我们的目标是通过结合来自数字来源（缺乏随机对照试验）的个体参与者数据（IPD）和汇总数据（AD）的证据，开发一个网络荟萃分析模型，用于评估预测生物标志物亚组中的治疗效果。 ：开发了贝叶斯框架，用于对事件时间数据进行建模以评估预测生物标志物。 IPD 源自电子健康记录，使用目标试验仿真方法或数字化 Kaplan-Meier 曲线。该模型使用两个示例进行说明：具有激素受体生物标志物的乳腺癌和具有克尔斯滕大鼠肉瘤 (KRAS) 生物标志物的转移性结直肠癌。该模型可以估计根据生物标志物状态定义的两个患者亚组的治疗效果。紫杉烷类药物的有效性在激素受体阳性和阴性乳腺癌患者中没有差异。对于 KRAS 野生型结直肠癌患者，表皮生长因子受体 (EGFR) 抑制剂比化疗更有效，但对于 KRAS 突变状态患者则不然。使用 IPD 可以将亚组特异性治疗效果估计的不确定性降低高达 49%。与单独使用 AD 相比，IPD 的使用可以对预测生物标志物和癌症治疗进行更详细的评估，并提高估计的精确度。版权所有 © 2023。由爱思唯尔公司出版

We aimed to develop a network meta-analytic model for the evaluation of treatment effectiveness within predictive biomarker subgroups, by combining evidence from individual participant data (IPD) from digital sources (in the absence of RCTs) and aggregate data (AD).And Setting: A Bayesian framework was developed for modelling time-to-event data to evaluate predictive biomarkers. IPD were sourced from electronic health records, using a target trial emulation approach, or digitised Kaplan-Meier curves. The model is illustrated using two examples: breast cancer with a hormone receptor biomarker, and metastatic colorectal cancer with the Kirsten Rat Sarcoma (KRAS) biomarker.The model allowed for the estimation of treatment effects in two subgroups of patients defined by their biomarker status. Effectiveness of taxanes did not differ in hormone receptor positive and negative breast cancer patients. Epidermal growth factor receptor (EGFR) inhibitors were more effective than chemotherapy in KRAS wild type colorectal cancer patients but not in patients with KRAS mutant status. Use of IPD reduced uncertainty of the subgroup-specific treatment effect estimates by up to 49%.Utilisation of IPD allowed for more detailed evaluation of predictive biomarkers and cancer therapies and improved precision of the estimates compared to use of AD alone.Copyright © 2023. Published by Elsevier Inc.