aDivision of Biostatistics, University of Minnesota School of Public Health, Minneapolis, MN, USA.
Clin Trials. 2014 Apr;11(2):246-62. doi: 10.1177/1740774513498322. Epub 2013 Oct 3.
In the absence of sufficient data directly comparing multiple treatments, indirect comparisons using network meta-analyses (NMAs) can provide useful information. Under current contrast-based (CB) methods for binary outcomes, the patient-centered measures including the treatment-specific event rates and risk differences (RDs) are not provided, which may create some unnecessary obstacles for patients to comprehensively trade-off efficacy and safety measures.
We aim to develop NMA to accurately estimate the treatment-specific event rates.
A Bayesian hierarchical model is developed to illustrate how treatment-specific event rates, RDs, and risk ratios (RRs) can be estimated. We first compare our approach to alternative methods using two hypothetical NMAs assuming a fixed RR or RD, and then use two published NMAs to illustrate the improved reporting.
In the hypothetical NMAs, our approach outperforms current CB NMA methods in terms of bias. In the two published NMAs, noticeable differences are observed in the magnitude of relative treatment effects and several pairwise statistical significance tests from previous report.
First, to facilitate the estimation, each study is assumed to hypothetically compare all treatments, with unstudied arms being missing at random. It is plausible that investigators may have selected treatment arms on purpose based on the results of previous trials, which may lead to 'nonignorable missingness' and potentially bias our estimates. Second, we have not considered methods to identify and account for potential inconsistency between direct and indirect comparisons.
The proposed NMA method can accurately estimate treatment-specific event rates, RDs, and RRs and is recommended.
在缺乏直接比较多种治疗方法的充分数据的情况下，使用网络荟萃分析（NMA）进行间接比较可以提供有用的信息。在目前基于对照（CB）的二元结局方法中，不提供以患者为中心的措施，包括治疗特异性事件发生率和风险差异（RDs），这可能会给患者全面权衡疗效和安全性措施带来一些不必要的障碍。
我们旨在开发 NMA 来准确估计治疗特异性事件发生率。
开发了一个贝叶斯层次模型来说明如何估计治疗特异性事件发生率、RDs 和风险比（RRs）。我们首先使用两个假设的 NMA 来比较我们的方法与替代方法，一个假设 RR，另一个假设 RD，然后使用两个已发表的 NMA 来说明改进的报告。
在假设的 NMA 中，我们的方法在偏差方面优于当前的 CB NMA 方法。在两个已发表的 NMA 中，从以前的报告中观察到相对治疗效果的大小和几个成对统计显著性检验有明显差异。
首先，为了便于估计，假设每个研究都假设将所有治疗方法进行比较，未研究的手臂随机缺失。研究人员可能会根据先前试验的结果故意选择治疗臂，这可能导致“不可忽略的缺失”，并可能使我们的估计产生偏差，这是合理的。其次，我们尚未考虑用于识别和处理直接和间接比较之间潜在不一致性的方法。
建议使用所提出的 NMA 方法准确估计治疗特异性事件发生率、RDs 和 RR。
