Department of Statistics, Florida State University, 411 OSB, 117 N Woodward Ave, Tallahassee, FL, 32306, USA.
Department of Biostatistics, Harvard University, Boston, MA, USA.
BMC Med Res Methodol. 2021 Oct 17;21(1):213. doi: 10.1186/s12874-021-01397-5.
Network meta-analysis (NMA) is a widely used tool to compare multiple treatments by synthesizing different sources of evidence. Measures such as the surface under the cumulative ranking curve (SUCRA) and the P-score are increasingly used to quantify treatment ranking. They provide summary scores of treatments among the existing studies in an NMA. Clinicians are frequently interested in applying such evidence from the NMA to decision-making in the future. This prediction process needs to account for the heterogeneity between the existing studies in the NMA and a future study.
This article introduces the predictive P-score for informing treatment ranking in a future study via Bayesian models. Two NMAs were used to illustrate the proposed measure; the first assessed 4 treatment strategies for smoking cessation, and the second assessed treatments for all-grade treatment-related adverse events. For all treatments in both NMAs, we obtained their conventional frequentist P-scores, Bayesian P-scores, and predictive P-scores.
In the two examples, the Bayesian P-scores were nearly identical to the corresponding frequentist P-scores for most treatments, while noticeable differences existed for some treatments, likely owing to the different assumptions made by the frequentist and Bayesian NMA models. Compared with the P-scores, the predictive P-scores generally had a trend to converge toward a common value of 0.5 due to the heterogeneity. The predictive P-scores' numerical estimates and the associated plots of posterior distributions provided an intuitive way for clinicians to appraise treatments for new patients in a future study.
The proposed approach adapts the existing frequentist P-score to the Bayesian framework. The predictive P-score can help inform medical decision-making in future studies.
网络荟萃分析（NMA）是一种广泛用于通过综合不同来源证据来比较多种治疗方法的工具。表面下累积排序曲线面积（SUCRA）和 P 评分等措施越来越多地用于量化治疗排序。它们提供了 NMA 中现有研究中治疗方法的综合评分。临床医生经常有兴趣将来自 NMA 的此类证据应用于未来的决策制定。该预测过程需要考虑 NMA 中现有研究与未来研究之间的异质性。
本文通过贝叶斯模型介绍了用于通过预测性 P 评分来告知未来研究中治疗方法排序的方法。使用两个 NMA 来说明所提出的措施；第一个评估了戒烟的 4 种治疗策略，第二个评估了所有级别治疗相关不良事件的治疗方法。对于两个 NMA 中的所有治疗方法，我们获得了它们的传统频率 P 评分、贝叶斯 P 评分和预测性 P 评分。
在这两个示例中，对于大多数治疗方法，贝叶斯 P 评分与相应的频率 P 评分几乎相同，而对于一些治疗方法，存在明显差异，这可能是由于频率和贝叶斯 NMA 模型的不同假设所致。与 P 评分相比，由于异质性，预测性 P 评分通常趋于收敛到 0.5 的共同值。预测性 P 评分的数值估计及其后验分布的相关图为临床医生在未来研究中为新患者评估治疗方法提供了直观的方法。
所提出的方法将现有的频率 P 评分适应到贝叶斯框架中。预测性 P 评分可以帮助为未来的研究提供医学决策信息。
