-class solution, the additional class consists of a
clinically distinct group (“class 2” in Figure 9) comprised of patients with an initial moderate
mean AJC with progression to higher mean AJC after 5 years.
The GMM for the four-class model allowing the variance of the intercept for the non-
inflated portion of the model to estimated provides a clinically sensible solution however the
entropy is low (0.605) and class two in this model represents only 2% of the cohort (Figure 7).
The GMM for the four-class model allowing the variance of the intercept and slope growth
factor for the non-inflated portion of the model to be estimated has a smaller BIC and reasonable
class sizes but the resultant classes are not clinically sensible (Figure 8).
In selecting the final model (five-class LCGA), in addition to the BIC, the LMR-LRT
comparing the five-class LCGA to four-class LCGA was statistically significant (p=0.0482).
The bootstrapped LRT did not produce results (did not converge).
To evaluate if the fit of the quadratic term was sufficient to describe the variability in the
data, a cubic model was fit to the five-class LCGA. The cubic five-class LCGA model results in
a larger BIC (20225.376) and the fit of the estimated curves to the observed did not provide a
better fit (Figure 14). Thus the quadratic five-class LCGA was retained.
Growth mixture models of the five-class solution were run in an exploratory analysis. It
has been suggested that the k-1class GMM (here, four-class) should provide a more
parsimonious model than the k class LCGA [57] however this was the not true for our analysis.
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The five-class GMM explored were
1) Estimation of the variance of the intercept for the non-inflated portion of the model
(subjects within a class have a different starting point but generally follow the same
trend)
2) Estimation of the variance of the intercept and slope growth factors for the non-
inflated portion of the model (subjects have a different starting point and may follow a
different curve)
3) Estimation of the variance of the intercept and slope growth factor for the non-inflated
portion of the model for class 2 and 5 (large variability was observed in the plots of the
individual values around the estimated class specific mean curves) with variance of all
growth factors for class 1,3 and 4 fixed to zero.
Although the five-class GMM models provided smaller BIC values, the class sizes were
too small (1 class with <5%) and the resulting classes (Figure 10-12) were not clinically sensible.
A six-class LCGA model (k +1 class than makes sense clinically) did have a smaller BIC value
(20148.033) however, the additional class size was small (3%) (Figure 13).
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Table 10. Fit statistics of the latent curve growth analysis and growth mixture models with the zero-inflated negative binomial
distribution
2-class
LCGA
3-class
LCGA
4-class
LCGA
4-class
GMM
4-class
GMM
5-class
LCGA
5-class
GMM
5-class
GMM
5-class
GMMb
6-class
LCGA
Variance of
growth
factors
I S Q F F F F F F F F F R F F R R F F F F R F F R R F R R F F F F
Ii Si Qi F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F
Sample sizea
(proportion)
C = 1
C = 2
C = 3
C = 4
C = 5
C = 6
0.73
0.26
-
-
-
-
0.25
0.18
0.57
-
-
-
0.18
0.44
0.20
0.18
-
-
0.12
0.02
0.65
0.21
-
-
0.16
0.60
0.13
0.10
-
-
0.44
0.10
0.18
0.18
0.10
-
0.08
0.10
0.63
0.02
0.16
-
0.15
0.13
0.10
0.01
0.60
-
0.17
0.12
0.04
0.32
0.34
-
0.03
0.09
0.17
0.18
0.10
0.42
Fit statistics # parameters
AIC
BIC
ssABIC
Entropy
LMR-LRT
p-value
30
20530.5
20665.3
20570.1
0.889
0.00
34
20206.3
20359.0
20251.0
0.789
0
38
20106.2
20276.9
20156.2
0.734
0
39
19933.6
20108.7
19984.9
0.605
0
41
19858.4
20042.5
19912.3
0.695
0.0215
42
20005.2
20193.8
20060.4
0.730
0.0482
43
19890.5
20083.6
19947.1
0.565
0.1678
45
19846.7
20048.8
19905.9
0.723
0
45
19857.3
20059.4
19916.5
0.596
0.3639
46
19941.5
20148.0
20002.0
0.744
0.0127
(I)=intercept; (S)=slope; (Q)=quadratic; (Ii) = inflated intercept; (Si) = inflated slope; (Qi)=inflated quadratic; (C)=class; (F)= fixed to 0;
(R)=random; a Estimated proportion for the latent classes based on the posterior probabilities;
b variance of all growth factors fixed to 0 for
classes1,3 and 4 but non-inflated intercept and slope estimated for groups 2 and 5.
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Figure 6. Four-class latent curve growth analysis. Two growth curves are presented for each
class (○ = observed sample means; ∆ = estimated means based on the model). The percent of the
study population in each class is also presented.
Figure 7. Four-class growth mixture model (variance of the non-inflated intercept estimated)
Two growth curves are presented for each class (○ = observed sample means; ∆ = estimated
means based on the model). The percent of the study population in each class is also presented.
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Figure 8. Four-class growth mixture model (variance of the non-inflated intercept and slope
estimated). Two growth curves are presented for each class (○ = observed sample means; ∆ =
estimated means based on the model). The percent of the study population in each class is also
presented.
