Andrew Anglemyer*, Amy Sturt and Yvonne Maldonado Pages 151 - 157 ( 7 )
Background: Instrumental variable (IV) analyses are a common causal inference technique used in the absence of randomized data. Combination Antiretroviral Therapy (cART) was first introduced in 1996 and calendar periods have been used as a proxy for cART use. However, cART use misclassification can bias IV analyses.
Objective: We aim to highlight the differences in the effects of antiretroviral therapy on clinical outcomes between the applications of traditional and adapted IV analysis techniques.
Methods: This study includes children with perinatal human immunodeficiency virus (HIV-1) infection followed from 1988 to 2009. We describe an application of traditional and adapted IV analysis techniques. Noncompliance adjustments were applied to correct the misclassification of cART-use. Weighting the inverse probability of calendar era, the selected covariates were performed to control for variables that may be related to both the IV and outcome.
Results: During 48,380 person-days, 78 HIV-positive children progressed to an initial stage-3- defining diagnosis or death. The Intention to Treat (ITT) rate ratio (RR) of stage-3-defining diagnosis or death comparing the pre-cART and cART eras was estimated at 2·67 (95% confidence interval (CI): 1·.47, 4·84). The IV estimator was used to adjust for cART use misclassification, yielding an IV RR of 5·42 (95% CI: 2·99, 9·83). Weighting analyses did not markedly alter the results.
Conclusion: cART use decreased progression to stage-3-defining diagnosis or death. The use of noncompliance adjustments for cART misclassification in IV analyses may provide more robust evidence of cART's effectiveness than traditional ITT analysis.
HIV infection, mortality, pediatrics, antiretroviral drugs, intention to treat analysis, instrumental variables analysis.
Operations Research Department, Naval Postgraduate School, Monterey, CA, Pediatrics-Infectious Diseases, Stanford University, Stanford, CA, Pediatrics-Infectious Diseases, Stanford University, Stanford, CA