title: Should we adjust for covariates in nonlinear regression analyses of randomized trials?
abstract: The analyses of the primary objectives of randomized clinical trials often are not adjusted for covariates, except possibly for stratification variables. For analyses with linear models, adjustment is a precision issue only. We review the literature regarding logistic and Cox (proportional hazards) regression models. For these nonlinear analyses, omitting covariates from the analysis of randomized trials leads to a loss of efficiency as well as a change in the treatment effect being estimated. We recommend that the primary analyses adjust for important prognostic covariates in order to come as close as possible to the clinically most relevant subject-specific measure of treatment effect. Additional benefits would be an increase in efficiency of tests for no treatment effect and improved external validity. The latter is particularly relevant to meta-analyses.
authors.affiliation: Division of Clinical Pharmacology, Thomas Jefferson University, Philadelphia, Pennsylvania 19107, USA.
authors.forename: W W
authors.initials: WW
authors.lastname: Hauck
authors.forename: S
authors.initials: S
authors.lastname: Anderson
authors.forename: S M
authors.initials: SM
authors.lastname: Marcus
mesh_terms: D015982:Bias; D001943:Breast Neoplasms / Q000601:surgery; D005260:Female; D006801:Humans; D016015:Logistic Models; D015201:Meta-Analysis as Topic; D008722:Methods; D015999:Multivariate Analysis; D016016:Proportional Hazards Models; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data*