Please use this identifier to cite or link to this item: http://hdl.handle.net/11054/1790
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dc.contributorMoran, J.en_US
dc.contributorSantamaria, J.en_US
dc.contributorDuke, G.en_US
dc.date.accessioned2021-10-04T04:03:28Z-
dc.date.available2021-10-04T04:03:28Z-
dc.date.issued2021-
dc.identifier.govdoc01744en_US
dc.identifier.urihttp://hdl.handle.net/11054/1790-
dc.descriptionIncludes BHS dataen_US
dc.description.abstractBackground: Mortality modelling in the critical care paradigm traditionally uses logistic regression, despite the availability of estimators commonly used in alternate disciplines. Little attention has been paid to covariate endogeneity and the status of non-randomized treatment assignment. Using a large registry database, various binary outcome modelling strategies and methods to account for covariate endogeneity were explored. Methods: Patient mortality data was sourced from the Australian & New Zealand Intensive Society Adult Patient Database for 2016. Hospital mortality was modelled using logistic, probit and linear probability (LPM) models with intensive care (ICU) providers as fixed (FE) and random (RE) effects. Model comparison entailed indices of discrimination and calibration, information criteria (AIC and BIC) and binned residual analysis. Suspect covariate and ventilation treatment assignment endogeneity was identified by correlation between predictor variable and hospital mortality error terms, using the Stata™ "eprobit" estimator. Marginal effects were used to demonstrate effect estimate differences between probit and "eprobit" models. Results: The cohort comprised 92,693 patients from 124 intensive care units (ICU) in calendar year 2016. Patients mean age was 61.8 (SD 17.5) years, 41.6% were female and APACHE III severity of illness score 54.5(25.6); 43.7% were ventilated. Of the models considered in predicting hospital mortality, logistic regression (with or without ICU FE) and RE logistic regression dominated, more so the latter using information criteria indices. The LPM suffered from many predictions outside the unit [0,1] interval and both poor discrimination and calibration. Error terms of hospital length of stay, an independent risk of death score and ventilation status were correlated with the mortality error term. Marked differences in the ventilation mortality marginal effect was demonstrated between the probit and the "eprobit" models which were scenario dependent. Endogeneity was not demonstrated for the APACHE III score. Conclusions: Logistic regression accounting for provider effects was the preferred estimator for hospital mortality modelling. Endogeneity of covariates and treatment variables may be identified using appropriate modelling, but failure to do so yields problematic effect estimates.en_US
dc.description.provenanceSubmitted by Gemma Siemensma (gemmas@bhs.org.au) on 2021-08-20T05:27:24Z No. of bitstreams: 0en
dc.description.provenanceApproved for entry into archive by Gemma Siemensma (gemmas@bhs.org.au) on 2021-10-04T04:03:28Z (GMT) No. of bitstreams: 0en
dc.description.provenanceMade available in DSpace on 2021-10-04T04:03:28Z (GMT). No. of bitstreams: 0 Previous issue date: 2021en
dc.titleModelling hospital outcome: problems with endogeneity.en_US
dc.typeJournal Articleen_US
dc.type.specifiedArticleen_US
dc.contributor.corpauthorThe Australian & New Zealand Intensive Care Society (ANZICS) Centre for Outcomes & Resource Evaluation (CORE)en_US
dc.bibliographicCitation.titleBMC Medical Research Methodologyen_US
dc.bibliographicCitation.volume21en_US
dc.bibliographicCitation.stpage124en_US
dc.subject.healththesaurusCALIBRATIONen_US
dc.subject.healththesaurusENDOGENEITYen_US
dc.subject.healththesaurusLINEAR PROBABILITY MODELen_US
dc.subject.healththesaurusLOGITen_US
dc.subject.healththesaurusMARGINAL EFECTSen_US
dc.subject.healththesaurusOUTCOME ANALYSISen_US
dc.subject.healththesaurusPROBITen_US
dc.identifier.doihttps://doi.org/10.1186/s12874-021-01251-8en_US
Appears in Collections:Research Output

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