posted on 2016-12-13, 15:07authored byPaul C. Lambert, S. R. Wilkes, Michael J. Crowther
Competing risks arise with time-to-event data when individuals are at risk of more than one type of event and the
occurrence of one event precludes the occurrence of all other events. A useful measure with competing risks is the
cause-specific cumulative incidence function (CIF), which gives the probability of experiencing a particular event
as a function of follow-up time, accounting for the fact that some individuals may have a competing event. When
modelling the cause-specific CIF, the most common model is a semi-parametric proportional subhazards model. In
this paper we propose the use of flexible parametric survival models to directly model the cause-specific CIF where
the effect of follow-up time is modelled using restricted cubic splines. The models provide smooth estimates of the
cause-specific CIF with the important advantage that the approach is easily extended to model time-dependent
effects. The models can be fitted using standard survival analysis tools by a combination of data expansion and
introducing time-dependent weights. Various link functions are available that allow modelling on different scales
and have proportional subhazards, proportional odds and relative absolute risks as particular cases. We conduct
a simulation study to evaluate how well the spline functions approximate subhazard functions with complex
shapes. The methods are illustrated using data from the European Blood and Marrow Transplantation Registry
showing excellent agreement between parametric estimates of the cause-specific CIF and those obtained from a
semi-parametric model. We also fit models relaxing the proportional subhazards assumption using alternative link
functions and/or including time-dependent effects.
History
Citation
Statistics in Medicine, 2016
Author affiliation
/Organisation/COLLEGE OF MEDICINE, BIOLOGICAL SCIENCES AND PSYCHOLOGY/School of Medicine/Department of Health Sciences
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