The analysis of COVID-19 in-hospital mortality: A competing risk approach or a cure model?

Abstract

Competing risk analyses have been widely used for the analysis of in-hospital mortality in which hospital discharge is considered as a competing event. The competing risk model assumes that more than one cause of failure is possible, but there is only one outcome of interest and all others serve as competing events. However, hospital discharge and in-hospital death are two outcomes resulting from the same disease process and patients whose disease conditions were stabilized so that inpatient care was no longer needed were discharged. We therefore propose to use cure models, in which hospital discharge is treated as an observed “cure” of the disease. We consider both the mixture cure model and the promotion time cure model and extend the models to allow cure status to be known for those who were discharged from the hospital. An EM algorithm is developed for the mixture cure model. We also show that the competing risk model, which treats hospital discharge as a competing event, is equivalent to a promotion time cure model. Both cure models were examined in simulation studies and were applied to a recent cohort of COVID-19 in-hospital patients with diabetes. The promotion time model shows that statin use improved the overall survival; the mixture cure model shows that while statin use reduced the in-hospital mortality rate among the susceptible, it improved the cure probability only for older but not younger patients. Both cure models show that treatment was more beneficial among older patients.

Target Trial Emulation Using Hospital-Based Observational Data: Demonstration and Application in COVID-19

“…Their second method entailed an inverse probability weighted Kaplan–Meier estimator that accounted for the eliminated (censored) competing event of discharge alive [ 48 ]. We believe that acknowledging the treatment effects on discharge alive is more meaningful: the interest may be to examine not only how treatment prevents death, but also how treatment affects the probability of recovering from the infection [ 41 ]. We showed that a competing risks analysis provides important insights on treatment effects on all clinically meaningful and heterogeneous end points [ 11 , 50 ].…”

“…By definition, a competing risk (e.g., hospital discharge) is an event that prevents the occurrence of the primary outcome (e.g., in-hospital death) of interest [ 39 ]. Our emulated framework fits a simplified three-state competing risks model to estimate transition hazard rates ( Figure 2 ), where in-hospital death and discharge alive are two absorbing events of the same disease [ 40 , 41 ]. Each arrow represents a time-homogeneous hazard rate from the initial state of hospital admission as untreated or treated to the two terminating end points of in-hospital death or discharged alive , respectively.…”