title: Improving precision by adjusting for prognostic baseline variables in randomized trials with binary outcomes, without regression model assumptions.
abstract: In randomized clinical trials with baseline variables that are prognostic for the primary outcome, there is potential to improve precision and reduce sample size by appropriately adjusting for these variables. A major challenge is that there are multiple statistical methods to adjust for baseline variables, but little guidance on which is best to use in a given context. The choice of method can have important consequences. For example, one commonly used method leads to uninterpretable estimates if there is any treatment effect heterogeneity, which would jeopardize the validity of trial conclusions. We give practical guidance on how to avoid this problem, while retaining the advantages of covariate adjustment. This can be achieved by using simple (but less well-known) standardization methods from the recent statistics literature. We discuss these methods and give software in R and Stata implementing them. A data example from a recent stroke trial is used to illustrate these methods.
authors.affiliation: Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, United States. Electronic address: jsteing5@jhu.edu.
authors.forename: Jon Arni
authors.initials: JA
authors.lastname: Steingrimsson
authors.affiliation: Department of Neurology, Brain Injury Outcomes Coordinating Center, Johns Hopkins University, Baltimore, MD 21231, United States. Electronic address: dhanley@jhmi.edu.
authors.forename: Daniel F
authors.initials: DF
authors.lastname: Hanley
authors.affiliation: Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, United States; Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, United States. Electronic address: mrosen@jhu.edu.
authors.forename: Michael
authors.initials: M
authors.lastname: Rosenblum
mesh_terms: D000074042:Cerebral Intraventricular Hemorrhage / Q000188:drug therapy*; D005343:Fibrinolytic Agents / Q000627:therapeutic use*; D006801:Humans; D016015:Logistic Models; D011379:Prognosis; D016032:Randomized Controlled Trials as Topic; D018401:Sample Size; D013223:Statistics as Topic; D020521:Stroke / Q000188:drug therapy*; D010959:Tissue Plasminogen Activator / Q000627:therapeutic use*
chemical_list: D005343:Fibrinolytic Agents; D010959:Tissue Plasminogen Activator
keywords: Covariate adjustment; Post-stratification