STATISTICS COLLOQUIUM
THURSDAY, May 1st, 2008 at 4.10pm, MSB
1147 (Colloquium Room)
Refreshments: 3.30pm, MSB 4110 (Statistics
Lounge)
Speaker: Michael Rosenblum (UC
Berkeley)
Title: 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, since 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 paper, 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 regression models, standard regression-based
hypothesis tests (but using robust variance estimators) are guaranteed to have
correct Type I error for large samples, even when the models are incorrectly
specified. To the best of our knowledge, this robustness of such model-based
hypothesis tests to incorrectly specified models was previously unknown for
many of the regression models we consider.
Our results have practical implications for
understanding the reliability of commonly used, model-based tests for analyzing
randomized trials.
