title: Causal inference in longitudinal comparative effectiveness studies with repeated measures of a continuous intermediate variable.
abstract: We propose a principal stratification approach to assess causal effects in nonrandomized longitudinal comparative effectiveness studies with a binary endpoint outcome and repeated measures of a continuous intermediate variable. Our method is an extension of the principal stratification approach originally proposed for the longitudinal randomized study "Prevention of Suicide in Primary Care Elderly: Collaborative Trial" to assess the treatment effect on the continuous Hamilton depression score adjusting for the heterogeneity of repeatedly measured binary compliance status. Our motivation for this work comes from a comparison of the effect of two glucose-lowering medications on a clinical cohort of patients with type 2 diabetes. Here, we consider a causal inference problem assessing how well the two medications work relative to one another on two binary endpoint outcomes: cardiovascular disease-related hospitalization and all-cause mortality. Clinically, these glucose-lowering medications can have differential effects on the intermediate outcome, glucose level over time. Ultimately, we want to compare medication effects on the endpoint outcomes among individuals in the same glucose trajectory stratum while accounting for the heterogeneity in baseline covariates (i.e., to obtain 'principal effects' on the endpoint outcomes). The proposed method involves a three-step model estimation procedure. Step 1 identifies principal strata associated with the intermediate variable using hybrid growth mixture modeling analyses. Step 2 obtains the stratum membership using the pseudoclass technique and derives propensity scores for treatment assignment. Step 3 obtains the stratum-specific treatment effect on the endpoint outcome weighted by inverse propensity probabilities derived from Step 2.
authors.affiliation: Department of Epidemiology and Biostatistics, University of Texas Health Science Center, San Antonio, TX 78229, U.S.A.
authors.forename: Chen-Pin
authors.initials: CP
authors.lastname: Wang
authors.forename: Booil
authors.initials: B
authors.lastname: Jo
authors.forename: C Hendricks
authors.initials: CH
authors.lastname: Brown
mesh_terms: D000368:Aged; D002318:Cardiovascular Diseases / Q000401:mortality; D015984:Causality; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D003920:Diabetes Mellitus / Q000188:drug therapy; D006801:Humans; D007004:Hypoglycemic Agents; D008137:Longitudinal Studies; D015233:Models, Statistical; D012044:Regression Analysis; D016896:Treatment Outcome
chemical_list: D007004:Hypoglycemic Agents
keywords: causal inference; comparative effectiveness studies; growth mixture model; principal stratification; propensity score