Figure 9. Five-class latent curve growth analysis. Two growth curves are presented for each
class (○ = observed sample means; ∆ = estimated means based on the model). The percent of the
study population in each class is also presented.
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Time (1=6 months)
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Figure 10. Five-class growth mixture model (variance of the non-inflated intercept estimated).
Two growth curves are presented for each class (○ = observed sample means; ∆ = estimated
means based on the model). The percent of the study population in each class is also presented.
Figure 11. Five-class growth mixture model (variance of the non-inflated intercept and slope
estimated). Two growth curves are presented for each class (○ = observed sample means; ∆ =
estimated means based on the model). The percent of the study population in each class is also
presented.
Time (1=6 months)
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Figure 12. Five-class growth mixture model (non-inflated intercept and slope estimated for
groups 2 and 5). Two growth curves are presented for each class (○ = observed sample means; ∆
= estimated means based on the model). The percent of the study population in each class is also
presented.
Figure 13. Six-class latent curve growth analysis. Two growth curves are presented for each
class (○ = observed sample means; ∆ = estimated means based on the model). The percent of the
study population in each class is also presented.
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Figure 14. Five-class cubic latent curve growth analysis. Two growth curves are presented for
each class. (○ = observed sample means; ∆ = estimated means based on the model)
6.4.2.2 Classification quality
A second important consideration in the selection of the optimal number of classes is the
classification quality. Based on the fit indices and class size, the four and five class solutions
were superior thus these models were evaluated. The average posterior probabilities of the four
and five class solutions are presented in Table 11 and Table 12. The five-class LCGA had a
mean posterior probability of 0.817 (0.758-0.896) and the four-class LCGA was 0.849 (0.751-
0.959). These were the 2 best fitting models as identified by the BIC and class size in section
6.4.2.1. The four and five-class LCGA models had the highest entropy values of all four and
five class solutions (0.734 and 0.730 respectively) (Table 10).
When considering the classification quality of the model, one must also evaluate the
impact of misclassification. In the five-class LCGA model, the class with the lowest average
posterior probability is class 3 (0.758), of all patients most likely to be in class 3, 23.3% may be
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classified into class 1. For the four-class model, the class with the lowest average posterior
probability is class 4 (0.751), of all patients most likely to be in class 4, 24% may be classified
into class 2. In both models, the potential for misclassification is from the “no active joint
disease group” into the “minimal active joint disease group (initial mean approximately 1).”
Table 11. Average posterior probabilities for five-class solutions
Five-class LCGA (variance of all growth factors fixed to 0)
Class 1 2 3 4 5
1 0.845 0.004 0.066 0.084 0.001
2 0 0.763 0.00 0.092 0.144
3 0.233 0.001 0.758 0.008 0
4 0.062 0.089 0 0.824 0.024
5 0 0.091 0 0.013 0.896
Five-class GMM (variance of non-inflated intercept estimated)
Class 1 2 3 4 5
1 0.775 0.012 0.199 0 0.015
2 0.002 0.712 0.141 0.007 0.137
3 0.078 0.054 0.741 0.011 0.116
4 0 0.002 0.011 0.950 0.038
5 0 0.087 0.172 0.014 0.728
Five-class GMM (variance of non-inflated intercept and slope estimated)
Class 1 2 3 4 5
1 0.660 0.001 0.001 0.031 0.307
2 0 0.690 0.235 0 0.075
3 0 0.247 0.703 0 0.050
4 0 0 0 1.000 0.000
5 0.055 0.035 0.028 0.001 0.881
Five-class GMM (variance of non-inflated intercept and slope estimated for class 2 and 5)
Class 1 2 3 4 5
1 0.727 0.005 0 0.202 0.066
2 0 0.746 0.051 0 0.203
3 0 0.142 0.736 0 0.122
4 0.061 0.021 0 0.683 0.236
5 0 0.145 0.017 0.065 0.773
Each row contains information for individuals who were most likely to be in the class
represented by that row; LCGA=latent curve growth analysis; GMM=growth mixture model
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Table 12. Average posterior probabilities for four-class solutions
Four-class LCGA (variance of all growth factors fixed to 0)
Class 1 2 3 4
1 0.959 0 0.041 0
2 0.002 0.848 0.085 0.065
3 0.096 0.066 0.838 0
4 0.001 0.240 0.008 0.751
Four-class GMM (variance of non-inflated intercept estimated)
Class 1 2 3 4
1 0.702 0.006 0.091 0.202
2 0.003 0.951 0.003 0.043
3 0.044 0.010 0.823 0.123
4 0.174 0.016 0.152 0.657
Four-class GMM (variance of non-inflated intercept and slope estimated)
Class 1 2 3 4
1 0.690 0.308 0.001 0.001
2 0.054 0.882 0.035 0.029
3 0 0.069 0.699 0.232
4 0 0.051 0.247 0.702
Each row contains information for individuals who were most likely to be in the class
represented by that row; LCGA=latent curve growth analysis; GMM=growth mixture model
6.4.2.3 Clinical sensibility
Finally and most importantly, the model selection must be considered in terms of clinical
sensibility and usefulness. As outlined above, the final modeled selection represents a possible
solution but this needs to be considered in relation to important predictors (clinical, genetic) and
to distal outcomes. A summary of the model fit qualities is presented in Table 13. This table
highlights the final two models – four-class LCGA and five-class LCGA. The five-class LCGA
model was selected based on clinical relevance and usefulness. The five-class model provides
one further clinically meaningful class (“Class 2”, Figure 9) that represents 10% of the total
population. Given our clinical knowledge of the disease course, this class represented a subset of
patients that present with moderate active joint disease but progress to a more aggressive disease
course. The four-class growth mixture model with the variance of the non-inflated slope
54
estimated could also fit with physicians’ impression of disease course however the entropy and
posterior probabilities are lower and “Class 2” represents only 2% of the population (Figure 7) .
