Institute of Neurosurgery and Chinese Evidence-Based Medicine Center and Cochrane China Center and MAGIC China Center, West China Hospital, Sichuan University, Chengdu, China.
NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, China.
J Evid Based Med. 2024 Sep;17(3):550-558. doi: 10.1111/jebm.12633. Epub 2024 Aug 6.
An important consideration when combining RCTs and NRSIs is how to address their potential biases in the pooled estimates. This study aimed to propose a Bayesian bias-adjusted random effects model for the synthesis of evidence from RCTs and NRSIs.
We present a Bayesian bias-adjusted random effects model based on power prior method, which combines the likelihood contribution of the NRSIs, raised to the power parameter of alpha, with the likelihood of the RCT data, modeled with an additive bias. The method was illustrated using a meta-analysis on the association between low-dose methotrexate exposure and melanoma. We also combined RCTs and NRSIs using the naïve data synthesis.
The results including only RCTs has a posterior median and 95% credible interval (CrI) of 1.18 (0.31-4.04), the posterior probability of any harm (> 1.0) and a meaningful association (> 1.15) were 0.61 and 0.52, respectively. The posterior median and 95% CrI based on the naïve data synthesis resulted in 1.17 (0.96-1.47), and the posterior probability of any harm and a meaningful association were 0.96 and 0.60, respectively. For the Bayesian bias-adjusted analysis, the median OR was 1.16 (95% CrI: 0.83-1.71), and the posterior probabilities of any and a meaningful clinical association were 0.88 and 0.53, respectively.
The results indicated that integrating NRSIs into meta-analysis could increase the certainty of the body of evidence. However, directly combining RCTs and NRSIs in the same meta-analysis without distinction may lead to misleading conclusions.
当合并 RCTs 和 NRSIs 时，一个重要的考虑因素是如何解决它们在汇总估计中的潜在偏倚。本研究旨在提出一种贝叶斯偏倚调整随机效应模型，用于综合来自 RCTs 和 NRSIs 的证据。
我们提出了一种基于幂先验方法的贝叶斯偏倚调整随机效应模型，该模型将 NRSIs 的似然贡献提高到参数α的幂次，并对 RCT 数据进行建模，添加了一个偏倚项。该方法通过对低剂量甲氨蝶呤暴露与黑色素瘤之间关联的荟萃分析进行了说明。我们还使用了天真数据综合法来合并 RCTs 和 NRSIs。
仅包含 RCTs 的结果具有后验中位数和 95%可信区间（CrI）为 1.18（0.31-4.04），任何危害（>1.0）和有意义关联（>1.15）的后验概率分别为 0.61 和 0.52。基于天真数据综合法的后验中位数和 95%CrI 为 1.17（0.96-1.47），任何危害和有意义关联的后验概率分别为 0.96 和 0.60。对于贝叶斯偏倚调整分析，中位数 OR 为 1.16（95%CrI：0.83-1.71），任何和有意义临床关联的后验概率分别为 0.88 和 0.53。
结果表明，将 NRSIs 纳入荟萃分析可以提高证据的确定性。然而，在同一荟萃分析中不加区分地直接合并 RCTs 和 NRSIs 可能会导致误导性的结论。
