Profiling dialysis facilities for adverse recurrent events.

Autor: Estes JP; Pratt & Whitney, East Hartford, Connecticut., Chen Y; Institute for Clinical and Translational Science, University of California, Irvine, California., Şentürk D; Department of Biostatistics, University of California, Los Angeles, California., Rhee CM; Harold Simmons Center for Chronic Disease Research and Epidemiology, University of California Irvine School of Medicine, Orange, California., Kürüm E; Department of Statistics, University of California, Riverside, California., You AS; Harold Simmons Center for Chronic Disease Research and Epidemiology, University of California Irvine School of Medicine, Orange, California., Streja E; Harold Simmons Center for Chronic Disease Research and Epidemiology, University of California Irvine School of Medicine, Orange, California., Kalantar-Zadeh K; Harold Simmons Center for Chronic Disease Research and Epidemiology, University of California Irvine School of Medicine, Orange, California., Nguyen DV; Department of Medicine, University of California Irvine, Orange, California.
Jazyk: angličtina
Zdroj: Statistics in medicine [Stat Med] 2020 Apr 30; Vol. 39 (9), pp. 1374-1389. Date of Electronic Publication: 2020 Jan 30.
DOI: 10.1002/sim.8482
Abstrakt: Profiling analysis aims to evaluate health care providers, such as hospitals, nursing homes, or dialysis facilities, with respect to a patient outcome. Previous profiling methods have considered binary outcomes, such as 30-day hospital readmission or mortality. For the unique population of dialysis patients, regular blood works are required to evaluate effectiveness of treatment and avoid adverse events, including dialysis inadequacy, imbalance mineral levels, and anemia among others. For example, anemic events (when hemoglobin levels exceed normative range) are recurrent and common for patients on dialysis. Thus, we propose high-dimensional Poisson and negative binomial regression models for rate/count outcomes and introduce a standardized event ratio measure to compare the event rate at a specific facility relative to a chosen normative standard, typically defined as an "average" national rate across all facilities. Our proposed estimation and inference procedures overcome the challenge of high-dimensional parameters for thousands of dialysis facilities. Also, we investigate how overdispersion affects inference in the context of profiling analysis. The proposed methods are illustrated with profiling dialysis facilities for recurrent anemia events.
(© 2020 John Wiley & Sons, Ltd.)
Databáze: MEDLINE
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