Literature DB >> 36263277

A Systematic Review of Propensity Score Matching in the Orthopedic Literature.

Gabriel R Arguelles1, Max Shin1, Drake G Lebrun2, Christopher J DeFrancesco2, Peter D Fabricant2, Keith D Baldwin3.   

Abstract

Background: Propensity score matching (PSM) is a statistical technique used to reduce bias in observational studies by controlling for measured confounders. Given its complexity and popularity, it is imperative that researchers comprehensively report their methodologies to ensure accurate interpretation and reproducibility. Purpose: This systematic review sought to define how often PSM has been used in recent orthopedic research and to describe how such studies reported their methods. Secondary aims included analyzing study reproducibility, bibliometric factors associated with reproducibility, and associations between methodology and the reporting of statistically significant results.
Methods: PubMed and Embase databases were queried for studies containing "propensity score" and "match*" published in 20 orthopedic journals prior to 2020. All studies meeting inclusion criteria were used for trend analysis. Articles published between 2017 and 2019 were used for analysis of reporting quality and reproducibility.
Results: In all, 261 studies were included for trend analysis, and 162 studies underwent full-text review. The proportion of orthopedic studies using PSM significantly increased over time. Seventy-one (41%) articles did not provide justification for covariate selection. The majority of studies illustrated covariate balance through P values. We found that 19% of the studies were fully reproducible. Most studies failed to report the use of replacement (67.3%) or independent or paired statistical methods (34.0%). Studies reporting standardized mean differences to illustrate covariate balance were less likely to report statistically significant results.
Conclusion: Despite the increased use of PSM in orthopedic research, observational studies employing PSM have largely failed to adequately report their methodology.
© The Author(s) 2022.

Entities:  

Keywords:  biostatistics; observational studies; propensity score matching; reproducibility; scientific reporting

Year:  2022        PMID: 36263277      PMCID: PMC9527541          DOI: 10.1177/15563316221082632

Source DB:  PubMed          Journal:  HSS J        ISSN: 1556-3316


  24 in total

1.  Interval estimation for treatment effects using propensity score matching.

Authors:  Jennifer Hill; Jerome P Reiter
Journal:  Stat Med       Date:  2006-07-15       Impact factor: 2.373

2.  Balance diagnostics after propensity score matching.

Authors:  Zhongheng Zhang; Hwa Jung Kim; Guillaume Lonjon; Yibing Zhu
Journal:  Ann Transl Med       Date:  2019-01

3.  Critical appraisal of the application of propensity score methods in the urology literature.

Authors:  Madhur Nayan; Robert J Hamilton; David N Juurlink; Antonio Finelli; Girish S Kulkarni; Peter C Austin
Journal:  BJU Int       Date:  2017-07-07       Impact factor: 5.588

4.  Comparing Complications and Costs of Total Hip Arthroplasty and Hemiarthroplasty for Femoral Neck Fractures: A Propensity Score-Matched, Population-Based Study.

Authors:  Bheeshma Ravi; Daniel Pincus; Hayat Khan; David Wasserstein; Richard Jenkinson; Hans J Kreder
Journal:  J Bone Joint Surg Am       Date:  2019-04-03       Impact factor: 5.284

Review 5.  Potential Pitfalls of Reporting and Bias in Observational Studies With Propensity Score Analysis Assessing a Surgical Procedure: A Methodological Systematic Review.

Authors:  Guillaume Lonjon; Raphael Porcher; Patrick Ergina; Mathilde Fouet; Isabelle Boutron
Journal:  Ann Surg       Date:  2017-05       Impact factor: 12.969

Review 6.  Reporting and Guidelines in Propensity Score Analysis: A Systematic Review of Cancer and Cancer Surgical Studies.

Authors:  Xiaoxin I Yao; Xiaofei Wang; Paul J Speicher; E Shelley Hwang; Perry Cheng; David H Harpole; Mark F Berry; Deborah Schrag; Herbert H Pang
Journal:  J Natl Cancer Inst       Date:  2017-08-01       Impact factor: 13.506

7.  Cost-effectiveness of Balloon Kyphoplasty for Patients With Acute/Subacute Osteoporotic Vertebral Fractures in the Super-Aging Japanese Society.

Authors:  Shinji Takahashi; Masatoshi Hoshino; Hiroyuki Yasuda; Hidetomi Terai; Kazunori Hayashi; Tadao Tsujio; Hiroshi Kono; Akinobu Suzuki; Koji Tamai; Shoichiro Ohyama; Hiromitsu Toyoda; Sho Dohzono; Fumiaki Kanematsu; Yusuke Hori; Hiroaki Nakamura
Journal:  Spine (Phila Pa 1976)       Date:  2019-03-01       Impact factor: 3.468

8.  Comparing paired vs non-paired statistical methods of analyses when making inferences about absolute risk reductions in propensity-score matched samples.

Authors:  Peter C Austin
Journal:  Stat Med       Date:  2011-02-21       Impact factor: 2.373

9.  Propensity score matching in otolaryngologic literature: A systematic review and critical appraisal.

Authors:  Aman Prasad; Max Shin; Ryan M Carey; Kevin Chorath; Harman Parhar; Scott Appel; Alvaro Moreira; Karthik Rajasekaran
Journal:  PLoS One       Date:  2020-12-31       Impact factor: 3.240

10.  Matrix-Associated Chondrocyte Implantation Is Associated With Fewer Reoperations Than Microfracture: Results of a Population-Representative, Matched-Pair Claims Data Analysis for Cartilage Defects of the Knee.

Authors:  Philipp Niemeyer; Tino Schubert; Marco Grebe; Arnd Hoburg
Journal:  Orthop J Sports Med       Date:  2019-10-21
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