Premium
Patient death as a censoring event or competing risk event in models of nursing home placement
Author(s) -
Szychowski Jeff M.,
Roth David L.,
Clay Olivio J.,
Mittelman Mary S.
Publication year - 2009
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.3797
Subject(s) - censoring (clinical trials) , covariate , proportional hazards model , medicine , event (particle physics) , survival analysis , statistics , mathematics , surgery , physics , quantum mechanics
Participant death is often observed in studies that examine predictors of events, such as hospitalization or institutionalization, in older adult populations. The Cox proportional hazards modeling of the target event, whereby death is treated as a censoring event, is the standard analysis in this competing risks situation. However, the assumption of noninformative censoring applied to a frequently occurring competing event like death may be invalid and complicate interpretation in terms of the probability of the event. Multiple cause‐specific hazard (CSH) models can be estimated, but ambiguities may arise when interpreting covariate effects across multiple CSH models and in terms of the cumulative incidence function (CIF). Alternatively, one can model the proportional hazards of the subdistribution of the CIF and evaluate the covariate effects on the CIF directly. We examine and compare these two approaches with nursing home (NH) placement data from a randomized controlled trial of a counseling and support intervention for spouse‐caregivers of patients with Alzheimer's disease. CSHs for NH placement (where death is treated as a censoring event) and death (where NH placement is treated as a censoring event) and subdistribution hazards of the CIF for NH placement are modeled separately. In the presence of multiple covariates, the intervention effect is significant in both approaches, but the interpretation of the covariate effects requires joint evaluation of all estimated models. Copyright © 2009 John Wiley & Sons, Ltd.