For these reasons, the five-class LCGA was retained for further analysis. The growth curves of
the estimated means based on the model for each class are presented in Figures 15-19 and in
Figure 20; all five classes are presented together. The five classes are described below with
clinical relevance are described below:
1. “Persistent high” (9.8% of study population) – initial mild polyarthritis (mean AJC 14.1)
followed by a gradual decrease in mean AJC over years.
Figure 15. Five-class latent growth curve analysis. Growth curve of estimated class means
based on the model. “Persistent high class” (9.8%)
55
2. “Moderate increasing” (10% of study population) - initial mean AJC of 5.5 followed by an
increasing AJC at 5 years (mean AJC 9.7).
Figure 16. Five-class latent growth curve analysis. Growth curve of estimated class means based
on the model. “Moderate increasing class” (10%)
3. “Persistent moderate” (18.5% of study population) – initial mean AJC 3.2 followed by
persistent moderate AJC)
Figure 17. Five-class latent growth curve analysis. Growth curve of estimated class means based
on the model. “Persistent moderate class” (18.5%)
56
4. “Persistent low” (43.6% of study population) - minimal disease activity (initial mean AJC
0.9) followed by improvement.
Figure 18. Five-class latent growth curve analysis. Growth curve of estimated class means based
on the model. “Persistent low” (43.6%)
5. “No joint activity” (18.2% of study population) - minimal to no active joint disease
throughout course (initial mean AJC 0.3).
Figure 19. Five-class latent growth curve analysis. Growth curve of estimated class means based
on the model. “No joint activity” (18.2%)
57
Figure 20. Five-class latent growth curve analysis. Growth curves for all five classes based on
the estimated sample means from the model. Class size is also presented.
6.4.2.4 Association of baseline characteristics with identified trajectories
The association of baseline characteristics, stratified by class is presented in Table 14.
The evaluation of differences based on preexisting characteristics is important to consider in
evaluating clinical usefulness of models [48]. Enthesitis, dactylitis, ethnicity, ESR and CRP
were not considered in this analysis as these variables had >20% missing values. There was a
statistically significant difference between the classes for all variables considered (p-values for
F-test or χ2 all < 0.05) except for psoriasis (p=0.2622).
The persistent low class contained the highest proportion of the ILAR subtype
oligoarthritis-persistent. The proportion of ANA positive subjects was higher in the persistent
low, moderate increasing and persistent moderate classes than the persistent high or no joint
activity classes. Polyarthritis RF negative and positive patients were mostly in the moderate
increasing and persistent high classes that are clearly two distinct trajectories. The no joint
activity class participants were more likely to have lumbosacral back pain, be HLA-B27 positive
58
and have a family history of HLA-B27 associated diseases but less likely to be ANA positive.
This class contained a significant portion of subjects with systemic arthritis however as did the
persistent high class. The persistent moderate group contained a higher proportion of patients
with diagnoses of RF negative polyarthritis and psoriatic arthritis.
The most significant finding when examining the characteristics of the subjects in each
class is that the ILAR subtypes are dispersed among the classes. This finding supports the
concept that the variability in disease course over time is not adequately explained by the ILAR
classification alone.
