We introduce a statistical procedure that integrates datasets from multiple biomedical studies to predict patients' survival, based on individual clinical and genomic profiles. The proposed procedure accounts for potential differences in the relation between predictors and outcomes across studies, due to distinct patient populations, treatments and technologies to measure outcomes and biomarkers. These differences are modeled explicitly with study-specific parameters. We use hierarchical regularization to shrink the study-specific parameters towards each other and to borrow information across studies. The estimation of the study-specific parameters utilizes a similarity matrix, which summarizes differences and similarities of the relations between covariates and outcomes across studies. We illustrate the method in a simulation study and using a collection of gene expression datasets in ovarian cancer. We show that the proposed model increases the accuracy of survival predictions compared to alternative meta-analytic methods.

中文翻译：

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整合来自多项研究的生存数据

我们引入了一种统计程序，该程序整合了多项生物医学研究的数据集，以根据个体临床和基因组概况预测患者的存活率。由于不同的患者群体、治疗和测量结果和生物标志物的技术，拟议的程序解释了研究中预测因子和结果之间关系的潜在差异。这些差异是用特定于研究的参数明确建模的。我们使用分层正则化来缩小研究特定的参数，并在研究之间借用信息。研究特定参数的估计使用相似性矩阵，该矩阵总结了研究中协变量和结果之间关系的差异和相似性。我们在模拟研究中说明了该方法，并在卵巢癌中使用了一组基因表达数据集。我们表明，与替代元分析方法相比，所提出的模型提高了生存预测的准确性。