Pairing regression and configurational analysis in health services research: modelling outcomes in an observational cohort using a split-sample design
Background Configurational methods are increasingly being used in health services research.Objectives To use configurational analysis and logistic regression within a single data set to compare results from the two methods.Design Secondary analysis of an observational cohort; a split-sample design i...
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| Format: | Article |
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BMJ Publishing Group
2022-06-01
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| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/12/6/e061469.full |
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| author | Jason J Sico Laura J Myers Dawn M Bravata Ying Zhang Anthony J Perkins Edward J Miech Joanne Daggy |
| author_facet | Jason J Sico Laura J Myers Dawn M Bravata Ying Zhang Anthony J Perkins Edward J Miech Joanne Daggy |
| author_sort | Jason J Sico |
| collection | DOAJ |
| description | Background Configurational methods are increasingly being used in health services research.Objectives To use configurational analysis and logistic regression within a single data set to compare results from the two methods.Design Secondary analysis of an observational cohort; a split-sample design involved randomly dividing patients into training and validation samples.Participants and setting Patients who had a transient ischaemic attack (TIA) in US Department of Veterans Affairs hospitals.Measures 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).Results 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.Conclusions 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. |
| format | Article |
| id | doaj-art-234e52bc26a04a1ab660dd1f240661eb |
| institution | Kabale University |
| issn | 2044-6055 |
| language | English |
| publishDate | 2022-06-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open |
| spelling | doaj-art-234e52bc26a04a1ab660dd1f240661eb2025-01-24T09:10:09ZengBMJ Publishing GroupBMJ Open2044-60552022-06-0112610.1136/bmjopen-2022-061469Pairing regression and configurational analysis in health services research: modelling outcomes in an observational cohort using a split-sample designJason J Sico0Laura J Myers1Dawn M Bravata2Ying Zhang3Anthony J Perkins4Edward J Miech5Joanne Daggy6Department of Neurology, Veterans Affairs Connecticut Healthcare System, West Haven, Connecticut, USA4 VA Health Services Research and Development (HSR&D) Center for Healthcare Informatics and Communication and the HSR&D Stroke Quality Enhancement Research Initiative (QUERI), Indianapolis, Indiana, USACenter for Health Services Research, Regenstrief Institute Inc, Indianapolis, Indiana, USADepartment of Biostatistics, University of Nebraska Medical Center, Omaha, Nebraska, USABiostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, Indiana, USAVA Health Services Research and Development (HSR&D) Expanding Expertise Through E-health Network Development (EXTEND) Quality Enhancement Research Initiative (QUERI), Indianapolis, Indiana, USAIndiana University School of Medicine, Indianapolis, Indiana, USABackground Configurational methods are increasingly being used in health services research.Objectives To use configurational analysis and logistic regression within a single data set to compare results from the two methods.Design Secondary analysis of an observational cohort; a split-sample design involved randomly dividing patients into training and validation samples.Participants and setting Patients who had a transient ischaemic attack (TIA) in US Department of Veterans Affairs hospitals.Measures 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).Results 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.Conclusions 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.https://bmjopen.bmj.com/content/12/6/e061469.full |
| spellingShingle | Jason J Sico Laura J Myers Dawn M Bravata Ying Zhang Anthony J Perkins Edward J Miech Joanne Daggy Pairing regression and configurational analysis in health services research: modelling outcomes in an observational cohort using a split-sample design BMJ Open |
| title | Pairing regression and configurational analysis in health services research: modelling outcomes in an observational cohort using a split-sample design |
| title_full | Pairing regression and configurational analysis in health services research: modelling outcomes in an observational cohort using a split-sample design |
| title_fullStr | Pairing regression and configurational analysis in health services research: modelling outcomes in an observational cohort using a split-sample design |
| title_full_unstemmed | Pairing regression and configurational analysis in health services research: modelling outcomes in an observational cohort using a split-sample design |
| title_short | Pairing regression and configurational analysis in health services research: modelling outcomes in an observational cohort using a split-sample design |
| title_sort | pairing regression and configurational analysis in health services research modelling outcomes in an observational cohort using a split sample design |
| url | https://bmjopen.bmj.com/content/12/6/e061469.full |
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