Biostatistics Research Group, Department of Population Health Sciences, University of Leicester, Leicester, UK.
Statistical Innovation Group, AstraZeneca, Cambridge, UK.
Res Synth Methods. 2024 Jul;15(4):543-560. doi: 10.1002/jrsm.1707. Epub 2024 Feb 5.
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.
在药物开发过程中，可能会有证据表明某种治疗方法在特定的患者亚组中更有效。虽然早期试验可能在生物标志物混杂的人群中进行，但后期试验更有可能仅招募生物标志物阳性的患者，从而导致对同一治疗方法在不同人群中进行的试验。进行荟萃分析时，一种保守的方法是仅合并在生物标志物阳性亚组中进行的试验。然而，这会丢弃在生物标志物混杂人群中观察到的治疗效果中隐藏的生物标志物阳性亚组中治疗效果的潜在有用信息。我们将标准随机效应荟萃分析扩展到合并来自不同人群的试验的治疗效果，以估计感兴趣的生物标志物亚组中的汇总治疗效果。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果存在系统差异，该差异是根据报告了一种或两种治疗效果的试验来估计的。生物标志物混杂研究中生物标志物阴性患者的比例和系统差异用于从生物标志物混杂人群中观察到的治疗效果推断生物标志物阳性亚组中的治疗效果。所开发的方法应用于转移性结直肠癌的说明性示例，并在模拟研究中进行了评估。在该示例中，与仅对生物标志物阳性患者进行研究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的治疗效果系统差异不是很大时，所开发的方法可以在保持低偏倚的同时提高汇总治疗效果估计的精度。
