title: Adaptive regression.
abstract: Adjustment for prognostic covariates is recommended in clinical trials because relative to a t-test, it improves precision and adjusts for treatment imbalances caused by an "unlucky" randomization. But, inclusion of too many covariates can be counterproductive. In the quest to strike a balance between inclusion of all important variables and not going overboard, people have proposed methods such as stepwise regression, whereby the decision to include a covariate depends on post-randomization data. Covariate inclusion decisions are typically based on either the strength of its correlation with the outcome or the degree of treatment imbalance. Are these methods valid? Is there a valid way to analyze such data? These are some of the questions we address.
authors: National Heart, Lung, and Blood Institute, Bethesda, Maryland, USA. ProschaM@mail.nih.gov
authors: Michael A
authors: MA
authors: Proschan
authors: Eric
authors: E
authors: Leifer
authors: Qing
authors: Q
authors: Liu
mesh_terms: D000465:Algorithms; D003627:Data Interpretation, Statistical; D011379:Prognosis; D011897:Random Allocation; D012107:Research Design