Models and methods for analysing clustered recurrent hospitalisations in the presence of COVID-19 effects
针对美国数千家透析设施的大量重复住院数据，提出一种计算高效且能灵活调整基线风险率的统计方法，同时兼顾直接与间接标准化的优点，并用于监测COVID-19影响下的设施质量。
Abstract Recurrent events such as hospitalisations are outcomes that can be used to monitor dialysis facilities’ quality of care. However, current methods are not adequate to analyse data from many facilities with multiple hospitalisations, especially when adjustments are needed for multiple time scales. It is also controversial whether direct or indirect standardisation should be used in comparing facilities. This study is motivated by the need of the Centers for Medicare and Medicaid Services to evaluate US dialysis facilities using Medicare claims, which involve almost 8,000 facilities and over 500,000 dialysis patients. This scope is challenging for current statistical software’s computational power. We propose a method that has a flexible baseline rate function and is computationally efficient. Additionally, the proposed method shares advantages of both indirect and direct standardisation. The method is evaluated under a range of simulation settings and demonstrates substantially improved computational efficiency over the existing R package survival. Finally, we illustrate the method with an important application to monitoring dialysis facilities in the U.S., while making time-dependent adjustments for the effects of COVID-19.