Reduced-rank proportional hazards regression and simulation-based prediction for multi-state models
Reduced-rank proportional hazards regression and simulation-based prediction for multi-state models
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DOI：
10.1002/sim.3305
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发表时间：
2008-09-20
影响因子：
2
通讯作者：
van Houwelingen, Hans C.
中科院分区：
文献类型：
--
作者：
Fiocco, Marta;Putter, Hein;van Houwelingen, Hans C.
在本文中，我们解决了两个问题产生的多状态模型与协变量。第一个问题是如何在协变量影响的建模中获得简约性。在多状态模型中纳入协变量的标准方法是将转换视为单独的构建块，并对每个转换分别建模协变量的影响，通常通过转换风险的比例风险模型。这通常会导致需要估计大量的回归系数，并且存在过度拟合的真正危险，特别是当存在少量事件的转换时。为了处理这个问题，我们将之前在竞争风险背景下提出的降阶思想扩展到多状态模型中。本文讨论的第二个问题的动机是希望获得降阶模型回归系数的标准误差。我们提出了一种基于模型的重采样技术。在重复采样轨迹上，通过多状态模型。同样的思想也用于估计一般多状态模型的预测概率和相关的标准误差。我们使用欧洲血液和骨髓移植组织的数据来说明我们的技术。版权所有(c) 2008约翰威利父子有限公司
In this paper we address two issues arising 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 seperate building blocks, and modeling the effect of covariates for each transition seperately, 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 techniques. on repeatedly sampling trajectories, through the multi-state model. The same ideas are also used for the estimation of predicition 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.