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Table 13. Summary of model qualities
Quality
∆AICa
∆BICa Class size
adequate
LMR LRT
p-value
Entropy Posterior Probability
(mean)(range)
Clinical
sensibility
Model
2-class LCGA Y Y Y Y Y 0.968 (0.960-0.975) N
3-class LCGA Y Y Y Y Y 0.907 (0.850-0.955) N
4-class LCGA Y Y Y Y Y 0.849 (0.751-0.959) Y
4-class GMM (variance I estimated)
Y Y N Y N 0.783 (0.657-0.951) Y
4-class GMM (variance I/S estimated)
Y Y Y Y Y 0.743 (0.690-0.882) N
5-class LCGA Y Y Y Y Y 0.817 (0.763-0.896) Y
5-class GMM (variance I estimated)
Y Y N Y N 0.781 (0.712-0.950) N
5-class GMM (variance I/S estimated)
Y Y N Y Y 0.787 (0.690-1.0) N
5-class GMM (variance I/S estimated for class 2/5)
Y Y N Y N 0.733 (0.683-0.773) N
6-class LCGA Y Y N Y Y 0.807 (0.731-0.901)
(I)=non-inflated intercept; (S)=non-inflated slope; AIC= Akaike’s information criterion; BIC=Bayesian information criterion;
LMR LRT= Lo=Mendell-Rubin likelihood ratio test; aAIC/BIC significantly better than k-1 model
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Table 14. Characteristics of the study participants, stratified by trajectory *
Persistent
low
Moderate
increasing
No joint activity Persistent
moderate
Persistent
high
Number of subjects in class 295 65 133 108 58
Variable p-value
Female sex (n/N)(%) 175 (59.3) 46 (70.8) 61 (45.9) 79 (73.2) 41 (70.7) <0.0001
Age at diagnosis (mean)(SD) 8.5 (4.8) 10.7 (4.7) 10.0 (4.7) 7.7 (5.3) 8.8 (4.7) <0.0001
ANA positive (n/N)(%) 137/277 (49.5) 32/61 (52.5) 32/130 (24.6) 59/103 (57.3) 26/58 (44.8) <0.0001
Diagnostic delay, months (median)(25th%-75th%) 4.3 (2-10.2) 5.2 (3.2-13.1) 10.1 (2.8-24.4) 4.4 (2.4-11.7) 4.4 (2.6-9.5) 0.0004
ILAR diagnosis (n/N)(%) < 0.0001
Systemic arthritis
Oligoarthritis
RF negative polyarthritis
RF positive polyarthritis
Psoriatic arthritis
Enthesitis related arthritis
Undifferentiated
14 (4.8)
166 (56.3)
12 (4.1)
1 (0.3)
16 (5.4)
49 (16.6)
37 (12.5)
4 (6.2)
4 (6.2)
27 (41.5)
8 (12.3)
6 (9.2)
5 (7.7)
11 (16.9)
12 (9.0)
27 (20.3)
1(0.8)
0
9 (6.8)
70 (52.6)
14 (10.5)
6 (5.6)
35 (32.4)
26 (24.1)
4 (3.7)
19 (17.6)
8 (7.4)
10 (9.3)
9 (15.5)
3 (5.2)
21 (36.2)
10 (17.2)
4 (6.9)
2 (3.5)
9 (15.5)
No active joint disease 0 0 95 (100) 0 0 <0.0001
Individual ILAR criteria (n/N)(%)
Systemic Fever 11/292 (3.8) 4/65 (6.2) 12/133 (9.0) 7/108 (6.5) 11/57 (19.3) 0.0084
Psoriasis 15/220 (6.8) 3/43(7.0) 12/124 (9.7) 13/89 (14.6) 3/43 (7.0) 0.2622
RF positive 6/267 (2.2) 10/60 (16.7) 0 5/98 (5.1) 13/58 (22.4) <0.0001
Lumbosacral back pain 52/232 (22.4) 6/49 (12.2) 65/127 (51.2) 16/77 (20.8) 8/41 (19.5) <0.0001
HLA-B27 positive 47/242 (19.4) 2/50 (4.0) 23/125 (18.4) 18/93 (19.4) 3/49 (6.1) 0.0190
1st degree family hx psoriasis 34/251 (13.5) 15/52 (28.8) 17/126 (13.5) 25/86 (29.1) 8/49 (16.3) 0.0022
Family hx HLA-B27 assoc disease 35/248 (14.1) 7/50 (14.0) 39/129 (30.2) 9/81 (11.1) 1/50 (2.0) <0.0001
Treatment during follow-up (n/N)(%)
DMARD
Biologic therapy
Intraarticular injection
Oral corticosteroid therapy
65 (22.0)
9 (3.1)
129 (43.7)
33 (11.2)
35 (53.9)
6 (9.2)
15 (23.1)
13 (20.0)
4 (3.0)
2 (1.5)
17 (12.8)
11 (8.3)
58 (53.7)
7 (6.5)
49 (45.4)
14 (13.0)
46 (79.3)
10 (17.2)
13 (22.4)
23 (39.7)
<0.0001
<0.0001
<0.0001
<0.0001
* Percentages are expressed as % of class; n/N= number positive/number tested, when N is not specified there was no missing data for the variable and N = class
size
61
7. DISCUSSION
7.1 Overview and strengths of study
In this 10-year retrospective cohort study (8324 patient visits), we explored the use of a
novel statistical approach to characterize disease course in JIA. This study is the first to use a
longitudinal growth modeling technique to identify homogenous subsets (classes) of patients that
follow a similar pattern of disease activity over time. As outlined above, we argue that in a
chronic and fluctuating illness like JIA, the appropriate outcome measure is disease course rather
than a fixed outcome assessed at one point in time. We identified 5 clinically and statistically
distinct trajectories of disease course. The subsets of patients within each class were different
from those described by the ILAR classification criteria. The results of this study are important
and provide evidence that clinical heterogeneity observed between and within the ILAR subtypes
is not adequately captured by the ILAR criteria.
7.2 Key findings
7.2.1 Successful application of a novel longitudinal data modeling technique
The current study successfully used a novel approach to longitudinal growth curve
modeling. The main objective of this study was to apply latent growth curve modeling
methodology to characterize the disease course in JIA. This first attempt used the active joint
count as the trajectory descriptive variable, however, future work needs to examine other
components of disease activity. Despite the fact that latent growth curve modeling is in its
infancy, it does show promise in the identification of homogeneous subsets within our
heterogeneous population. The results from this study support the use of this methodology in a
chronic disease with a fluctuating course like JIA.
