Observational studyBMJ open2022
Pairing regression and configurational analysis in health services research: modelling outcomes in an observational cohort using a split-sample design.
Observational study in BMJ open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04322162 (Addressing Sleep Apnea Post-Stroke), which is not on this map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Addressing Sleep Apnea Post-Stroke (ASAP)
Who cites it
9 citing papers in PubMed.
- Difference-Makers for Robust Implementation of a Nursing Home Advance Care Planning Embedded Pragmatic Clinical Trial.Journal of the American Geriatrics Society · 2026Trial
- How local prevention efforts and state regulations intersect to reduce fatal shootings: a configurational analysis of firearm policies in the United States.Journal of public health policy · 2026Article
- Difference-making pathways to implementing systematic, partial, or no data collection of sexual orientation and gender identity in oncology settings: a coincidence analysis.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026Article
- Organization-level determinants for low secondary traumatic stress in lay counselors delivering trauma-focused cognitive behavioral therapy in Kenya.PLOS global public health · 2026Article
- Exploring paths to participation and non-participation in physical exercise among Swedish adolescents.Frontiers in public health · 2026Article
- Difference-Making Factors Linked to Higher Reach of Specialist Palliative Care Among People With Heart Failure Across a National Sample of VA Medical Centers.The American journal of hospice & palliative care · 2025Article
- Difference-Makers for Collecting Sexual Orientation and Gender Identity Data in Oncology Settings.Cancer medicine · 2025Article
- Difference-making factors for successful implementation of a multicomponent colorectal cancer screening program in rural clinics (SMARTER CRC).Frontiers in medicine · 2025Article
- Reducing sickness absence among public-sector healthcare employees: the difference-making roles of managerial and employee participation.International archives of occupational and environmental health · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
backgroundConfigurational methods are increasingly being used in health services research.
objectivesTo use configurational analysis and logistic regression within a single data set to compare results from the two methods.
designSecondary analysis of an observational cohort; a split-sample design involved randomly dividing patients into training and validation samples. PARTICIPANTS AND
settingPatients 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).
resultsFor 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.
conclusionsConfigurational 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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.