Reduced-rank proportional hazards regression and simulation-based prediction for multi-state models.

Marta Fiocco — 2008-07-22T08:22:16-00:00

Statistics in medicine (21 April 2008)

In this paper we address two issues arising in multi-state models with covariates. The first issue deals with how to obtain parsimony in the modeling of the effect of covariates. The standard way of incorporating covariates in multi-state models is by considering the transitions as separate building blocks, and modeling the effect of covariates for each transition separately, usually through a proportional hazards model for the transition hazard. This typically leads to a large number of regression coefficients to be estimated, and there is a real danger of over-fitting, especially when transitions with few events are present. We extend the reduced-rank ideas, proposed earlier in the context of competing risks, to multi-state models, in order to deal with this issue.The second issue addressed in this paper was motivated by the wish to obtain standard errors of the regression coefficients of the reduced-rank model. We propose a model-based resampling technique, based on repeatedly sampling trajectories through the multi-state model. The same ideas are also used for the estimation of prediction probabilities in general multi-state models and associated standard errors.We use data from the European Group for Blood and Marrow Transplantation to illustrate our techniques. Copyright (c) 2008 John Wiley & Sons, Ltd.

Tutorial in biostatistics: competing risks and multi-state models.

H Putter — 2007-06-23T09:54:30-00:00

Stat Med, Vol. 26, No. 11. (20 May 2007), pp. 2389-2430.

Standard survival data measure the time span from some time origin until the occurrence of one type of event. If several types of events occur, a model describing progression to each of these competing risks is needed. Multi-state models generalize competing risks models by also describing transitions to intermediate events. Methods to analyze such models have been developed over the last two decades. Fortunately, most of the analyzes can be performed within the standard statistical packages, but may require some extra effort with respect to data preparation and programming. This tutorial aims to review statistical methods for the analysis of competing risks and multi-state models. Although some conceptual issues are covered, the emphasis is on practical issues like data preparation, estimation of the effect of covariates, and estimation of cumulative incidence functions and state and transition probabilities. Examples of analysis with standard software are shown.

CiteULike: jyuh's Putter

Reduced-rank proportional hazards regression and simulation-based prediction for multi-state models.

Tutorial in biostatistics: competing risks and multi-state models.