1. 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.

2. 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.

3. Objectives 1.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.

4. Methods Patients: Adult starting ART between January 1 st, 2004 and September 30 th, 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.

5. Patient Pre-therapy Characteristics n=3628 FactorISS 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 2004522 (14) 20051,380 (38) 2006930 (25) 2007796 (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

6. Patient Follow-up Characteristics n=3628 CharacteristicISS Clinic, Mbarara, Uganda Patient observation time, years, median (IQR)1.4 (0.8-2.2) Total observation time, years5503 Number of follow up visit, n, median (IQR)8 (3-12) Number of CD4+ T cell determinations, median (IQR) 2 (1-4) Number of weight determinations5 (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%)

7. 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

8. UnweightedIPCWSBW Stabilized weights Mean=1.001 SD = 0.100 Min=0.464 Max=4.955 Weight=7.47 if LTFU & found 1 if under observation 0 if LTFU & not found Weight estimation CD4, body weight, visit frequency, regimen, transfer request All patients weight=1

9. FactorUnweightedIPCWSBW OR95% CIp-valueOR95% CIp-valueOR95% CIp-value Age, per 10 years1.220.90-1.650.191.230.91-1.660.181.371.06-1.770.02 Male sex1.811.05-3.110.031.781.04-3.070.041.050.58-1.880.88 Weight < 40 kg2.070.87-4.960.101.950.82-4.600.133.661.85-7.270.00 Pretherapy CD4 value <=50ref 51-1000.700.32-1.520.370.720.33-1.560.400.460.20-1.030.06 101-2000.200.07-0.590.000.220.07-0.640.010.140.05-0.450.00 > 2000.220.06-0.760.020.230.07-0.790.020.270.09-0.800.02 Distance from home to clinic, per 10 km0.920.840.050.920.84-1.000.051.000.93-1.080.99 Pre-therapy WHO stage 40.920.35-2.400.860.910.35-2.400.860.760.26-2.250.62 ART start year 2004ref 20050.710.37-1.370.310.720.38-1.390.331.840.99-3.420.05 20060.540.22-1.300.170.580.24-1.410.230.770.31-1.960.59 20070.250.05-1.150.070.290.06-1.350.110.090.02-0.430.00 Adjusted Effect of Sex on Mortality via Three Analytic Approaches N=3628

10. 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.

11. Sex Clinical visit Vital status Loss to follow-up (i.e., censor) Pre-therapy CD4 Time updated CD4 Conditioning on a common effect

12. Sex Clinical visit Vital status Loss to follow-up (i.e., censor) Pre-therapy CD4 Time updated CD4 c Unweighted: IPCW: if vital status has an effect on censor, CAR fails. SBW: outcomes completely ascertained in the weighted population

13. FactorUnweightedIPCWSBW OR95% CIp-valueOR95% CIp-valueOR95% CIp-value Age, per 10 years Male sex Weight < 40 kg Pretherapy CD4 value <=50 51-100 101-200 > 200 Distance from home to clinic, per 10 km Pre-therapy WHO stage 4 ART start year 2004 2005 2006 2007 Adjusted Effect of Sex on Mortality via Three Analytic Approaches N=3628

14. 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.

15. 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