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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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
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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| Summary: | 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. |
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| ISSN: | 2044-6055 |