surrosurv: An R package for the evaluation of failure time surrogate endpoints in individual patient data meta-analyses of randomized clinical trials
surrosurv: An R package for the evaluation of failure time surrogate endpoints in individual patient data meta-analyses of randomized clinical trials
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DOI：
10.1016/j.cmpb.2017.12.005
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发表时间：
2018-03-01
影响因子：
6.1
通讯作者：
Michiels, Stefan
中科院分区：
文献类型：
--
作者：
Rotolo, Federico;Paoletti, Xavier;Michiels, Stefan
背景和目的：由于实用方便，替代终点在临床试验中的使用更具吸引力。为了验证替代终点，当个体患者数据可用时，可以在荟萃分析的背景下估计两个重要的衡量标准：个体水平的R-INDIV(2)或Kendall‘s tau，以及试验水平的R-Trial(2)。我们的目标是提供经典的和成熟的以及更新的统计方法的R实现，用于带有失败时间终点的代孕评估。我们还打算将文献中描述的用于模型检查和可视化的实用工具以及数据生成方法结合起来。方法：在故障时间端点的情况下，经典方法基于两个步骤。首先，使用Copula模型估计Kendall‘s tau作为个体水平代孕的衡量标准。然后，通过估计的处理效果的线性回归来计算R-试验(2)；在第二步，可以通过测量误差模型或通过权重来考虑估计的不确定度。除了经典的方法外，我们最近开发了一种基于具有个体随机效应的双变量辅助泊松模型的方法来测量Kendall的tau和逐个试验的治疗交互作用来测量R试验(2)。文献中描述的最常见的数据模拟模型是基于Copula模型、混合比例风险模型以及半正态和指数随机变量的混合。结果：R包Surrosurv使用Clayton、Plack-ett和Hougaard Copula实现了经典的两步法。它还允许有选择地调整第二步线性回归的测量误差。除了完整模型外，混合泊松方法还采用了不同的简化模型。我们给出了用于估计代孕模型、检验其收敛、进行留下一次试验交叉验证以及绘制结果的包函数。我们通过对来自20个化疗试验的4069名晚期胃癌患者的荟萃分析，说明了它们在实践中的应用。结论：Surrosurv软件包为代理评估失败时间终点的经典和最新统计方法提供了R实现。根据文献中描述的方法，可以使用灵活的模拟功能来生成数据。(C)2017爱思唯尔B.V.保留所有权利。
Background and objective: Surrogate endpoints are attractive for use in clinical trials instead of well-established endpoints because of practical convenience. To validate a surrogate endpoint, two important measures can be estimated in a meta-analytic context when individual patient data are available: the R-indiv (2)or the Kendall's tau at the individual level, and the R-trial(2) at the trial level. We aimed at providing an R implementation of classical and well-established as well as more recent statistical methods for surrogacy assessment with failure time endpoints. We also intended incorporating utilities for model checking and visualization and data generating methods described in the literature to date.Methods: In the case of failure time endpoints, the classical approach is based on two steps. First, a Kendall's tau estimated as measure of individual level surrogacy using a copula model. Then, the R-trial(2) is computed via a linear regression of the estimated treatment effects; at this second step, the estimation uncertainty can be accounted for via measurement-error model or via weights. In addition to the classical approach, we recently developed an approach based on bivariate auxiliary Poisson models with individual random effects to measure the Kendall's tau and treatment-by-trial interactions to measure the R-trial(2). The most common data simulation models described in the literature are based on: copula models, mixed proportional hazard models, and mixture of half-normal and exponential random variables.Results: The R package surrosurv implements the classical two-step method with Clayton, Plack-ett, and Hougaard copulas. It also allows to optionally adjusting the second-step linear regression for measurement-error. The mixed Poisson approach is implemented with different reduced models in addition to the full model. We present the package functions for estimating the surrogacy models, for checking their convergence, for performing leave-one-trial-out cross-validation, and for plotting the results. We illustrate their use in practice on individual patient data from a meta-analysis of 4069 patients with advanced gastric cancer from 20 trials of chemotherapy.Conclusions: The surrosurv package provides an R implementation of classical and recent statistical methods for surrogacy assessment of failure time endpoints. Flexible simulation functions are available to generate data according to the methods described in the literature. (c) 2017 Elsevier B.V. Allrights reserved.