Uncertainty in the Bayesian meta-analysis of normally distributed surrogate endpoints.
Uncertainty in the Bayesian meta-analysis of normally distributed surrogate endpoints.
复制标题
DOI：
10.1177/0962280215597260
复制
发表时间：
2017-10
影响因子：
2.3
通讯作者：
Abrams KR
中科院分区：
文献类型：
--
作者：
Bujkiewicz S;Thompson JR;Spata E;Abrams KR
我们研究了元分析模型参数化的选择和相关不确定性对替代终点验证的影响。不同的荟萃分析方法考虑了不同水平的不确定性，这些不确定性可能会影响从这些模型获得的替代终点的治疗效果预测目标结局的治疗效果的准确性。使用复发缓解型多发性硬化症的说明性示例实施了一系列贝叶斯和频率主义荟萃分析方法，其中对残疾恶化的治疗效果是医疗保健评估中感兴趣的主要结局，而对复发率的影响被认为是对残疾进展的影响的潜在替代，并且在胃癌中，其中无病生存期已被证明是总生存期的良好替代终点。进行敏感度分析以评估分布假设对预测的影响。此外，灵敏度建模假设和性能的模型进行了研究模拟。虽然不同的方法可以预测平均真实结果几乎同样好，包括周围的所有相关参数的模型的不确定性可能会导致不太确定，因此更保守的预测。当研究终点作为候选替代结果时，必须仔细选择荟萃分析方法。低估现有证据的不确定性的模型可能会导致过于乐观的预测，这可能会影响基于这些预测做出的决策。
We investigate the effect of the choice of parameterisation of meta-analytic models and related uncertainty on the validation of surrogate endpoints. Different meta-analytical approaches take into account different levels of uncertainty which may impact on the accuracy of the predictions of treatment effect on the target outcome from the treatment effect on a surrogate endpoint obtained from these models. A range of Bayesian as well as frequentist meta-analytical methods are implemented using illustrative examples in relapsing–remitting multiple sclerosis, where the treatment effect on disability worsening is the primary outcome of interest in healthcare evaluation, while the effect on relapse rate is considered as a potential surrogate to the effect on disability progression, and in gastric cancer, where the disease-free survival has been shown to be a good surrogate endpoint to the overall survival. Sensitivity analysis was carried out to assess the impact of distributional assumptions on the predictions. Also, sensitivity to modelling assumptions and performance of the models were investigated by simulation. Although different methods can predict mean true outcome almost equally well, inclusion of uncertainty around all relevant parameters of the model may lead to less certain and hence more conservative predictions. When investigating endpoints as candidate surrogate outcomes, a careful choice of the meta-analytical approach has to be made. Models underestimating the uncertainty of available evidence may lead to overoptimistic predictions which can then have an effect on decisions made based on such predictions.