title: Observational Studies: Matching or Regression?
abstract: In observational studies with an aim of assessing treatment effect or comparing groups of patients, several approaches could be used. Often, baseline characteristics of patients may be imbalanced between groups, and adjustments are needed to account for this. It can be accomplished either via appropriate regression modeling or, alternatively, by conducting a matched pairs study. The latter is often chosen because it makes groups appear to be comparable. In this article we considered these 2 options in terms of their ability to detect a treatment effect in time-to-event studies. Our investigation shows that a Cox regression model applied to the entire cohort is often a more powerful tool in detecting treatment effect as compared with a matched study. Real data from a hematopoietic cell transplantation study is used as an example.
authors.affiliation: Division of Biostatistics and Center for International Blood and Marrow Transplant Research, Department of Medicine, Medical College of Wisconsin, Milwaukee, Wisconsin. Electronic address: ruta@mcw.edu.
authors.forename: Ruta
authors.initials: R
authors.lastname: Brazauskas
authors.affiliation: Division of Biostatistics and Center for International Blood and Marrow Transplant Research, Department of Medicine, Medical College of Wisconsin, Milwaukee, Wisconsin.
authors.forename: Brent R
authors.initials: BR
authors.lastname: Logan
mesh_terms: D006801:Humans; D008962:Models, Theoretical; D064887:Observational Studies as Topic
keywords: Cox regression model; Hematopoietic stem cell transplantation; Matched pairs study; Observational studies