A structural approach to understanding the effect of loss to follow-up on epidemiologic analyses of HIV-infected patients on antiretroviral

therapy in Africa

Geng E.H, Bangsberg D.R., Glidden, D.V., Emenyonu, N., Musinguzi N., Neilands, T.B., Metcalfe, J., Christopoulos, K.A., Kigozi, I, Muyindike, W., Deeks, S.G., Bwana, M.B., Yiannoutsos, C.T., Martin J.N., Petersen M.L.

Background• High loss to follow-up in cohorts of HIV-infected patients on

ART in Africa may lead to bias in epidemiologic analyses.

• “Traditional” unweighted regression, inverse probability of censor weights (IPCW) and sample based weights – derived from tracking a numerically small but representative sample of patients lost to follow-up in the community – address the potential for bias via different approaches.

• To date, no formal comparison of these approaches in a single cohort of HIV-infected patients on ART from Africa has been conducted.

Objectives1. Evaluate sex as an independent predictor of survival via these

three approaches.– Sex is an example of an easily and commonly measured characteristic.

– In order to understand the predictive value of sex at ART start, we avoided adjusting for potential time-updated mediators of the effect.

2. Use directed acyclic graphs (DAG’s) to represent contextual knowledge about causal relationships in this patient population.

3. Interpret differences in the findings of each analytic approach in light of these causal assumptions.

Methods• Patients: Adult starting ART between January 1st , 2004 and September

30th, 2007 at Immune Suppression Syndrome Clinic in Mbarara, Uganda.• Measurements: Socio-demographic and clinical characteristics obtained

during the course of routine care.

• Analyses: Pooled logistic regression– Unweighted– Inverse probability of censor weighted– Sample weighted

• All pre-therapy predictors were used in each multivariable analysis to facilitate cross-analysis comparison

• Discretized time into month intervals and handled as a restricted cubic spline, obtained standard errors with the clustered sandwich estimator.

Patient Pre-therapy Characteristics n=3628

Factor ISS Clinic, Mbarara, Uganda

Age, years, (median, IQR)* 35 (30-41)

Male sex, n(%) 1408 (39)

Pre-therapy CD4, cells/cc3, (median, IQR)† 117 (48-197)

Weight, kg, (median, IQR) ‡ 54 (47-60)

WHO stage 4, n (%) € 745 (22)

Start year

2004 522 (14)

2005 1,380 (38)

2006 930 (25)

2007 796 (21)

Distance from clinic to residence ¥ 35.4 (8.8-64.7)

* missing in 31, †missing in 1036; ‡missing in 227, € missing in 1403, ¥missing in 740

Patient Follow-up Characteristics n=3628

Characteristic ISS Clinic, Mbarara, UgandaPatient observation time, years, median (IQR) 1.4 (0.8-2.2)Total observation time, years 5503Number of follow up visit, n, median (IQR) 8 (3-12)Number of CD4+ T cell determinations, median

(IQR) 2 (1-4)

Number of weight determinations 5 (3-7)Number of regimen switches, n (%) 154 (4.2)Transfer requests, n (%) 192 (5.3)Interval between visits, days, median (IQR) 32 (28-62)Number of deaths known to clinic, n (%) 57 (1.6)

Number of patients lost to follow-up (defined as six months of absence), n (%) 829 (23%)

All Patients, n=3628

Patients who Continue in Care

829 patients LTFU

128 (15%) tracked

111 (87%) vital status ascertained

Sample based weight = 829

111

Sample based weight

Unweighted IPCW SBW

Stabilized weightsMean=1.001SD = 0.100Min=0.464Max=4.955

Weight=7.47 if LTFU & found 1 if under observation0 if LTFU & not found

Weight estimationCD4, body weight, visit

frequency, regimen, transfer request

All patients weight=1

Factor Unweighted IPCW SBW OR 95% CI p-value OR 95% CI p-value OR 95% CI p-valueAge, per 10 years 1.22 0.90-1.65 0.19 1.23 0.91- 1.66 0.18 1.37 1.06- 1.77 0.02Male sex 1.81 1.05-3.11 0.03 1.78 1.04- 3.07 0.04 1.05 0.58- 1.88 0.88Weight < 40 kg 2.07 0.87-4.96 0.10 1.95 0.82- 4.60 0.13 3.66 1.85- 7.27 0.00Pretherapy CD4 value