62
A five-class latent curve growth analysis solution was chosen as the final model. It
should be reiterated that the selected model in our study does not represent the “final” answer but
one possible solution. The five classes need to be evaluated to see if they differ based on
preexisting characteristics, subsequent outcomes or their response to treatment. These aspects are
most important to determine clinical usefulness. Within the scope of this study, the baseline
characteristics were evaluated with univariate techniques. However, the association of a
trajectory class with biological predictors, response to treatment or to a more distal outcome
needs to be addressed.
7.2.2 Clinical interpretation of the trajectory groups and characteristics of the trajectory
group members
The persistent low and no joint activity classes may represent those subjects whose joint
disease was relatively easy to control with therapy. It is also probable for the no joint disease
group that there are other aspects of the disease (enthesitis, fever, rash, inflammatory back pain)
not captured by using active joint count as a marker of disease activity. The moderate persistent
class represents those that may have required ongoing or intensification in therapy. The
moderate increasing and persistent high groups were characterized by a more refractory disease;
these groups may represent those subjects who required an escalation in therapy (Figures 15 &
16).
If we consider the distribution of the ILAR criteria, certain subtypes are primarily found
in one class (oligoarthritis) whereas others are dispersed among several classes (systemic and
polyarthritis) (Table 14). This is consistent with clinicians’ impression regarding the
heterogeneity of the disease course of patients within a class. An observational study has
63
supported this concept. This small study (n=45) of systemic arthritis patients described the
course of disease as monophasic (one episode of active disease not lasting more than 24 months),
polycyclic (≥ 1 active and inactive disease) and persistent (active disease for more than 24
months). In this study active disease was any of joint disease, systemic features or inflammatory
blood work [39].
With regard to the polyarthritis RF negative patients, at least two distinct subtypes are
recognized. The first is a form similar to adult-onset RF negative RA and is characterized by
symmetric synovitis of large and small joints, onset in school age and negative ANA. The second
resembles oligoarthritis (asymmetric arthritis, early age at onset, female predominance, ANA
positivity) except for the number of joints affected in the first six months [22, 78]. This is
consistent with our results as the RF negative polyarthritis patients are distributed among three
classes (moderate increasing, persistent moderate and persistent high).
The oligoarthritis patients are primarily in the persistent low class. A long-term outcome
study reporting on 207 oligoarticular onset patients, found that at the end of 6 years of follow-up,
the probability of a polyarticular course was 50%. [31]. Our cohort had 12.8% (30/235) of the
oligoarthritis patients who followed a polyarticular course. There were only 4 (6.2%)
oligoarthritis patients in the moderately increasing group. There are a few potential reasons why
this occurred. First, if the polyarticular extension was mild (low total active joint count), this
may not have been detected by the LCGA that identifies average change over time within a
group. Second, if the patient was treated and quickly returned to low active joint counts, this
rapid transition may not have been detected with 6 monthly visits as the effect of medication was
not accounted for in our study.
64
A few groups have proposed that psoriatic subtype should not be part of the classification
criteria as the course of oligoarticular and polyarticular onset psoriatic arthritis patients may not
be clinically different from the oligoarthritis and polyarthritis (non-psoriatic) subtype patients
[78-79] . We found that the proportion of patient with psoriasis in each class was not
significantly different between the classes supporting the concept that the presence of psoriasis is
not sufficient to determine a homogeneous group of patients (p=0.2622).
It would be important to assess the baseline characteristics in a multinomial predictive
model to determine the key factors that determine membership in a trajectory. This would
provide an unbiased estimate of the effect of each of the predictors while controlling for the
known confounders. Novel genetic and biologic markers could similarly be assessed in this way.
7.3 Limitations
As with all studies, our study has limitations that are important to address. These include
limitations related to the statistical software, retrospective study design, issues of data
availability and quality, lack of account for the effect of medication on trajectories and the use of
the AJC to describe the disease course.
7.3.1 Limitations of the Mplus software
The Mplus software was developed 10-years ago and is in its sixth version but this software
is not as user friendly as other commercially available software. The user guide provides limited
information for language to code the models and provides virtually no theoretical background
information [59]. The developer of the software, however, is quite helpful to troubleshoot technical
aspects. There were still difficulties encountered in deciding on the correct distribution to describe
65
the data, issues with model selection and model non-convergence. Two illustrative examples are 1)
the zero-inflated Poisson distribution did not converge for any model and 2) the BLRT failed to
execute. The BLRT is proposed to be the optimal method to choose the number of latent classes
between the final two models [60]. In a private correspondence, Muthen suggested using only the
BIC and not the bootstrapped LRT for “models of this complexity” [80]. When a model fails to
execute, it is not possible to determine if it related to the data or to the computational burden on the
software. These technical difficulties highlight the relative immaturity of this software for latent
growth curve modeling.
