B0955
Title: DPQL: A lossless distributed algorithm for generalized linear mixed model with application to hospital profiling
Authors: Chongliang Luo - Washington University in St Louis (United States) [presenting]
Abstract: Hospital profiling, the process that determines to what extent patient outcomes depend on the hospital, provides a quantitative comparison of healthcare providers based on their quality of care. To implement hospital profiling, the generalized linear mixed model (GLMM) is used to fit outcome models using clinical or administrative claims data. For better generalizability, data across multiple hospitals, databases, or networks are desired. However, due to privacy regulations and the computational complexity of GLMM, a distributed algorithm for hospital profiling is needed. We develop a novel distributed Penalized Quasi Likelihood (dPQL) algorithm to fit GLMM when only aggregated data, rather than individual patient data, can be shared across hospitals. The proposed algorithm is lossless, i.e., it obtains identical results as if individual patient data were pooled from all hospitals. We apply the dPQL algorithm by ranking 929 hospitals for COVID-19 mortality or referral to a hospice that has been previously studied.
