The potential for bias in principal causal effect estimation when treatment received depends on a key covariate

Details The potential for bias in principal causal effect estimation when treatment received depends on a key covariate The potential for bias in principal causal effect estimation when treatment received depends on a key covariate

2011-11-07

arxiv.org/abs/1111.1509v1

Annals of Applied Statistics 2011, Vol. 5, No. 3, 1876-1892

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Published in at http://dx.doi.org/10.1214/11-AOAS477 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Ins

Scientific paper

10.1214/11-AOAS477

Motivated by a potential-outcomes perspective, the idea of principal stratification has been widely recognized for its relevance in settings susceptible to posttreatment selection bias such as randomized clinical trials where treatment received can differ from treatment assigned. In one such setting, we address subtleties involved in inference for causal effects when using a key covariate to predict membership in latent principal strata. We show that when treatment received can differ from treatment assigned in both study arms, incorporating a stratum-predictive covariate can make estimates of the "complier average causal effect" (CACE) derive from observations in the two treatment arms with different covariate distributions. Adopting a Bayesian perspective and using Markov chain Monte Carlo for computation, we develop posterior checks that characterize the extent to which incorporating the pretreatment covariate endangers estimation of the CACE. We apply the method to analyze a clinical trial comparing two treatments for jaw fractures in which the study protocol allowed surgeons to overrule both possible randomized treatment assignments based on their clinical judgment and the data contained a key covariate (injury severity) predictive of treatment received.

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