Structural Models to Bridge Observational and Randomized Evidence: HAART and AIDS

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DESCRIPTION (provided by applicant): The proposed research addresses the paramount and prevalent scientific problem of estimating causal effects of treatments or exposures from observational (i.e., nonrandomized) data. The overall goal of the proposed research is to use multiple preexisting NIAID-funded data sources to develop a novel analytic method to correct age-period observational analyses for information bias and thereby provide a refined estimate of the average causal effect of highly active antiretroviral therapy (HAART) on time to AIDS or death. The effect of HAART on time to AIDS or death is an exemplar case of estimating causal effects from disparate published results in different study designs. Specifically, results from the observational Multicenter AIDS Cohort Study differ from the AIDS Clinical Trial Group - 320 randomized clinical trial results. The former study was subject to bias due to non-trival misclassification, while the latter study was subject to bias due to non-trival noncompliance. Calendar period and randomization will be used as instrumental variables and analytic methods (i.e., structural models) will be applied and adapted to estimate the average causal effect of HAART on time to AIDS or death. Adapting these methods to the problem of misclassification in the observational cohort study setting represents a novel application of structural nested models. The specific aims of this research are to: (1) develop a structural nested model approach to estimate the misclassification-corrected average causal effect of HAART versus combination therapy on time to AIDS or death using the Multicenter AIDS Cohort Study observational data, (2) use existing structural model approaches to estimate the compliance-corrected average causal effect of HAART versus combination therapy on time to AIDS or death using the AIDS Clinical Trials Group-320 randomized trial data, and (3) compare the estimated hazard ratios resulting from aims 1 and 2 and conduct sensitivity analyses and Monte Carlo simulations that will evaluate the impact of modeling assumptions and assist in determining the cause for the observed discrepancy in published results. The proposed research is relevant and highly significant for biomedical science because it (a) will provide a novel method to refine existing age-period analyses to yield estimates of the average causal effect of a treatment or exposure, (b) capitalizes on a valuable discrepancy between randomized and observational evidence to illuminate connections between scientific approaches, and (c) will provide necessary inputs for HIV cost-effectiveness and healthcare planning analyses. The proposed research is timely in light of recent discrepancies between observational and randomized evidence (e.g., postmenopausal hormone treatment and cardiovascular disease). The proposed research has significance beyond the HIV/AIDS community, as results may be used as a framework in other biomedical fields suffering from a fracture between observational and randomized evidence. The proposed research is relevant and highly significant for biomedical science because it (a) will provide a novel method to refine existing age-period analyses to yield estimates of the average causal effect of a treatment or exposure, (b) capitalizes on a valuable discrepancy between randomized and observational evidence to illuminate connections between scientific approaches, and (c) will provide necessary inputs for HIV cost-effectiveness and healthcare planning analyses. The proposed research is timely in light of recent discrepancies between observational and randomized evidence (e.g., postmenopausal hormone treatment and cardiovascular disease). The proposed research has significance beyond the HIV/AIDS community, as results may be used as a framework in other biomedical fields suffering from a fracture between observational and randomized evidence.