<=50 ref ref ref 51-100 0.70 0.32-1.52 0.37 0.72 0.33- 1.56 0.40 0.46 0.20- 1.03 0.06101-200 0.20 0.07-0.59 0.00 0.22 0.07- 0.64 0.01 0.14 0.05- 0.45 0.00> 200 0.22 0.06-0.76 0.02 0.23 0.07- 0.79 0.02 0.27 0.09- 0.80 0.02

Distance from home to clinic, per 10 km 0.92 0.84-1.00 0.05 0.92 0.84- 1.00 0.05 1.00 0.93- 1.08 0.99Pre-therapy WHO stage 4 0.92 0.35-2.40 0.86 0.91 0.35- 2.40 0.86 0.76 0.26- 2.25 0.62ART start year

2004 ref ref ref 2005 0.71 0.37-1.37 0.31 0.72 0.38- 1.39 0.33 1.84 0.99- 3.42 0.052006 0.54 0.22-1.30 0.17 0.58 0.24- 1.41 0.23 0.77 0.31- 1.96 0.59

2007 0.25 0.05-1.15 0.07 0.29 0.06- 1.35 0.11 0.09 0.02- 0.43 0.00

Adjusted Effect of Sex on Mortality via Three Analytic ApproachesN=3628

Sex

Clinical visit

Vital status

Loss to follow-up(i.e., censor)

Pre-therapy CD4

Time updated CD4

1. Sex has an effect on LTFU because men tend to move, travel for work, etc. But deaths among men are more likely to be reported.2. Deaths effect LTFU because no systematic mechanism –such as a death registry – exists to capture deaths.

3. Clinic visits effect mortality and LTFU because (a) missing visits at ISS clinic leads to deterioration of health unless the patient seeks care elsewhere and (b) patients who stop visits become censored unless they die and the death is reported.

Sex

Clinical visit

Vital status

Loss to follow-up(i.e., censor)

Pre-therapy CD4

Time updated CD4

Conditioning on a common effect

Sex

Clinical visit

Vital status

Loss to follow-up(i.e., censor)

Pre-therapy CD4

Time updatedCD4

c

Unweighted: IPCW: if vital status has an

effect on censor, CAR fails.

SBW: outcomes completely

ascertained in the weighted population

Factor Unweighted IPCW SBW OR 95% CI p-value OR 95% CI p-value OR 95% CI p-valueAge, per 10 yearsMale sexWeight < 40 kgPretherapy CD4 value

<=5051-100101-200> 200

Distance from home to clinic, per 10 kmPre-therapy WHO stage 4ART start year

200420052006

2007

Adjusted Effect of Sex on Mortality via Three Analytic ApproachesN=3628

Conclusions and Implications• Structural biases may be present in data with high losses to

follow- up that cannot be easily removed in traditional and IPCW analyses.

• The basis of the structural bias proposed here – death leads to loss to follow-up (i.e., censor) – may be common in resource limited settings where knowledge of deaths relies on informal reporting mechanisms.

• The information in time updated covariates that can be used to adjust for informative censor through routine care from African HIV cohorts is limited.

• A sampling based approach can manage the effects of loss to follow-up in this setting.

Acknowledgements!• UCSF

– Jeffrey Martin– David Glidden– Eric Vittinghoff– Steven Deeks– Diane Havlir

• Harvard– David Bangsberg

• Indiana University– Constantin Yiannoutsos– Kara Wools Kaloustian– Paula Braitstein

• UC Berkeley– Maya Petersen

• MUST– Hassan Baryahikwa – “The Ascertainer”– Nicholas Musinguzi– Nneka Emenyonu– Mwebesa Bwana– Winnie Muyindike

• NIH• Rosemary McKaig• Carlie Williams• Melanie Bacon