Examining the Reliability of Model-Based Meta-Analysis (MBMA) Outcomes: A Simulation Study.

IF 3.1 3区 医学 Q2 PHARMACOLOGY & PHARMACY
Jiesen Yu, Ting Li, Jieren Luo, Qingshan Zheng, Lujin Li
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引用次数: 0

Abstract

Model-based meta-analysis (MBMA) can be utilized to synthesize literature data and predict drug efficacy, particularly suitable for constructing external comparator arms for non-randomized controlled trials (NRCTs). This study evaluated the reliability of MBMA by comparing covariate models generated through MBMA to individual patient data. A pharmacodynamic covariate model, commonly employed in MBMA, was used to set true parameter values and simulate data across various scenarios. The reliability of MBMA models was assessed by comparing estimated to true parameter values and identifying optimal conditions for MBMA use. Linear and nonlinear covariate models were evaluated in 24 scenarios, focusing on the relative deviations of parameter estimates from their true values. Evaluation metrics included minimization successful rate, covariate introduction rate, and the accuracy of parameters such as Emax, ET50, and covariate influences. Both model types showed similar reliability in most scenarios. Notably, model performance significantly improved when the number of included trials was 10 or more, the distribution of covariates exceeded 66.6% of its median, and the covariate impact coefficient was greater than 0.15. The study identified critical factors and thresholds that influence the accuracy of MBMA modeling. Enhanced accuracy in synthetic control analysis using MBMA was achieved under specified conditions, highlighting the effectiveness of MBMA in NRCT applications.

基于模型的meta分析(MBMA)结果的可靠性检验:一项模拟研究。
基于模型的meta分析(MBMA)可用于综合文献数据和预测药物疗效,特别适用于构建非随机对照试验(NRCTs)的外部比较组。本研究通过比较由MBMA生成的协变量模型与个体患者数据来评估MBMA的可靠性。使用MBMA常用的药效学协变量模型设置真实参数值并模拟不同情况下的数据。通过比较估计参数值与真实参数值并确定MBMA使用的最佳条件来评估MBMA模型的可靠性。在24种情况下对线性和非线性协变量模型进行了评估,重点关注参数估计值与其真实值的相对偏差。评估指标包括最小化成功率、协变量引入率和参数的准确性,如Emax、ET50和协变量影响。在大多数情况下,两种模型类型显示出相似的可靠性。值得注意的是,当纳入试验数为10或更多,协变量分布超过其中位数的66.6%,协变量影响系数大于0.15时,模型性能显著提高。研究确定了影响MBMA模型准确性的关键因素和阈值。在特定条件下,利用MBMA提高了综合控制分析的准确性,突出了MBMA在NRCT应用中的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
5.00
自引率
11.40%
发文量
146
审稿时长
8 weeks
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