Simple, efficient estimators of treatment effects in randomized trials using generalized linear models to leverage baseline variables.

The International Journal of Biostatistics
Michael Rosenblum, Mark J van der Laan

Abstract

Models, such as logistic regression and Poisson regression models, are often used to estimate treatment effects in randomized trials. These models leverage information in variables collected before randomization, in order to obtain more precise estimates of treatment effects. However, there is the danger that model misspecification will lead to bias. We show that certain easy to compute, model-based estimators are asymptotically unbiased even when the working model used is arbitrarily misspecified. Furthermore, these estimators are locally efficient. As a special case of our main result, we consider a simple Poisson working model containing only main terms; in this case, we prove the maximum likelihood estimate of the coefficient corresponding to the treatment variable is an asymptotically unbiased estimator of the marginal log rate ratio, even when the working model is arbitrarily misspecified. This is the log-linear analog of ANCOVA for linear models. Our results demonstrate one application of targeted maximum likelihood estimation.

Citations

Jan 6, 2010·The International Journal of Biostatistics·Michael Rosenblum, Mark J van der Laan
Nov 26, 2014·Statistics in Medicine·Laura B BalzerUNKNOWN SEARCH Consortium
Apr 16, 2015·Statistics in Medicine·Elizabeth Colantuoni, Michael Rosenblum
Jul 7, 2016·Proceedings of the National Academy of Sciences of the United States of America·Michael J HigginsJasjeet S Sekhon
Jul 21, 2016·Statistics in Medicine·Laura B BalzerUNKNOWN SEARCH Collaboration
Mar 22, 2020·Pharmaceutical Statistics·Patrick DarkenDavid Wright
Nov 20, 2016·Statistics in Medicine·David HajageFlorence Tubach
Apr 19, 2016·Statistics in Medicine·Laura B BalzerUNKNOWN SEARCH Collaboration
Jul 13, 2018·Pharmaceutical Statistics·Jonathan W Bartlett
Jan 27, 2021·International Journal of Environmental Research and Public Health·Katie PotterLaura B Balzer

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