Logistic Versus Hierarchical Modeling: An Analysis of a Statewide Inpatient Sample - 24/08/11
, Min-Hua Jen, MS, PhD, Alex Bottle, BSc, MSc, PhD, Brian Jarman, MA, PhD, FRCP, FRCGP, FFPH, FMedSci, Paul Aylin, MB, CHB, FFPH
Correspondence address: Roxana Alexandrescu, MD, MPH, Dr Foster Unit at Imperial College, Department of Primary Care and Public Health, Imperial College London, London EC1A 9LA, UKRésumé |
Background |
Although logistic regression is traditionally used to calculate hospital standardized mortality ratio (HSMR), it ignores the hierarchical structure of the data that can exist within a given database. Hierarchical models allow examination of the effect of data clustering on outcomes.
Study Design |
Traditional logistic regression and random intercepts fixed slopes hierarchical models were fitted to a dataset of patients hospitalized between 2005 and 2007 in Massachusetts. We compared the observed to expected (O/E) in-hospital death ratios between the 2 modeling techniques, a restricted HSMR using only those diagnosis models that converged in both methods and a full hybrid HSMR using a combination of the hierarchical diagnosis models when they converge, plus the remaining diagnoses using standard logistic regression models.
Results |
We restricted the analysis to the 36 diagnoses accounting for 80% of in-hospital deaths nationally, based on 1,043,813 admissions (59 hospitals). A failure of the hierarchical models to converge in 15 of 36 diagnosis groups hindered full HSMR comparisons. A restricted HSMR, derived from a dataset based on the 21 diagnosis groups that converged (552,933 admissions) showed very high correlation (Pearson r = 0.99). Both traditional logistic regression and hierarchical model identified 12 statistical outliers in common, 7 with high O/E values and 5 with low O/E values. In addition, the multilevel analysis identified 5 additional unique high outliers and 1 additional unique low outlier, and the conventional model identified 2 additional unique low outliers.
Conclusions |
Similar results were obtained from the 2 modeling techniques in terms of O/E ratios. However, because a hierarchical model is associated with convergence problems, traditional logistic regression remains our recommended procedure for computing HSMRs.
Le texte complet de cet article est disponible en PDF.Abbreviations and Acronyms : CCS, CL, DNR, HSMR, ICC, O/E
Plan
| Disclosure Information: Dr Alexandrescu is funded by a research grant from Rx Foundation of Cambridge, MA. All authors are members of the Dr Foster Unit at Imperial College, which is funded by a research grant from Dr Foster Intelligence, an independent health care information company. |
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| The Dr Foster Unit at Imperial College is principally funded through a research grant from Dr Foster Intelligence, an independent health care information company (part owned by the UK Department of Health). The Dr Foster Unit at Imperial is affiliated with the Imperial Centre for Patient Safety and Service Quality at Imperial College Healthcare NHS Trust, which is funded by the National Institute for Health Research. The Department of Primary Care and Public Health is grateful for support from the National Institute for Health Research Biomedical Research Centre Funding Scheme. |
Vol 213 - N° 3
P. 392-401 - septembre 2011 Retour au numéro
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