Bayesian meta-analysis for evaluating treatment effectiveness in biomarker subgroups using trials of mixed patient populations
Bayesian meta-analysis for evaluating treatment effectiveness in biomarker subgroups using trials of mixed patient populations
复制标题
使用混合患者群体的试验评估生物标志物亚组治疗效果的贝叶斯荟萃分析
DOI：
10.1002/jrsm.1707
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发表时间：
2024
影响因子：
9.8
通讯作者：
Wheaton L
中科院分区：
文献类型：
--
作者：
Wheaton L
在药物开发过程中，可能会出现证据表明治疗在特定患者亚组中更有效。虽然早期试验可能在生物标志物混合人群中进行，但后期试验更有可能单独招募生物标志物阳性患者，从而导致在不同人群中研究相同治疗的试验。在进行荟萃分析时，保守的方法是仅将在生物标志物阳性亚组中进行的试验联合收割机合并。然而，这排除了在生物标志物混合人群中观察到的治疗效应中隐藏的生物标志物阳性亚组治疗效应的潜在有用信息。我们扩展了标准随机效应荟萃分析，将从不同人群的试验中获得的联合收割机治疗效应结合起来，以估计感兴趣的生物标志物亚组的汇总治疗效应。该模型假设生物标志物阳性和生物标志物阴性亚组之间的治疗效应存在系统性差异，这是根据报告其中一种或两种治疗效应的试验估计的。使用生物标志物混合研究中生物标志物阴性患者的系统性差异和比例，根据生物标志物混合人群中观察到的治疗效应，对生物标志物阳性亚组中的治疗效应进行插值。所开发的方法应用于转移性结直肠癌的一个说明性的例子，并在模拟研究中进行评估。在该示例中，与仅调查生物标志物阳性患者的试验的标准随机效应荟萃分析相比，所开发的方法提高了汇总治疗效果估计的精度。模拟研究证实，当生物标志物亚组之间治疗效应的系统性差异不是很大时，所开发的方法可以提高合并治疗效应估计的精度，同时保持低偏倚。
During drug development, evidence can emerge to suggest a treatment is more effective in a specific patient subgroup. Whilst early trials may be conducted in biomarker‐mixed populations, later trials are more likely to enroll biomarker‐positive patients alone, thus leading to trials of the same treatment investigated in different populations. When conducting a meta‐analysis, a conservative approach would be to combine only trials conducted in the biomarker‐positive subgroup. However, this discards potentially useful information on treatment effects in the biomarker‐positive subgroup concealed within observed treatment effects in biomarker‐mixed populations. We extend standard random‐effects meta‐analysis to combine treatment effects obtained from trials with different populations to estimate pooled treatment effects in a biomarker subgroup of interest. The model assumes a systematic difference in treatment effects between biomarker‐positive and biomarker‐negative subgroups, which is estimated from trials which report either or both treatment effects. The systematic difference and proportion of biomarker‐negative patients in biomarker‐mixed studies are used to interpolate treatment effects in the biomarker‐positive subgroup from observed treatment effects in the biomarker‐mixed population. The developed methods are applied to an illustrative example in metastatic colorectal cancer and evaluated in a simulation study. In the example, the developed method improved precision of the pooled treatment effect estimate compared with standard random‐effects meta‐analysis of trials investigating only biomarker‐positive patients. The simulation study confirmed that when the systematic difference in treatment effects between biomarker subgroups is not very large, the developed method can improve precision of estimation of pooled treatment effects while maintaining low bias.