Assessing the Effectiveness of Antiretroviral Adherence Interventions: Using Marginal Structural Models to Replicate the Findings of Randomized Controlled Trials.
Randomized controlled trials of interventions to improve adherence to antiretroviral medications are not always feasible. Marginal Structural Models (MSM) are a statistical methodology that aims to replicate the findings of randomized controlled trials using observational data. Under the assumption of no unmeasured confounders, three MSM estimators are available to estimate the causal effect of an intervention. Two of these estimators, G-computation and Inverse Probability of Treatment Weighted, can be implemented using standard software. G-computation relies on fitting a multivariable regression of adherence on the intervention and confounders. Thus, it is related to the standard multivariable regression approach to estimating causal effects. In contrast, Inverse Probability of Treatment Weighting relies on fitting a multivariable logistic regression of the intervention on confounders. This article reviews the implementation of these methods, the assumptions underlying them, and interpretation of results. Findings are illustrated with a theoretical data example in which Marginal Structural Models are used to estimate the effect of a behavioral intervention on adherence to antiretroviral therapy.
Maya L. Petersen, Yue Wang, Mark J. van der Laan, and David R. Bangsberg. "Assessing the Effectiveness of Antiretroviral Adherence Interventions: Using Marginal Structural Models to Replicate the Findings of Randomized Controlled Trials." JAIDS 43.Supplement 1 (2006): S96-S103.
Available at: http://works.bepress.com/maya_petersen/27