In addition to technical difficulties, it is a challenge to properly navigate the process of model
building with the latent growth curve approach. When exploring the random effects models (growth
mixture modeling), there is a multiplicity of combinations of random intercept, slope and quadratic
terms. Furthermore, when adding more than a random intercept and slope term to the model, the
interpretation of the results becomes increasingly more difficult. Concrete guidelines for model
building need to be established to ensure a parsimonious and reproducible model is found.
7.3.2 Limitations of the study design
This longitudinal cohort study was a retrospective chart review. As such, there is a lack of
control over how and what data were originally recorded which may lead to inaccurate data.
Additionally, as with any study, missing data is a concern. The study visits were arbitrary and set to 6
months +/- 2 months from the previous visit. If a visit did not occur during this interval, a missed
visit was recorded. There were 15.8% (1318/8254) missing visits. However, for the visits that were
recorded, the outcome variable (AJC) was missing in only 33. Given a cohort of this size, there was a
relatively small number of missing information for the AJC, which was the major focus of this study.
66
There was a significant percentage of missing data for the baseline characteristics of the
study subjects. The variables ESR, CRP, enthesitis and dactylitis had >25% missing and were not
evaluated in the univariate analysis. The following variables had 14-20% missing data: psoriasis,
family history of HLA-B27 associated diseases, family history of psoriasis, lumbosacral back pain
and HLA-B27 positivity). Certainly, the lack of precision in documenting this information could
have impaired the detection of true relationships between the baseline predictors and class
membership.
Medication data was not included in the latent growth curve modeling. This was due to
technical complexity and interpretability of the results of a time-varying covariate in the context of
the relatively immature latent growth curve modeling technique and software. Medication exposure
is an important determinant of the shape of the trajectory as well as individual class membership. The
results of our study do not reflect the natural history of JIA but rather a treated group of patients.
To minimize the effect of medications started before study entry, we sought to create an
inception cohort that was treatment naïve. Despite the exclusion criteria of patients that were
diagnosed more than 90 days before seeing the rheumatologist, at the first visit 60% were taking
NSAIDs, 3% DMARD, 3% oral corticosteroids for 18 (3%) and 11% had intraarticular steroid
injections. There were 95 subjects with no joint disease throughout their follow-up time and many
zeros at each time point for the other 564 subjects (Appendix D). The shape of trajectories is
affected by medication however, medication exposure occurs as a result of disease activity that
required more or less treatment. Thus, the class shape we observed is confounded by the effect of
medication. It is impossible to separate the relative contribution of medication effect and disease
activity in determining class membership without properly accounting for the effect of medication in
the latent growth curve models.
67
7.3.3 Limitation of AJC as outcome measure
JIA is a multidimensional disease and the active joint count does not represent all important
aspects of disease activity. Other important aspects of disease to consider include components of the
ACR JIA disease core set (Child health assessment questionnaire, Parent/patient global assessment,
Physician global assessment, ESR) [67]. Additional factors that may also be important for disease
activity are the size and symmetry of the joints involved [34]. For patients with systemic onset JIA,
both systemic features and inflammatory markers are important determinants of disease activity and
disease course [32]. In using the active joint count as the disease activity measure, the course of
disease in those subjects whose disease manifested as enthesitis, uveitis or systemic features would
not be adequately characterized.
Given the successful application of this technique using the active joint count as the
prototype, other components of disease activity can now be evaluated. The other components could
be used as the trajectory descriptive variable or as a covariate in the model with the active joint
count. Muthen has suggested that an analysis without covariates can be useful to study different
growth in different trajectory classes. However, it is expected that the class distribution or
individual classification will not remain the same when adding covariates [58].
7.4 Future directions
Future studies should address the identified shortcomings with a prospective cohort study
to minimize missing data as well as collect information for other important aspects of disease
activity. In addition, the final few models (five-class latent curve growth analysis, four-class
latent curve growth analysis and four-class growth mixture model with variance of the non-
inflated intercept estimate) need further evaluation. The latent classes from these models should
be evaluated for their predictive ability for distal outcomes and for their relationship to important
68
biological predictors. For each model, the clinical usefulness needs to be evaluated by
experienced clinicians in pediatric rheumatology.
This method should be replicated in another cohort to evaluate for consistency in the
shape and number of trajectories. Data from a large prospective Canadian multicenter cohort
study (Research in Arthritis in Canadian Children focusing on outcome, ReACCH-Out) [18] will
be used to validate the technique and in particular, the five-class solution. Importantly, the effect
of medication on shape and number of trajectories needs to be evaluated in future studies.
To enable longitudinal growth curve modeling to be used more commonly in clinical
medicine further work needs to be done. Formalized criteria need to be developed to aid in
model building and statistical model selection processes. Additionally, there needs to be more
documentation for the software program to inform users of these techniques.
