Ollier Edouard, Blanchard Pierre, Le Teuff Gwénaël, Michiels Stefan
Service de Biostatistique et d'Épidémiologie, Gustave Roussy, Université Paris-Saclay, Villejuif, France.
Oncostat U1018, Inserm, Université Paris-Saclay, Labeled Ligue Contre le Cancer, Villejuif, France.
Stat Med. 2022 Jan 30;41(2):340-355. doi: 10.1002/sim.9240. Epub 2021 Oct 28.
Network meta-analysis (NMA) allows the combination of direct and indirect evidence from a set of randomized clinical trials. Performing NMA using individual patient data (IPD) is considered as a "gold standard" approach as it provides several advantages over NMA based on aggregate data. For example, it allows to perform advanced modeling of covariates or covariate-treatment interactions. An important issue in IPD NMA is the selection of influential parameters among terms that account for inconsistency, covariates, covariate-by-treatment interactions or nonproportionality of treatments effect for time to event data. This issue has not been deeply studied in the literature yet and in particular not for time-to-event data. A major difficulty is to jointly account for between-trial heterogeneity which could have a major influence on the selection process. The use of penalized generalized mixed effect model is a solution, but existing implementations have several shortcomings and an important computational cost that precludes their use for complex IPD NMA. In this article, we propose a penalized Poisson regression model to perform IPD NMA of time-to-event data. It is based only on fixed effect parameters which improve its computational cost over the use of random effects. It could be easily implemented using existing penalized regression package. Computer code is shared for implementation. The methods were applied on simulated data to illustrate the importance to take into account between trial heterogeneity during the selection procedure. Finally, it was applied to an IPD NMA of overall survival of chemotherapy and radiotherapy in nasopharyngeal carcinoma.
网络荟萃分析（NMA）能够整合一系列随机临床试验中的直接和间接证据。使用个体患者数据（IPD）进行NMA被视为一种“金标准”方法，因为与基于汇总数据的NMA相比，它具有诸多优势。例如，它允许对协变量或协变量 - 治疗相互作用进行高级建模。IPD NMA中的一个重要问题是在那些解释不一致性、协变量、协变量与治疗相互作用或事件发生时间数据的治疗效果不成比例性的项中选择有影响的参数。这个问题在文献中尚未得到深入研究，尤其是针对事件发生时间数据。一个主要困难是要共同考虑试验间的异质性，这可能对选择过程产生重大影响。使用惩罚广义混合效应模型是一种解决方案，但现有的实现方法存在几个缺点，并且计算成本很高，这使得它们无法用于复杂的IPD NMA。在本文中，我们提出一种惩罚泊松回归模型来对事件发生时间数据进行IPD NMA。它仅基于固定效应参数，与使用随机效应相比，这降低了计算成本。使用现有的惩罚回归软件包可以很容易地实现它。我们共享了实现该方法的计算机代码。这些方法应用于模拟数据，以说明在选择过程中考虑试验间异质性的重要性。最后，将其应用于鼻咽癌化疗和放疗总生存期的IPD NMA。
