Generalised linear models for correlated pseudo-observations, with applications to multi-state models
Generalised linear models for correlated pseudo-observations, with applications to multi-state models
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DOI：
10.1093/biomet/90.1.15
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发表时间：
2003-03-01
期刊：
影响因子：
2.7
通讯作者：
Rosthoj, S
中科院分区：
文献类型：
--
作者：
Andersen, PK;Klein, JP;Rosthoj, S
在多状态模型中，回归分析通常涉及对每个过渡强度分别建模。每个感兴趣的概率，即受试者在某一时刻处于给定状态的概率，是强度回归系数的复杂非线性函数。我们提出了一种直接对状态概率建模的方法。该方法基于由状态概率的简单汇总统计估计构造的叠刀统计的伪值。然后将这些伪值用于广义估计方程，以获得模型参数的估计。我们通过研究常见回归问题的例子来说明这种技术是如何工作的。我们将该技术应用于骨髓移植急性移植物抗宿主病模型。
In multi-state-models regression analysis typically involves the modelling of each transition intensity separately. Each probability of interest, namely the probability that a subject will be in a given state at some time, is a complex nonlinear function of the intensity regression coefficients. We present a technique which models the state probabilities directly. This method is based on the pseudo-values from a jackknife statistic constructed from simple summary statistic estimates of the state probabilities. These pseudo-values are then used in a generalised estimating equation to obtain estimates of the model parameters. We illustrate how this technique works by studying examples of common regression problems. We apply the technique to model acute graft-versus-host disease in bone marrow transplants.