69
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9. APPENDICES
Appendix A: Comparison of the ILAR, JRA and JCA criteria [81]
Comparison of Classifications of Chronic Arthritis in Children
Juvenile Rheumatoid
Arthritis (ACR)
Juvenile Chronic Arthritis
(EULAR)
Juvenile Idiopathic Arthritis
(ILAR)
Systemic Systemic Systemic
Polyarticular Polyarticular Polyarticular RF-negative
Oligoarticular (pauciarticular) Juvenile rheumatoid arthritis Polyarticular RF-positive
Pauciarticular Oligoarticular
Persistent
Extended
Juvenile psoriatic arthritis Psoriatic arthritis
Juvenile ankylosing
spondylitis
Enthesitis-related arthritis
Other arthritis
74
Appendix B: ILAR Classification of JIA: Second Revision, Edmonton, 2001[11]
Exclusions
a. Psoriasis or a history of psoriasis in the patient or first degree relative
b. Arthritis in an HLA-B27 positive male beginning after the 6th
birthdat
c. Ankylosing spondylitis, Enthesitis related arthritis, sacroiliitis with inflammatory bowel
disease, Reiter’s syndrome, or acute anterior uveitis, or a history of one of these disorders
in a first-degree relative.
d. The presence of IgM rheumatoid factor on at least 2 occasions at least 3 months apart
e. The presence of systemic JIA in the patients
Categories
1. Systemic arthritis
Definition: arthritis in one or more joints with or preceded by fever of at least 2 weeks’ duration
that is documented to be daily (“quotidian”) for at least 3 days, and accompanied by one of the
following:
1. Evanescent (nonfixed) erythematous rash
2. Generalized lymph node enlargement
3. Serositis
Exclusions: a, b, c, d
2. Oligoarthritis
Definition: Arthritis affecting one to 4 joints during the first 6 months of disease. Two
subcategories are recognized:
1. Persistent oligoarthritis: affecting not more than 4 joints throughout the disease course
2. Extended oligoarthritis: affecting a total of more than 4 joints after the first 6 months of
disease
Exclusions: a, b, c, d, e
3. Polyarthritis (RF negative)
Definition: arthritis affecting 5 or more joints during the first 6 months of disease: a test for RF is
negative.
Exclusions: a, b, c, d, e
4. RF negative Polyarthritis
Definition: arthritis affecting 5 or more joints during the first 6 months of disease; 2 or more tests
for RF at least 3 months apart during the first 6 months of disease are positive.
Exclusions: a, b, c, e
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5. Psoriatic arthritis
Definition: Arthritis and psoriasis, or arthritis and at least 2 of the following:
1. Dactylitis
2. Nail pitting or onycholysis
3. Psoriasis in a first-degree relative
Exclusions: b, c, d, e
6. Enthesitis related arthritis
Definition: arthritis and Enthesitis, or arthritis or Enthesitis with at least 2 of the following:
1. The presence of or a history of sacroiliac joint tenderness and/or inflammatory
lumbosacral pain
2. The presence of HLA-B27 antigen
3. Onset of arthritis in a male over 6 years of age
4. Acute (symptomatic) anterior uveitis
f. History of ankylosing spondylitis, enthesitis related arthritis, sacroiliitis with
inflammatory bowel disease, Reiter’s syndrome, or acute anterior uveitis in a first-degree
relative.
Exclusions: a, d, e
7. Undifferentiated arthritis
Definition: Arthritis that fulfills criteria in no category or in 2 or more of the above categories.
76
Appendix C: Data abstraction form
I. PATIENT ID, DEMOGRAPHICS
Patient study identification number: Sex: (Circle One) M F
Date of birth (DD-MM-YYYY)_____________
RACE: (CHECK ONE)
□ 01 ARAB
□ 02 BLACK- AFRICAN, CARRIBEAN
□ 11 ABORIGINAL, INUIT
□ 12 ABORIGINA,L NORTH AMERICAN INDIAN
□ 21 CHINESE, 23 KOREAN, or 24 JAPANESE
□ 22 FILIPINO
□ 26 SOUTH ASIAN (INDIAN SUBCONTINENT)
□ 27 SOUTHEAST ASIAN (VIETNAMESE, BURMESE, CAMBODIAN, THAI, MALAY, INDONESIAN)
□ 31-36 CAUCASIAN EUROPEAN
□ OTHER
DIAGNOSIS:
Initial arthritis diagnosis (made by physician) Check One If diagnosis revised later to another
category, enter relevant date
pauciarticular JRA
polyarticular JRA-RF negative
polyarticular JRA-RF positive
systemic JRA
Psoriatic arthritis
SEA syndrome
Juvenile Ankylosing spondylitis
Systemic JIA (ILAR)
Oligoarticular JIA , persistent (ILAR)
Oligoarticular JIA, extended (ILAR)
Polyarticular JIA, RF negative (ILAR)
Polyarticular JIA, RF positive (ILAR)
Psoriatic JIA (ILAR)
Enthesitis-related arthritis JIA (ILAR)
Undifferentiated JIA (ILAR)
Date of symptom onset of arthritis (fever if systemic JIA) (DD-MM-YYYY)
Date of initial diagnosis (DD-MM-YYYY)
Date of first visit: at this paediatric rheumatology centre (DD-MM-YYYY)
77
II. DIAGNOSTIC CRITERIA, COMORBIDITY
CIRCLE ONE
IF YES, DATE
FIRST NOTED
(DD-MM-YYYY)
Diagnostic criteria, exclusions, and other lab: YES NO
Rheumatoid factor (RF) positive x 2, > 3 months apart YES NO
Fever≥2 weeks, quotidian pattern, documented >3 days YES NO
Systemic JIA rash YES NO
Lymphadenopathy YES NO
Hepatosplenomegaly YES NO
Serositis YES NO
Psoriasis YES NO
Dacytilitis YES NO
Nail pitting YES NO
HLA-B27 positive YES NO
Male with onset of arthritis after 6th
birthday YES NO
Acute uveitis YES NO
Enthesitis YES NO
Sacroiliac (SI )joint tenderness and/or lumbosacral pain YES NO
First degree relative with Anklyosing spondylitis (AS) YES NO
First degree relative with sacroiliitis YES NO
First degree relative with acute uveitis YES NO
First degree relative with psoriasis/ psoriatic arthritis YES NO
First degree relative with inflammatory bowel disease
(IBD)
YES NO
First degree relative with reactive arthritis YES NO
Antinuclear Antibody (ANA) positive, ever YES NO
Co-morbidity (e.g. diabetes, osteoporosis, cataracts, asthma, allergic rhinitis, hypertension,
seizure disorder, hypothyroidism, attention deficit/hyperactivity syndrome, developmental delay,
macrophage activation syndrome)
DATE FIRST NOTED
(DD-MM-YYYY)
1.
