Incorporation of individual-patient data in network meta-analysis for multiple continuous endpoints, with application to diabetes treatment
Incorporation of individual-patient data in network meta-analysis for multiple continuous endpoints, with application to diabetes treatment
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DOI：
10.1002/sim.6519
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发表时间：
2015-09-10
影响因子：
2
通讯作者：
Carlin, Bradley P.
中科院分区：
文献类型：
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作者：
Hong, Hwanhee;Fu, Haoda;Carlin, Bradley P.
个人患者级别数据(IPD)的可用性扩大了网络荟萃分析(NMA)的范围，并使我们能够纳入患者级别的信息。虽然IPD在生物医学领域是一个潜在的金矿，但由于获得此类数据的机会有限，方法学的发展一直很缓慢。在本文中，我们提出了一个贝叶斯IPD NMA建模框架，在基于对比度和基于ARM的参数化下，可以同时考虑多个连续结果。我们结合了个体协变量逐个治疗的交互作用，以促进个性化决策。此外，我们可以发现使用某种药物的亚群在预测结果方面表现良好。我们还通过MCMC算法来推算缺失的单个协变量。我们使用包括连续双变量疗效结果和三个基线协变量的糖尿病数据来说明这一方法，并展示其实际意义。最后，我们以对我们的结果的讨论、对计算挑战的回顾以及对未来研究领域的简要描述作为结束。版权所有(C)2015 John Wiley&Sons，Ltd.
Availability of individual patient-level data (IPD) broadens the scope of network meta-analysis (NMA) and enables us to incorporate patient-level information. Although IPD is a potential gold mine in biomedical areas, methodological development has been slow owing to limited access to such data. In this paper, we propose a Bayesian IPD NMA modeling framework for multiple continuous outcomes under both contrast-based and arm-based parameterizations. We incorporate individual covariate-by-treatment interactions to facilitate personalized decision making. Furthermore, we can find subpopulations performing well with a certain drug in terms of predictive outcomes. We also impute missing individual covariates via an MCMC algorithm. We illustrate this approach using diabetes data that include continuous bivariate efficacy outcomes and three baseline covariates and show its practical implications. Finally, we close with a discussion of our results, a review of computational challenges, and a brief description of areas for future research. Copyright (c) 2015 John Wiley & Sons, Ltd.