Inverse probability weighting for covariate adjustment in randomized studies

Changyu Shen, Xiaochun Li, Lingling Li

Research output: Contribution to journalArticle

12 Scopus citations

Abstract

Covariate adjustment in randomized clinical trials has the potential benefit of precision gain. It also has the potential pitfall of reduced objectivity as it opens the possibility of selecting a 'favorable' model that yields strong treatment benefit estimate. Although there is a large volume of statistical literature targeting on the first aspect, realistic solutions to enforce objective inference and improve precision are rare. As a typical randomized trial needs to accommodate many implementation issues beyond statistical considerations, maintaining the objectivity is at least as important as precision gain if not more, particularly from the perspective of the regulatory agencies. In this article, we propose a two-stage estimation procedure based on inverse probability weighting to achieve better precision without compromising objectivity. The procedure is designed in a way such that the covariate adjustment is performed before seeing the outcome, effectively reducing the possibility of selecting a 'favorable' model that yields a strong intervention effect. Both theoretical and numerical properties of the estimation procedure are presented. Application of the proposed method to a real data example is presented.

Original languageEnglish (US)
Pages (from-to)555-568
Number of pages14
JournalStatistics in Medicine
Volume33
Issue number4
DOIs
StatePublished - Feb 20 2014

Keywords

  • Clinical trials
  • Covariate adjustment
  • Efficiency
  • Inverse probability weighting
  • Objectivity

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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