Using regression models to analyze randomized trials: asymptotically valid hypothesis tests despite incorrectly specified models.

ABSTRACT: Regression models are often used to test for cause-effect relationships from data collected in randomized trials or experiments. This practice has deservedly come under heavy scrutiny, because commonly used models such as linear and logistic regression will often not capture the actual relationships between variables, and incorrectly specified models potentially lead to incorrect conclusions. In this article, we focus on hypothesis tests of whether the treatment given in a randomized trial has any effect on the mean of the primary outcome, within strata of baseline variables such as age, sex, and health status. Our primary concern is ensuring that such hypothesis tests have correct type I error for large samples. Our main result is that for a surprisingly large class of commonly used regre

SUBMITTER: Rosenblum M 

PROVIDER: S-EPMC2748134 | biostudies-literature | 2009 Sep

REPOSITORIES: biostudies-literature