Linking Clinical Cohorts and Basic/Clinical Science in Developing Countries

1. Linking Clinical Cohorts and Basic/Clinical Science in Developing Countries Challenges and pitfalls Eugène Kroon, MD -SEARCH, Thai Red Cross AIDS Research Center -USAMC-AFRIMS Bangkok, Thailand eugene.kroon@afrims.org

2. Cohorts: challenges and pitfalls Prospective cohort studies are considered to yield the most reliable results in observational epidemiology. They enable a wide range of exposure-disease associations to be studied. Many excellent examples in the field of HIV medicine: treatment outcomes in different populations, ART safety, longer term disease complications, etc. but: Cohort studies may be expensive to conduct, are sensitive to attrition and often take a long follow-up time to generate useful data. 

3. Cohorts: challenges and pitfalls In addition: Randomized controlled trials (RTC) are generally considered a superior methodology in the hierarchy of evidence in at least therapy. RTClimit the potential for any biases by randomly assigning one patient pool to an intervention and another patient pool to non-intervention or placebo. This minimizes the chance that the incidence of (unknown) confounding variables will differ between the two groups.

4. Cohorts: challenges and pitfalls Today touching upon: Internal versus external validity Attrition and missing data Representativeness and generalizability Cohort effect and the need to ‘refresh cohorts’ Reality check when setting up a prospective cohort: data collection, timing and looking in the right population

5. Internal versus external validity Associations found in cohorts may hold up in cohorts, but may not be replicable outside the cohort setting due to interference of many other factors, thatin a clinical trial can be randomized. Example: the complex issue of nevirapinevs.efavirenz in terms of virological failure

6. Internal versus external validity In the early 2000s four articles were published by multiple, reputable and sizeable cohorts, both in ART naïve and ART experienced patients, all suggesting a greater or earlier risk of virological failure on nevirapine (NVP) vs. efavirenz (EFV) Keiser et al.HIV Clin Trials. 2002;3:296-303. Shorter time to treatment failure on NVP (307 days vs. 589 days; p <.001). Cozzi-Lepriet al.JInfect Dis. 2002;185:1062-1069.  Adjusted relative hazard of virological failure on NVP 2.08 (95% CI, 1.37–3.15; P=0.0006). Matthews et al .AIDS. 2002;16:53-61. Relative hazard for achieving undetectable viral load on NVP 0.77 (95% CI, 0.61-0.96, P=0.02) Phillips et al. AIDS. 2001;15:2385-2395. Relative hazard of virological failure on EFV 0.57 (95% CI, 0.47-0.69;P < 0.0001).

7. Internal versus external validity Two of those four publications mentioned the possibility of biasexplicitly and state that replication of study findings in randomized trials and other cohort studies is required. RTC: 2004, the Lancet (vol. 363, page 1253-1263): Comparison of first-line antiretroviral therapy with regimens including nevirapine, efavirenz, or both drugs, plus stavudineand lamivudine: a randomised open-label trial, the 2NN Study. F van Leth, P Phanuphak, K Ruxrungtham, et al.

8. Internal versus external validity 2NN study: multicentre, open-label, randomisedtrial 1216 antiretroviral-therapy-naive patients 48 weeks duration nevirapine400 mg once daily nevirapine200 mg twice daily efavirenz600 mg once daily nevirapine(400 mg) and efavirenz(800 mg) once daily all arms plus stavudine and lamivudine

9. Internal versus external validity 2NN at 48 weeks: Treatment failure difference NVP twice daily and EFV 5·9% (43·7% NVP vs 37·8% EFV; 95% CI -0·9 to 12·8) Differences in plasma HIV-1 RNA below 50 copies per mL at week 48 not significant between any of the arms (p=0·193)

10. Internal versus external validity The authors state in their discussion: “The disparities between results from cohort studies and the 2NN Study might be partly explained by differences in study design. Since there is no randomisation in cohort studies, selection bias could have been introduced if the choice between nevirapine and efavirenz were influenced by patients and physicians.……Although these biases can be kept to a minimum by the use of multivariate models, the possibility of residual confounding in cohort studies remains.” However, Dr Andrew Carr noted in his commentary: “2NN found that nevirapine twice daily was not equivalent to efavirenz at week 48.” as non-inferiority had not been shown.

