Principal stratification with predictors of compliance for randomized trials with 2 active treatments.
In behavioral medicine trials, such as smoking cessation trials, 2 or more active treatments are often compared. Noncompliance by some subjects with their assigned treatment poses a challenge to the data analyst. The principal stratification framework permits inference about causal effects among subpopulations characterized by potential compliance. However, in the absence of prior information, there are 2 significant limitations: (1) the causal effects cannot be point identified for some strata and (2) individuals in the subpopulations (strata) cannot be identified. We propose to use additional information-compliance-predictive covariates-to help identify the causal effects and to help describe characteristics of the subpopulations. The probability of membership in each principal stratum is modeled as a function of these covariates. The model is constructed using marginal compliance models (which are identified) and a sensitivity parameter that captures the association between the 2 marginal distributions. We illustrate our methods in both a simulation study and an analysis of data from a smoking cessation trial.
Center for Health Research, Geisinger Health System, Danville, PA, USA. jaroy@geisinger.edu
Jason
J
Roy
Joseph W
JW
Hogan
Bess H
BH
Marcus
D001699:Biometry / Q000379:methods; D015984:Causality; D005500:Follow-Up Studies; D006801:Humans; D010349:Patient Compliance / Q000706:statistics & numerical data*; D011336:Probability; D016032:Randomized Controlled Trials as Topic / Q000379:methods* / Q000706:statistics & numerical data; D012044:Regression Analysis; D012680:Sensitivity and Specificity; D016540:Smoking Cessation / Q000706:statistics & numerical data; D016896:Treatment Outcome