A Data Augmentation Method for Estimating the Causal Effect of Adherence to Treatment Regimens Targeting Control of an Intermediate Measure

摘要

A dynamic treatment regimen is a rule or set of rules which define how a subject's treatment at repeated visits depends on their evolving history of time-dependent covariates. In this manuscript we focus on regimens that are characterized by intermediate target ranges [b1, b2] which are used clinically to modify treatment such that longitudinal measures of the intermediate stay within a tolerance defined by [b1, b2]. For observational data, survival under a particular regimen can be consistently estimated by artificially censoring subjects when they become nonadherent to the regimen and then weighting subjects by the inverse probability of remaining un-censored. In many settings subjects are not identified at baseline as individuals who are following a pre-specified regimen. In addition, it may be possible for subjects to be adherent to multiple regimens at the same time. In order to compare alternative regimens we present a data augmentation methodology in which regimen membership is stochastically imputed multiple times and parameter estimates are aggregated to provide a final point estimate of a causal hazard ratio, and for which sandwich variance estimation methods provide consistent inference. The method is illustrated through simulation results as well as a preliminary analysis comparing epoetin therapy regimens with different target hemoglobin ranges in a cohort of hemodialysis subjects from the United States Renal Data System.