Reporting and Guidelines in Propensity Score Analysis: A Systematic Review of Cancer and Cancer Surgical Studies

J Natl Cancer Inst. 2017 Aug 1;109(8):djw323. doi: 10.1093/jnci/djw323.


Background: : Propensity score (PS) analysis is increasingly being used in observational studies, especially in some cancer studies where random assignment is not feasible. This systematic review evaluates the use and reporting quality of PS analysis in oncology studies.

Methods: : We searched PubMed to identify the use of PS methods in cancer studies (CS) and cancer surgical studies (CSS) in major medical, cancer, and surgical journals over time and critically evaluated 33 CS published in top medical and cancer journals in 2014 and 2015 and 306 CSS published up to November 26, 2015, without earlier date limits. The quality of reporting in PS analysis was evaluated. It was also compared over time and among journals with differing impact factors. All statistical tests were two-sided.

Results: More than 50% of the publications with PS analysis from the past decade occurred within the past two years. Of the studies critically evaluated, a considerable proportion did not clearly provide the variables used to estimate PS (CS 12.1%, CSS 8.8%), incorrectly included non baseline variables (CS 3.4%, CSS 9.3%), neglected the comparison of baseline characteristics (CS 21.9%, CSS 15.6%), or did not report the matching algorithm utilized (CS 19.0%, CSS 36.1%). In CSS, the reporting of the matching algorithm improved in 2014 and 2015 ( P = .04), and the reporting of variables used to estimate PS was better in top surgery journals ( P = .008). However, there were no statistically significant differences for the inclusion of non baseline variables and reporting of comparability of baseline characteristics.

Conclusions: The use of PS in cancer studies has dramatically increased recently, but there is substantial room for improvement in the quality of reporting even in top journals. Herein we have proposed reporting guidelines for PS analyses that are broadly applicable to different areas of medical research that will allow better evaluation and comparison across studies applying this approach.

Publication types

  • Review
  • Systematic Review

MeSH terms

  • Algorithms
  • Biomedical Research / standards*
  • Guidelines as Topic
  • Humans
  • Journal Impact Factor
  • Neoplasms / surgery*
  • Periodicals as Topic / standards
  • Periodicals as Topic / trends
  • Propensity Score*
  • Research Design / standards*
  • Research Design / trends
  • Research Report / standards*
  • Research Report / trends