Semi-Parametric Time-to-Event Modelling of Lengths of Hospital Stays

Abstract

Length of stay (LOS) is an essential metric for the quality of hospital care. Published works on LOS analysis have primarily focused on skewed LOS distributions and the influences of patient diagnostic characteristics. Few authors have considered the events that terminate a hospital stay: Both successful discharge and death could end a hospital stay but with completely different implications. Modelling the time to the first occurrence of discharge or death obscures the true nature of LOS. In this research, we propose a structure that simultaneously models the probabilities of discharge and death. The model has a flexible formulation that accounts for both additive and multiplicative effects of factors influencing the occurrence of death and discharge. We present asymptotic properties of the parameter estimates so that valid inference can be performed for the parametric as well as nonparametric model components. Simulation studies confirmed the good finite‐sample performance of the proposed method. As the research is motivated by practical issues encountered in LOS analysis, we analysed data from two real clinical studies to showcase the general applicability of the proposed model.

An additive-multiplicative model for longitudinal data with informative observation times

“…Covariates in the additive function have constant effects on the hazard function over time, whereas the multiplicative component represents the time-varying effects on the hazard rate. [14][15][16] Such modeling structures, however, are less explored in longitudinal data analysis. Theoretical results in support of inference in such models remain undeveloped.…”