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.

翻译：在药物开发过程中，可能出现证据表明某种治疗在特定患者亚组中更有效。尽管早期试验可能在生物标志物混合人群中进行，但后期试验更可能仅招募生物标志物阳性患者，从而导致同一疗法在不同人群中的试验。进行荟萃分析时，保守方法可能仅合并生物标志物阳性亚组中的试验结果。然而，这丢弃了隐藏在生物标志物混合人群观察到的治疗效果中、关于生物标志物阳性亚组治疗效果的潜在有用信息。我们扩展标准随机效应荟萃分析，以合并来自不同人群试验的治疗效果，从而估计目标生物标志物亚组的合并治疗效果。该模型假设生物标志物阳性与阴性亚组间存在系统性治疗效果差异，并通过报告任一或两种治疗效果的研究来估计该差异。利用估计的系统性差异及生物标志物混合研究中阴性患者比例，可从生物标志物混合人群的观察治疗效果中插值得到阳性亚组的治疗效果。将所开发方法应用于转移性结直肠癌的示例，并通过模拟研究进行评估。示例中，与仅针对生物标志物阳性患者试验的标准随机效应荟萃分析相比，本方法提高了合并治疗效果估计的精度。模拟研究证实，当生物标志物亚组间治疗效果的系统性差异不大时，本方法可在保持低偏倚的同时提高合并治疗效果估计的精度。