Calibration of multiple treatment comparisons using individual patient data

在综合所有比较两种或两种以上治疗的试验的基础上，已经有了比较多种治疗的成熟方法。这些“网络荟萃分析”方法通常用于NICE评估、NICE临床指南和一般的卫生技术评估，并且在Cochrane协作综述中使用的频率越来越高。然而，这些方法确实假设在纳入的试验中效果修饰因子的分布是相同的。这样的假设很难验证，但如果不满足这些假设，结果很可能是有偏差的。因此，最近出现了一些解决这些问题的方法，通过使用一个试验的IPD来调整治疗间比较。这些方法越来越多地出现在应用卫生技术评估文献中，也出现在向NICE提交的材料中。它们对制造商特别有吸引力，因为制造商可以从自己的试验中获得IPD，但无法获得竞争对手产品的试验。已发表的方法范围有限，其可靠性也未得到评估。更令人担忧的是，它们也被应用于非随机（单臂）研究：这是对标准实践的彻底背离，因为它做出的假设一直被认为是不可信的。通过模拟研究和来自行业合作伙伴的真实案例，我们建议开发和评估新方法，这些方法可以使用来自一个或多个试验的IPD来调整整个比较网络中的治疗比较，并提供与任何特定目标人群相关的改进的比较有效性估计。我们将评估和比较我们计划用标准方法开发的方法的可靠性，以及现在出现在文献中的方法，我们将使用实际数据检查不同方法所做的各种假设的有效性。

There are well established methods for comparing multiple treatments based on a synthesis of all the trials that compare any two or more of them. These "network meta-analysis" methods are routinely used in NICE appraisals, NICE Clinical Guidelines, health technology assessment in general, and with growing frequency in Cochrane Collaboration reviews. These methods do, however, assume that the distribution of effect modifiers is the same in the included trials. Such assumptions are difficult to verify, but if they are not met results are likely to be biased. As a result, there has been recent interest in methods that appear to address these concerns by using IPD from one trial to adjust the between-treatment comparisons. These methods are being seen with increasing frequency in the applied health technology assessment literature, and also in submissions to NICE. They are particularly attractive to manufacturers, who have access to IPD from their own trials but not to trials on competitor products. The methods that have been published have limited scope, and their reliability has not been assessed. Of still greater concern is that they are also being applied to non-randomised (one-arm) studies: this is a radical departure from standard practice because it makes assumptions that have always been regarded as implausible.Using simulation studies, and real examples from industry partners, we propose to develop and evaluate new methods that can use IPD from one or more trials to adjust treatment comparisons across an entire network of comparisons, and to provide improved comparative effectiveness estimates that are relevant to any specified target population. We will assess and compare the reliability of or the methods we plan to develop with standard methods, and with the methods now appearing in the literature, and we will check the validity of the various assumptions made by the different methods, using real data.