{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T18:39:51Z","timestamp":1792435191855,"version":"4.5.0"},"reference-count":48,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"10","content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"abstract":"<jats:sec>\n                    <jats:title\/>\n                    <jats:p>\n                      <jats:list list-type=\"plain\">\n                        <jats:list-item>\n                          <jats:p>\u00bb The propensity score is a patient's modeled probability of receiving a treatment given measured baseline characteristics (covariates). Matching patients with similar scores balances those covariates between groups, approximating the comparability achieved by randomization.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>\u00bb Target trial emulation specifies the protocol of the ideal randomized trial and then reproduces it in observational data, enforcing an explicit eligibility window, time zero (the single point at which eligibility, treatment assignment, and the start of follow-up coincide), and analysis plan.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>\u00bb Regression adjusts for confounders within the outcome model, whereas propensity score matching balances those confounders before outcomes are examined. Propensity matching is preferred for rare outcomes or many confounders, where regression tends to overfit.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>\u00bb Neither propensity matching nor target trial emulation addresses unmeasured confounding, and both lose validity in small single-center cohorts with limited overlap (few treated and untreated patients who resemble one another closely enough to be fairly compared); a randomized trial remains the reference standard when feasible.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>\u00bb When appraising studies using propensity matching or target trial emulation, look for 3 key features: (1) Postmatching covariate balance: Are the treatment groups well balanced after matching? (standardized mean difference &lt;0.10). (2) Positivity: Is there enough overlap in patient characteristics that treated and untreated patients can be fairly compared? (3) Unambiguous \u201ctime zero\u201d: Did follow-up begin at the same well-defined starting point for all patients?<\/jats:p>\n                        <\/jats:list-item>\n                      <\/jats:list>\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.2106\/jbjs.rvw.26.00185","type":"journal-article","created":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T18:00:23Z","timestamp":1792432823000},"update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":0,"title":["Propensity Score Methods and Target Trial Emulation in Orthopaedic Surgery: A Practical Guide to Causal Inference from Observational Data"],"prefix":"10.2106","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0608-3065","authenticated-orcid":false,"given":"Wayne","family":"Hoskins","sequence":"first","affiliation":[{"name":"Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Parkville, Victoria, 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Missouri"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8987-2881","authenticated-orcid":false,"given":"Jaime","family":"Bellamy","sequence":"additional","affiliation":[{"name":"Department of Orthopaedic Surgery, University of Missouri\u2013Kansas City, Kansas City, Missouri"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3305-4067","authenticated-orcid":false,"given":"Elizabeth G.","family":"Lieberman","sequence":"additional","affiliation":[{"name":"Department of Orthopaedics and Rehabilitation, Oregon Health & Science University, Portland, Oregon"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9111-9239","authenticated-orcid":false,"given":"Gerard P.","family":"Slobogean","sequence":"additional","affiliation":[{"name":"Department of Orthopaedics, University of California \u2013 Irvine, Irvine, California"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0537-3474","authenticated-orcid":false,"given":"Nathan N.","family":"O'Hara","sequence":"additional","affiliation":[{"name":"Department of Orthopaedics, University of California \u2013 Irvine, Irvine, California"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","published-online":{"date-parts":[[2026,10]]},"reference":[{"key":"r1-20261019","doi-asserted-by":"crossref","first-page":"n1511","DOI":"10.1136\/bmj.n1511","article-title":"Common elective orthopaedic procedures and their clinical effectiveness: umbrella review of level 1 evidence","volume":"374","author":"Blom","year":"2021","journal-title":"BMJ"},{"issue":"1","key":"r2-20261019","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1007\/s00167-022-07215-9","article-title":"Multivariable regression: understanding one of medicine's most fundamental statistical 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