Multilevel mixed effects parametric survival models using adaptive Gauss-Hermite quadrature with application to recurrent events and individual participant data meta-analysis
Multilevel mixed effects parametric survival models using adaptive Gauss-Hermite quadrature with application to recurrent events and individual participant data meta-analysis
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DOI：
10.1002/sim.6191
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发表时间：
2014-09-28
影响因子：
2
通讯作者：
Riley, Richard D.
中科院分区：
文献类型：
--
作者：
Crowther, Michael J.;Look, Maxime P.;Riley, Richard D.
多水平混合效应生存模型用于重复事件、多中心临床试验和个体参与者数据(IPD)荟萃分析等集群生存数据的分析，以考察基线风险和协变量效应的异质性。在本文中，我们扩展了参数脆性模型，包括指数、威布尔和Gompertz比例风险(PH)模型，以及对数Logistic、对数正态和广义伽马加速失效时间模型，以允许任意数量的正态分布的随机效应。此外，我们扩展了Royston和Parmar的灵活参数生存模型，该模型是在对数累积风险尺度上使用受限三次样条建立的，在考虑随机效应的同时也考虑了非依赖于时间的效应。最大似然法用于估计采用自适应或非自适应Gauss-Hermite求积的模型。通过多中心临床试验和IPD荟萃分析的模拟研究对这些方法进行了评估，显示了该估计方法的良好性能。灵活的参数混合效应模型使用肾脏疾病患者和重复感染次数的数据集以及乳腺癌患者预后因素研究的IPD荟萃分析进行了说明。提供了用户友好的STATA软件来实现这些方法。版权所有(C)2014 John Wiley&Sons，Ltd.
Multilevel mixed effects survival models are used in the analysis of clustered survival data, such as repeated events, multicenter clinical trials, and individual participant data (IPD) meta-analyses, to investigate heterogeneity in baseline risk and covariate effects. In this paper, we extend parametric frailty models including the exponential, Weibull and Gompertz proportional hazards (PH) models and the log logistic, log normal, and generalized gamma accelerated failure time models to allow any number of normally distributed random effects. Furthermore, we extend the flexible parametric survival model of Royston and Parmar, modeled on the log-cumulative hazard scale using restricted cubic splines, to include random effects while also allowing for non-PH (time-dependent effects). Maximum likelihood is used to estimate the models utilizing adaptive or nonadaptive Gauss-Hermite quadrature. The methods are evaluated through simulation studies representing clinically plausible scenarios of a multicenter trial and IPD meta-analysis, showing good performance of the estimation method. The flexible parametric mixed effects model is illustrated using a dataset of patients with kidney disease and repeated times to infection and an IPD meta-analysis of prognostic factor studies in patients with breast cancer. User-friendly Stata software is provided to implement the methods. Copyright (C) 2014 John Wiley & Sons, Ltd.