Bayesian meta-analysis for evaluating treatment effectiveness in biomarker subgroups using trials of mixed patient populations
利用混合人群试验评估生物标志物亚组疗效的贝叶斯元分析AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要通过整合不同试验人群的信息估计亚组治疗效应，因此归入因果推断中的治疗效应估计。
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利用混合人群试验评估生物标志物亚组疗效的贝叶斯元分析
- 研究问题
- 当同一治疗的试验分别覆盖标志物阳性人群和混合人群时，如何利用全部试验信息估计目标亚组的合并治疗效应？
- 方法与思路
- 作者扩展随机效应元分析，估计阳性与阴性亚组效应的系统差异，再结合混合试验中的阴性比例推算阳性亚组效应。
- 材料陈述的贡献
- 摘要称，结直肠癌示例中方法提高了估计精度；模拟显示亚组效应差异不大时，可在维持低偏差的同时改善精度。
- 阅读关注点
- 应核对亚组效应如何在混合人群中聚合、系统差异的跨试验假设、先验设定，以及比例和亚组信息缺失的处理。
- 阅读边界
- 这是arXiv预印本，同行评审或接收未核实；摘要未提供完整模型和误设分析，精度改善仅限所述示例及模拟条件。
原文摘要
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 enrol 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 estimated 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 resulted in improved precision of the pooled treatment effect estimate compared to 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.