Individual participant data from digital sources informed and improved precision in the evaluation of predictive biomarkers in Bayesian network meta-analysis
Individual participant data from digital sources informed and improved precision in the evaluation of predictive biomarkers in Bayesian network meta-analysis
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DOI：
10.1016/j.jclinepi.2023.10.018
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发表时间：
2023-11-24
影响因子：
7.2
通讯作者：
Bujkiewicz，Sylwia
中科院分区：
文献类型：
--
作者：
Umemneku-Chikere，Chinyereugo M.;Wheaton，Lorna;Bujkiewicz，Sylwia
ObjectivesWe旨在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内的治疗有效性，通过结合来自数字来源（在没有随机对照试验的情况下）的个体参与者数据（IPD）和汇总数据（AD）的证据。IPD来源于电子健康记录，使用目标试验模拟方法或数字化Kaplan-Meier曲线。该模型使用两个例子说明：乳腺癌与激素受体生物标志物，转移性结直肠癌与Kirsten大鼠肉瘤（KRAS）biomarkers.ResultsThe模型允许估计治疗效果的两个亚组的患者定义的生物标志物的状态。紫杉烷类药物在激素受体阳性和阴性乳腺癌患者中的疗效没有差异。表皮生长因子受体抑制剂在KRAS野生型结直肠癌患者中比化疗更有效，但在KRAS突变状态的患者中并非如此。IPD的使用降低了亚组特异性治疗效果估计的不确定性高达49%.ConclusionUtilization IPD允许更详细的评估预测生物标志物和癌症治疗，并提高精度的估计相比，单独使用AD。
ObjectivesWe 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 randomized controlled trials) and aggregate data (AD).Study Design and SettingA Bayesian framework was developed for modeling time-to-event data to evaluate predictive biomarkers. IPD were sourced from electronic health records, using a target trial emulation approach, or digitized 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.ResultsThe 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 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%.ConclusionUtilization 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.