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

@article{citeulike:3032055, abstract = {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.}, address = {Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, P.O. Box 9604, 2300 RC, Leiden, The Netherlands.}, author = {Fiocco, Marta and Putter, Hein and van Houwelingen, Hans C C. }, citeulike-article-id = {3032055}, doi = {http://dx.doi.org/10.1002/sim.3305}, issn = {0277-6715}, journal = {Statistics in medicine}, keywords = {cox, recurrent}, month = {April}, posted-at = {2008-07-22 09:22:30}, priority = {2}, title = {Reduced-rank proportional hazards regression and simulation-based prediction for multi-state models.}, url = {http://dx.doi.org/10.1002/sim.3305}, year = {2008} }

TY - JOUR ID - citeulike:3032055 L3 - citeulike-article-id:3032055 TI - Reduced-rank proportional hazards regression and simulation-based prediction for multi-state models. JF - Statistics in medicine AD - Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, P.O. Box 9604, 2300 RC, Leiden, The Netherlands. SN - 0277-6715 N2 - 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. KW - cox KW - recurrent AU - Fiocco, Marta AU - Putter, Hein AU - van Houwelingen, Hans C PY - 2008/04/21/ UR - http://dx.doi.org/10.1002/sim.3305 ER -