Using Regression Models to Analyze Randomized Trials: Asymptotically Valid Hypothesis Tests Despite Incorrectly Specified Models

DOI10.1111/j.1541-0420.2008.01177.xzbMath1172.62058OpenAlexW2171755107WikidataQ37356269 ScholiaQ37356269MaRDI QIDQ3183243

Michael G. Rosenblum

Publication date: 19 October 2009

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://biostats.bepress.com/cgi/viewcontent.cgi?article=1222&context=ucbbiostat

zbMATH Keywords

regressioncausal effectsgeneralized linear modelmisspecified modelrobust methodsrandomized trial

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Applications of statistics to biology and medical sciences; meta analysis (62P10) Generalized linear models (logistic models) (62J12) Testing in survival analysis and censored data (62N03)

Related Items (7)

Estimating Malaria Vaccine Efficacy in the Absence of a Gold Standard Case Definition: Mendelian Factorial Design ⋮ A Broad Symmetry Criterion for Nonparametric Validity of Parametrically Based Tests in Randomized Trials ⋮ Randomization does not justify logistic regression ⋮ A Note on Using Regression Models to Analyze Randomized Trials: Asymptotically Valid Hypothesis Tests Despite Incorrectly Specified Models ⋮ Rejoinder to “A Note on Using Regression Models to Analyze Randomized Trials: Asymptotically Valid Hypothesis Tests Despite Incorrectly Specified Models” ⋮ \(Q\)- and \(A\)-learning methods for estimating optimal dynamic treatment regimes ⋮ On model selection and model misspecification in causal inference

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