11. Internal versus external validity The story continues: The HIV-CAUSAL Collaboration. AIDS 2012;26:1691-1705. The effect of efavirenz versus nevirapine-containing regimens on immunologic, virologic and clinical outcomes in a prospective observational study Results: A total of 15 ,336 individuals initiated an efavirenzregimen and 8129 individuals initiated a nevirapineregimen. Relative hazard for virological failure on NVP 1.5 (95% CI, 1.3–1.8). (no reference to 2NN study)

12. Attrition and missing data Cohorts usually reflect real life field situations with, for example, no transport covered for subjects or staff salary supplementation. Irrespective of these factors, attrition is a reality. This can be especially troubling in cohorts looking at clinical outcomes and/or death. Methods have been developed to try and account for this incompleteness, including modeling approaches that project missing data.  These approaches can be validated, but they do depend on the robustness of that model. As attrition is a reality, such methods are necessary to put to use the data we do gather. ‘Higer level’ expert input is needed and ongoing research into the assumptions used in the modeling.

13. Attrition and missing data Geng et al. Am J Epidemiol. 2012;175(10):1080–1087. ‘A Causal Framework for Understanding the Effect of Losses to Follow-up on Epidemiologic Analyses in Clinic-based Cohorts: The Case of HIV-infected Patients on Antiretroviral Therapy in Africa’ The authors evaluated adult patients starting ART at an HIV/AIDS clinic in Uganda, where 29% of patients were lost to follow-up after 2 years: ‘In a clinic-based cohort in Africa, unweightedand inverse probability censored weighting approaches which rely on the ‘‘missing at random’’ assumption (=assuming that the fact that data are missing is unrelated to the actual values of the missing data)-yielded biased estimates. A sampling-based approach (using supplemental data from a sample of lost patients subsequently tracked in the community)can in general strengthen epidemiologic analyses conducted in many clinic-based cohorts.’

14. Representativeness and generalizability Unless a cohort is drawn as a random sample, descriptive findings and bi-variateand multivariate associations cannot be generalized. Sometimes representativeness can matter: E.g. cohort studies into HIV incidence in preparation for HIV vaccine studies (studies RV121, RV126, and RV217) And sometimes it may not matter…

15. TAHOD - IeDEA • 2410 patients, • 12 countries • Median CD4 • 170cells/mm3before 2007 • to • 136cells/mm3 after 2009 ‘The data reflect trends in leading clinical HIV centers with the highest level of resources for HIV testing, care, treatment, lab monitoring in their countries.  We take this as in indication that we are not doing well enough in the places where HIV is concentrated (urban centers like Bangkok, Phnom Penh and Kuala Lumpur).’ Kiertiburanakul S, CROI 2013, Abstract 1089.

16. Cohort effect and the need to ‘refresh cohorts’ This refers to the dwindling incidence of disease because of declining probability of acquisition. Example: A long existing urban MSM cohort may have low HIV incidence (say 1%) with several hundreds of men left in the cohort, while aVCT clinic in the same are reports a much higher incidence (for example 7 per 100 PY) in the same population. This means it is time to refresh the cohort.

17. Reality check when setting up a prospective cohort: data collection, timing and looking in the right population Collect only the data needed Thailand Progress Adult Study (HIV disease progression) – 3 sites following 6000+ pts yearly to evaluate treatment outcome and morbidities.  First 3 years too many details were collected (demographics, social situation, symptoms, AEs) and most data were missing. Data collection was streamlined and data collection limited to ensure that endpoints are being collected. Tools: staff training, data manual and electronic transfer of lab data.

18. Reality check when setting up a prospective cohort: data collection, timing and looking in the right population • Timing: • HIV incidence studies for vaccine studies may be affected by change in genotype (IDU, Bangkok 1998) or change in incidence. The latter may be affected by foreseeable circumstances, such as PrEP. • TNT 003 at TRC is a cohort of HIV positives and HIV negatives aged > 30 yrs to evaluate long term morbidities of HIV and ARV (bone, liver, kidney, brain). After two years and 500+ patients, very few events are seen in both groups. The protocol will be modified to follow these pts every 5 years.

19. Reality check when setting up a prospective cohort: data collection, timing and looking in the right population Looking in the right population: Study RV217; HIV incidence in MARP in Pattaya, Thailand. Dropped FSWs from protocol target population after finding the following incidence (M. Robb, AIDS Vaccine, August 2012, Boston): TGW 6.3% N = 146 MSM 5.85 % N = 181 FSW 0.94% N = 141 Study RV254/SEARCH010 has tested samples from nearly 70,000 subjects to identify 136 AHI. Majority are in MSM. If costs become a barrier, one could consider looking in MSM only.

20. Acknowledgements Frits van Griensven HIV-NAT, Thai Red Cross AIDS Research Center Steve Kerr HIV-NAT, Thai Red Cross AIDS Research Center Kirby Institute UNSW Annette Sohn amfAR TREAT Asia Jintanat Ananworanich SEARCH, Thai Red Cross AIDS Research Center HIV-NAT, Thai Red Cross AIDS Research Center Mike Benenson Retired USAMC-AFRIMS