A class of semiparametric tests of treatment effect robust to confounder measurement error.

ABSTRACT: When assessing the presence of an exposure causal effect on a given outcome, measurement error of a confounder can inflate the type I error rate of a treatment effect in even the simplest of settings. In this paper, we develop a large class of semiparametric test statistics of an exposure causal effect, which are completely robust to additive unbiased measurement error of a subset of confounders. A unique and appealing feature of our proposed methodology is that it requires no external information such as validation data or replicates of error-prone confounders. We present a doubly robust form of this test that requires the exposure mean model to be linear in the mismeasured confounders, and only one of two models involving error-free confounders to be correctly specified for the resulting

SUBMITTER: Miles CH 

PROVIDER: S-EPMC10712939 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature