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PubMed · 16022165

Adaptive regression.

Michael A Proschan·Eric Leifer·Qing Liu
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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.

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AlgorithmsData Interpretation, StatisticalPrognosisRandom AllocationResearch DesignJournal Article

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BibTeXRIS

Michael A Proschan, Eric Leifer, Qing Liu. 2005. Adaptive regression.. https://doi.org/10.1081/bip-200062861

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