Observational Studies: Matching or Regression? Biol Blood Marrow Transplant 2016 Mar;22(3):557-63
Date
12/30/2015Pubmed ID
26712591Pubmed Central ID
PMC4756459DOI
10.1016/j.bbmt.2015.12.005Scopus ID
2-s2.0-85009461542 (requires institutional sign-in at Scopus site) 80 CitationsAbstract
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.
Author List
Brazauskas R, Logan BRAuthors
Ruta Brazauskas PhD Associate Professor in the Data Science Institute department at Medical College of WisconsinBrent R. Logan PhD Director, Professor in the Data Science Institute department at Medical College of Wisconsin
MESH terms used to index this publication - Major topics in bold
HumansModels, Theoretical
Observational Studies as Topic
