Post on 27-Mar-2015
transcript
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Why do people use LOCF? Or why not?
Naitee Ting, Allison BraileyPfizer Global R&D
CT Chapter Mini Conference
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Outline Last Observation Carried Forward
(LOCF) Data set description Modeling approaches Concerns in clinical Trials SAP concerns Why or why not use LOCF
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Observed data from each patient over time
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Observed Weeks in Study
Pa
tie
nts
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Complete Data
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0 1 2 3 4 5 6
PID 1PID 2PID 3PID 4
Visits
VA
S P
ain
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0 1 2 3 4 5 6
PID 1PID 2PID 3PID 4
Visits
VA
S P
ain
Last-Observation-Carried-Forward
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LOCF Conservative? Or anti-
conservative? Biased point estimate May underestimate variance
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50
55
60
65
70
75
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Time (in weeks)
Me
an
Ch
an
ge
fro
m B
as
elin
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Placebo Treatment
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0 1 2 3 4 5 6 7 8 9 10
Time (in weeks)
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Treatment Placebo
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Data set Simulated - standing diastolic BP Eight week study of test drug vs placebo Clinic visit every 2 weeks Primary endpoint – change in standing BP
from baseline to week 8 Patients completed the study or dropped
out at various time points Missing completely at random
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Simulated data ctr pid trt wk0 wk2 wk4 wk6 wk8
501 1 1 103.9 102.0 103.6 102.2 100.4 501 2 0 105.9 111.8 112.5 115.0 117.0 501 5 0 93.8 98.4 103.4 104.5 116.7 501 6 1 102.8 87.4 72.8 60.9 48.5 501 11 0 109.4 105.3 99.2 96.9 89.7 501 15 0 93.9 81.6 66.1 50.5 40.3 501 16 1 92.4 83.6 71.7 66.2 56.5 501 18 0 99.3 99.0 101.9 102.5 103.2 502 1 0 105.8 102.7 87.5 84.9 78.8 502 4 1 102.0 100.3 101.1 95.7 . 502 5 1 110.3 116.8 120.6 132.7 136.8 502 8 0 125.6 121.7 116.1 110.0 108.5 502 9 1 92.9 91.4 82.1 . . 502 12 0 123.7 121.7 118.3 122.0 120.3 502 13 0 107.7 121.4 141.5 154.7 168.9 502 16 1 112.1 109.6 103.6 103.3 104.2
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Modeling approaches Many proposals to deal with dropouts Mixed model approach
Repeated measures Random intercept, random slope
Single imputation Multiple imputation
Imputation model Analysis model
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ANCOVA on LOCF data Source | df MS F p-Value TREATMENT | 1 2441.0 4.13 0.0444 CENTER | 8 765.8 1.30 0.2523 BASELINE | 1 318.4 0.54 0.4644 ERROR |119 591.1 Statistic Test Drug Placebo Raw Mean -9.40 -0.54 Adj Mean -8.93 -0.26 Std Error 3.08 3.01 N 65 65
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Analysis of completed cases
Source | df MS F p-Value TREATMENT | 1 1963.6 3.32 0.0713 CENTER | 8 1007.2 1.70 0.1060 BASELINE | 1 73.2 0.12 0.7258 ERROR |109 592.0 Statistic Test Drug Placebo Raw Mean -10.40 -1.72 Adj Mean -10.23 -2.11 Std Error 3.27 3.14 N 60 60
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Naive interpretation If LOCF provides statistical
significance If completer analysis supports
LOCF True story may lie between the
two Clinical conclusion can be made
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Mixed model analysis For demonstration purposes, only
repeated measure results are presented
proc mixed method=reml ; where week>0 ; class pid trt week ctr ; model y=wk0 trt ctr week trt*week/solution ; repeated week / type=cs subject=pid r rcorr ; estimate 'trt dif at week 8' trt -1 1 trt*week
0 0 0 -1 0 0 0 1 / cl alpha=0.05 ;
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Results from PROC MIXED Num Den Effect DF DF F Value Pr > F
Baseline 1 456 3.03 0.0826 Treatment 1 16 5.57 0.0313 Center 8 85 5.43 <.0001 Week 3 108 2.46 0.0662 Trt*week 3 46 1.22 0.3132 Standard Label Estimate Error DF t Value Pr > |t|
week 8 dif 7.3739 3.0127 46 2.45 0.0183
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Single or multiple imputation Mixed model can be considered as
single imputation For imputation, we can use the
same model for imputation and analysis
However, one model can be used for imputation, but a different one is for analysis
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Should LOCF be used? After the modeling approaches
became available, use of LOCF have been discouraged
Models are developed with assumptions
More complicated models require more assumptions
Are these assumptions justified?
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Should LOCF be used? LOCF is a model and there are
simple assumptions behind it In New Drug Applications (NDA),
LOCF is still widely used Why?
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Different phases in clinical trials Phase I, II, III, IV Phase I – How often? Phase II – How much? Phase III – Confirm Phase IV – Post-Market
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DOES THE DRUG WORK? Double-blind, placebo controlled,
randomized clinical trial Test hypothesis - does the drug
work? Null hypothesis (H0) - no difference
between test drug and placebo Alternative hypothesis (Ha) - there is
a difference
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TYPES OF ERRORS Regulatory agencies focus on the
control of Type I error Probability of making a Type I error
is not greater than In general, = 0.05; i.e., 1 in 20 Avoid inflation of this error Changing the method of analysis
to fit data will inflate
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MULTIPLE COMPARISONS For 20 independent variables (clinical
endpoints), one significant at random For 20 independent treatment
comparisons, one significant at random
Subgroup analyses can also potentially inflate
Multiple comparison adjustment
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Report all data Scientific experiments generate data Outliers may be observed Delete outlier? Clinical trials generate data A wonder drug cures 9,999 patients
of 10,000 One died – outlier – delete?
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Statistical Analysis Plan (SAP) Pre-specification of analysis Prior to breaking blind Internal agreement within project
team Binding document to communicate
with regulatory authorities Use of LOCF or modeling approach
need to be pre-specified in SAP
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Modeling approaches Assumptions Can be complicated Difficult to explain to end users George Box – “All models are
wrong, some are useful”
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Why LOCF? Or why not? Easy to understand Easy to communicate between
statisticians and clinicians, and between sponsor and regulators
Lots of prior examples Biased point estimate, biased
variance
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Recommendations Understand the disease Understand data to be collected Understand the dropout issues Make use of Phase II results Encourage use of statistical models LOCF may still be considered as
supportive
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