Reporting of covariate selection and balance assessment in propensity score analysis is suboptimal: A systematic review

M. Sanni Ali, Rolf H H Groenwold, Svetlana V. Belitser, Wiebe R. Pestman, Arno W. Hoes, Kit C B Roes, Anthonius De Boer, Olaf H. Klungel*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

3 Citations (Scopus)


Objectives To assess the current practice of propensity score (PS) analysis in the medical literature, particularly the assessment and reporting of balance on confounders. Study Design and Setting A PubMed search identified studies using PS methods from December 2011 through May 2012. For each article included in the review, information was extracted on important aspects of the PS such as the type of PS method used, variable selection for PS model, and assessment of balance. Results Among 296 articles that were included in the review, variable selection for PS model was explicitly reported in 102 studies (34.4%). Covariate balance was checked and reported in 177 studies (59.8%). P-values were the most commonly used statistical tools to report balance (125 of 177, 70.6%). The standardized difference and graphical displays were reported in 45 (25.4%) and 11 (6.2%) articles, respectively. Matching on the PS was the most commonly used approach to control for confounding (68.9%), followed by PS adjustment (20.9%), PS stratification (13.9%), and inverse probability of treatment weighting (IPTW, 7.1%). Balance was more often checked in articles using PS matching and IPTW, 70.6% and 71.4%, respectively. Conclusion The execution and reporting of covariate selection and assessment of balance is far from optimal. Recommendations on reporting of PS analysis are provided to allow better appraisal of the validity of PS-based studies.

Original languageEnglish
Pages (from-to)112-121
Number of pages10
JournalJournal of Clinical Epidemiology
Issue number2
Publication statusPublished - 1 Feb 2015


  • Analysis of Variance
  • Data Interpretation, Statistical
  • Humans
  • Patient Selection
  • Propensity Score
  • Research Design
  • Selection Bias


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