2.
3.
4.
5.
6.
78
IV. VISITS- including first visit- Joint count Visit 1
Date
______________
Visit 2
Date
______________
Visit 3
Date
______________
Visit 4
Date
______________
Number of active joints
Systemic Fever YES NO YES NO YES NO YES NO
Systemic Rash YES NO YES NO YES NO YES NO
Hepatosplenomegaly YES NO YES NO YES NO YES NO
Lymphadenopathy YES NO YES NO YES NO YES NO
Serositis (pleuritis/pericarditis) YES NO YES NO YES NO YES NO
Enthesitis present YES NO YES NO YES NO YES NO
DMARDs at time of visit Visit 1 Visit 2 Visit 3 Visit 4
Methotrexate
Currently on:
YES □
NO □
Currently on:
YES □
NO □
Currently on:
YES □
NO □
Currently on:
YES □
NO □
Sulfasalazine Currently on:
YES □
NO □
Currently on:
YES □
NO □
Currently on:
YES □
NO □
Currently on:
YES □
NO □
Hydroxychloroquine
(Plaquenil) Currently on:
YES □
NO □
Currently on:
YES □
NO □
Currently on:
YES □
NO □
Currently on:
YES □
NO □
Other DMARD
(e.g. intramuscular gold
[aurothiomalate], oral gold,
leflunomide [Arava],cyclosporine
[Neoral], mycophenolate [Cellcept],
chloroquine, chlorabmucil, etc.) or
biologic (e.g. Enbrel, Remicade,
Anakinra, rituximab,)
DMARD name
______________
Currently on:
YES □
NO □
DMARD name
______________
Currently on:
YES □
NO □
DMARD name
______________
Currently on:
YES □
NO □
DMARD name
______________
Currently on:
YES □
NO □
DMARD name
______________
Currently on:
YES □
NO □
DMARD name
______________
Currently on:
YES □
NO □
DMARD name
______________
Currently on:
YES □
NO □
DMARD name
______________
Currently on:
YES □
NO □
79
Other Medications Visit 1 Visit 2 Visit 3 Visit 4
NSAIDS (e.g. Ibuprofen [Motrin/
Advil], diclofenac[Votaren],
indomethacin [Indocid],
naproxen[Naprosyn], etc
Currently on:
YES □
NO □
Currently on:
YES □
NO □
Currently on:
YES □
NO □
Currently on:
YES □
NO □
Prednisone Currently on:
YES
NO □
Currently on:
YES □
NO □
Currently on:
YES □
NO □
Currently on:
YES □
NO □
Laboratory data value at time of visit or within 3 months before visit date
Visit 1
Lab date
___________
Visit 2 Lab date ___________
Visit 3 Lab date ___________
Visit 4 Lab date ___________
Erythrocyte sedimentation rate
(ESR)
C-reactive protein (CRP)
Hemglobin
Platelet count
RF titre
ANA titre
Joint injections, Intravenous immune globulin (IVIG), methylprednisolone
Joint injected: Record dates of injections, if applicable
Shoulder_right
Shoulder_left
Elbow_right
Elbow_left
Wrist_right
Wrist_left
Hip_right
Hip_left
Knee_right
Knee_left
Ankle_right
Ankle_left
Other joint
IVIG –Ever (circle one) YES NO
IF YES: Start date
(DDMMYYYY)
Stop date
(DDMMYYYY)
Number of IVIG doses per course
Course 1
Course 2
80
Appendix D. Flow chart of patients and exclusions
1074 subjects from Winnipeg and Saskatoon
730 subjects
728 subjects
713 sujbects
659 subjects (361 Saskatoon, 298 Winnipeg)
344 subjects, < 3 visits with
rheumatologist
2 subjects, no
first visit
15 subjects, not JIA
54 subjects, diagnosis
>90 days before first visit
81
Appendix E. Frequency of AJC at each visit. Each unit of timeframe is 6 months of follow-up.
82
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