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Evaluating inter‐hospital variability in mortality rates over time, allowing for time‐varying effects
Author(s) -
Galai Noya,
Simchen Elisheva,
Braun Dalit,
Mandel Micha,
ZitserGurevich Yana
Publication year - 2001
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.936
Subject(s) - logistic regression , confidence interval , outcome (game theory) , proportional hazards model , medicine , statistics , survival analysis , mortality rate , demography , emergency medicine , econometrics , mathematics , mathematical economics , sociology
In outcome studies, quality of care in various institutions is typically assessed by comparing observed to expected outcome rates, after adjusting for patients' case‐mix factors in logistic regression models. However, differences in patterns of outcome rates over time, especially when there is a distinction between the determinants affecting early and later events, are rarely studied. We use six‐month mortality after coronary artery bypass graft operation (CABG) as an example. We present a statistically valid approach to estimate expected survival curves for different subgroups, based on a Cox survival model with time‐varying effects. Bootstrap confidence intervals around the expected survival curves are constructed. This approach is applied for examining the pattern of deviation of high‐mortality hospitals after CABG. Implications for quality assessment in comparative outcome studies are discussed. Copyright © 2002 John Wiley & Sons, Ltd.