{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T06:31:31Z","timestamp":1785825091073,"version":"3.56.0"},"reference-count":46,"publisher":"BMJ","issue":"6","license":[{"start":{"date-parts":[[2022,6,7]],"date-time":"2022-06-07T00:00:00Z","timestamp":1654560000000},"content-version":"unspecified","delay-in-days":6,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100007217","name":"Health Services Research and Development","doi-asserted-by":"publisher","award":["QUE 15-280"],"award-info":[{"award-number":["QUE 15-280"]}],"id":[{"id":"10.13039\/100007217","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["bmj.com"],"crossmark-restriction":true},"short-container-title":["BMJ Open"],"published-print":{"date-parts":[[2022,6]]},"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Configurational methods are increasingly being used in health services research.<\/jats:p><\/jats:sec><jats:sec><jats:title>Objectives<\/jats:title><jats:p>To use configurational analysis and logistic regression within a single data set to compare results from the two methods.<\/jats:p><\/jats:sec><jats:sec><jats:title>Design<\/jats:title><jats:p>Secondary analysis of an observational cohort; a split-sample design involved randomly dividing patients into training and validation samples.<\/jats:p><\/jats:sec><jats:sec><jats:title>Participants and setting<\/jats:title><jats:p>Patients who had a transient ischaemic attack (TIA) in US Department of Veterans Affairs hospitals.<\/jats:p><\/jats:sec><jats:sec><jats:title>Measures<\/jats:title><jats:p>The patient outcome was the combined endpoint of all-cause mortality or recurrent ischaemic stroke within 1 year post-TIA. The quality-of-care outcome was the without-fail rate (proportion of patients who received all processes for which they were eligible, among seven processes).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>For the recurrent stroke or death outcome, configurational analysis yielded a three-pathway model identifying a set of (validation sample) patients where the prevalence was 15.0% (83\/552), substantially higher than the overall sample prevalence of 11.0% (relative difference, 36%). The configurational model had a sensitivity (coverage) of 84.7% and specificity of 40.6%. The logistic regression model identified six factors associated with the combined endpoint (c-statistic, 0.632; sensitivity, 63.3%; specificity, 63.1%). None of these factors were elements of the configurational model. For the quality outcome, configurational analysis yielded a single-pathway model identifying a set of (validation sample) patients where the without-fail rate was 64.3% (231\/359), nearly twice the overall sample prevalence (33.7%). The configurational model had a sensitivity (coverage) of 77.3% and specificity of 78.2%. The logistic regression model identified seven factors associated with the without-fail rate (c-statistic, 0.822; sensitivity, 80.3%; specificity, 84.2%). Two of these factors were also identified in the configurational analysis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Configurational analysis and logistic regression represent different methods that can enhance our understanding of a data set when paired together. Configurational models optimise sensitivity with relatively few conditions. Logistic regression models discriminate cases from controls and provided inferential relationships between outcomes and independent variables.<\/jats:p><\/jats:sec>","DOI":"10.1136\/bmjopen-2022-061469","type":"journal-article","created":{"date-parts":[[2022,6,7]],"date-time":"2022-06-07T15:22:04Z","timestamp":1654615324000},"page":"e061469","update-policy":"https:\/\/doi.org\/10.1136\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":9,"title":["Pairing regression and configurational analysis in health services research: modelling outcomes in an observational cohort using a split-sample design"],"prefix":"10.1136","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7187-1570","authenticated-orcid":false,"given":"Edward J","family":"Miech","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anthony J","family":"Perkins","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laura J","family":"Myers","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jason J","family":"Sico","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joanne","family":"Daggy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3048-2536","authenticated-orcid":false,"given":"Dawn M","family":"Bravata","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"239","published-online":{"date-parts":[[2022,6,7]]},"reference":[{"key":"2022060708151474000_12.6.e061469.1","doi-asserted-by":"publisher","DOI":"10.1097\/MLR.0000000000001319"},{"key":"2022060708151474000_12.6.e061469.2","doi-asserted-by":"publisher","DOI":"10.1093\/geront\/gnaa041"},{"key":"2022060708151474000_12.6.e061469.3","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-publhealth-040218-044215"},{"key":"2022060708151474000_12.6.e061469.4","article-title":"School-based self-management interventions for asthma in children and adolescents: a mixed methods systematic review","volume":"1","author":"Harris","year":"2019","journal-title":"Cochrane Database Syst Rev"},{"key":"2022060708151474000_12.6.e061469.5","doi-asserted-by":"crossref","unstructured":"Cragun D . 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