Year-end Sale % OFF 
Abstract
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.
Highlights
Due to kidney failure, patients with end-stage renal disease (ESRD) require long-term renal replacement therapy with dialysis or kidney transplantation to sustain life
First, we develop a high-dimensional fixed effects (FEs) Poisson regression model that accommodates thousands of facility-level parameters needed for profiling and the model estimation is achieved via an efficient Newton-Raphson algorithm, inspired by the seminal work of
For profiling facilities with a count outcome, we introduce the standardized event ratio (SER) measure for each facility, which is the ratio of the expected number of events (e.g., # of Hb out of target) for patients treated at a given facility to the expected number of events if these same patients were treated at an “average” facility, i.e., a national reference standard
Summary
Profiling analysis aims to evaluate health care providers, such as hospitals, nursing homes, or dialysis facilities, with respect to a patient outcome. Anemic events (when hemoglobin levels exceeds normative range) are recurrent and common for patients on dialysis. We propose high-dimensional Poisson and negative binomial regression models for rate/count outcomes and introduce a standardized event ratio (SER) 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. The proposed methods are illustrated with profiling dialysis facilities for recurrent anemia events. Keywords End-stage renal disease; fixed effects; high-dimensional parameters, negative binomial regression; Poisson regression; profiling analysis
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
