Comparing predictions among competing risks models with time-dependent covariates

Competing risks models and time-dependent covariates

New statistical models for analysing survival data in an intensive care unit context have recently been developed. Two models that offer significant advantages over standard survival analyses are competing risks models and multistate models. Wolkewitz and colleagues used a competing risks model to examine survival times for nosocomial pneumonia and mortality. Their model was able to incorporate...

"Choice of prediction models for competing risks with time-dependent covariates"

We discuss prediction models for competing risks cumulative incidence in presence of internal time-dependent covariates [1], and propose a statistical procedure in order to choose the best model in terms of prediction accuracy. A common aim is to identify statistical models that accurately predict the cumulative risks for individual subjects and those covariates that provide higher predictive a...

Space-time models with time-dependent covariates

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

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 summar...

Missing covariates in competing risks analysis

Studies often follow individuals until they fail from one of a number of competing failure types. One approach to analyzing such competing risks data involves modeling the cause-specific hazards as functions of baseline covariates. A common issue that arises in this context is missing values in covariates. In this setting, we first establish conditions under which complete case analysis (CCA) i...