Time-dependent covariates in the proportional subdistribution hazards model for competing risks

• Published in: Biostatistics (Oxford, England) Vol. 9; no. 4; pp. 765 - 776
• Main Authors: Beyersmann, Jan, Schumacher, Martin
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 01.10.2008; Oxford Publishing Limited (England)
• Subjects: Algorithms; Berlin; Biomedical research; Cross Infection - mortality; Humans; Intensive Care Units - statistics & numerical data; Mathematical models; Models, Statistical; Patient Discharge - statistics & numerical data; Pneumonia - mortality; Probability; Proportional Hazards Models; Risk Factors; Statistical analysis; Stochastic Processes; Survival Analysis; Survival Rate; Time Factors; Berlin; Fine and Gray model; Hospital infection; Multistate model
• ISSN: 1465-4644; 1468-4357; 1468-4357
• DOI: 10.1093/biostatistics/kxn009

Separate Cox analyses of all cause-specific hazards are the standard technique of choice to study the effect of a covariate in competing risks, but a synopsis of these results in terms of cumulative event probabilities is challenging. This difficulty has led to the development of the proportional subdistribution hazards model. If the covariate is known at baseline, the model allows for a summarizing assessment in terms of the cumulative incidence function. black Mathematically, the model also allows for including random time-dependent covariates, but practical implementation has remained unclear due to a certain risk set peculiarity. We use the intimate relationship of discrete covariates and multistate models to naturally treat time-dependent covariates within the subdistribution hazards framework. The methodology then straightforwardly translates to real-valued time-dependent covariates. As with classical survival analysis, including time-dependent covariates does not result in a model for probability functions anymore. Nevertheless, the proposed methodology provides a useful synthesis of separate cause-specific hazards analyses. We illustrate this with hospital infection data, where time-dependent covariates and competing risks are essential to the subject research question.