M Sanni Ali1, Rolf H H Groenwold1, Svetlana V Belitser2, Wiebe R Pestman3, Arno W Hoes4, Kit C B Roes4, Anthonius de Boer2, Olaf H Klungel5. 1. Division of Pharmacoepidemiology & Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Faculty of Science, Utrecht University, Utrecht, the Netherlands; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands. 2. Division of Pharmacoepidemiology & Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Faculty of Science, Utrecht University, Utrecht, the Netherlands. 3. Catholic University of Leuven, Research unit for Quantitative Psychology and Individual Differences, Leuven, Belgium. 4. Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands. 5. Division of Pharmacoepidemiology & Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Faculty of Science, Utrecht University, Utrecht, the Netherlands; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands. Electronic address: O.H.Klungel@uu.nl.
Abstract
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.
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.
Authors: Jason J Rose; Mehdi Nouraie; Marc C Gauthier; Anthony F Pizon; Melissa I Saul; Michael P Donahoe; Mark T Gladwin Journal: Crit Care Med Date: 2018-07 Impact factor: 7.598
Authors: Jia Li; Stuart C Gordon; Loralee B Rupp; Talan Zhang; Sheri Trudeau; Scott D Holmberg; Anne C Moorman; Philip R Spradling; Eyasu H Teshale; Joseph A Boscarino; Mark A Schmidt; Yihe G Daida; Mei Lu Journal: Liver Int Date: 2019-01-24 Impact factor: 5.828
Authors: Jay R Horton; R Sean Morrison; Elizabeth Capezuti; Jennifer Hill; Eric J Lee; Amy S Kelley Journal: J Palliat Med Date: 2016-06-01 Impact factor: 2.947
Authors: Md Jamal Uddin; Rolf H H Groenwold; Mohammed Sanni Ali; Anthonius de Boer; Kit C B Roes; Muhammad A B Chowdhury; Olaf H Klungel Journal: Int J Clin Pharm Date: 2016-04-18
Authors: Daniel I McIsaac; Karim Abdulla; Homer Yang; Sudhir Sundaresan; Paula Doering; Sandeep Green Vaswani; Kednapa Thavorn; Alan J Forster Journal: CMAJ Date: 2017-07-10 Impact factor: 8.262
Authors: Jia Li; Stuart C Gordon; Loralee B Rupp; Talan Zhang; Sheri Trudeau; Scott D Holmberg; Anne C Moorman; Philip R Spradling; Eyasu H Teshale; Joseph A Boscarino; Yihe G Daida; Mark A Schmidt; Mei Lu Journal: J Gastroenterol Hepatol Date: 2017-06 Impact factor: 4.029
Authors: Jia Li; Stuart C Gordon; Loralee B Rupp; Talan Zhang; Sheri Trudeau; Scott D Holmberg; Anne C Moorman; Philip R Spradling; Eyasu H Teshale; Joseph A Boscarino; Mark A Schmidt; Yihe G Daida; Mei Lu Journal: Aliment Pharmacol Ther Date: 2019-01-16 Impact factor: 8.171
Authors: Ryan D Ross; Xu Shi; Megan E V Caram; Pheobe A Tsao; Paul Lin; Amy Bohnert; Min Zhang; Bhramar Mukherjee Journal: Health Serv Outcomes Res Methodol Date: 2020-10-20