Tree-based exploratory identification of predictive biomarkers in observational data
基于树的观察性数据预测性生物标志物探索识别AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要明确研究观察性数据中的混杂调整、治疗效应异质性及预测因素识别。
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基于树的观察性数据预测性生物标志物探索识别
- 研究问题
- 当只有临床登记或观察性数据时，如何在存在混杂的情况下识别解释治疗效应异质性的预测性生物标志物？
- 方法与思路
- 作者将专门搜索预测因素的树方法 predMOB 与协变量调整、匹配和逆概率治疗加权结合，并开展模拟及乳腺癌试验数据应用。
- 材料陈述的贡献
- 摘要称，在所考察模拟中，协变量调整能够识别预测因素，而预测因素与混杂机制相关时加权可能失败；联合调整可能更稳健。
- 阅读关注点
- 应核对混杂机制与预测因素如何设定、树的搜索和选择规则、匹配结果，以及联合方法更稳健的判断是否有直接证据支持。
- 阅读边界
- 依据的是 arXiv 预印本摘要，同行评审及期刊接收未核实；摘要未说明因果识别条件，模拟表现不能保证任意观察性数据中的正确识别。
原文摘要
The idea of "stratified medicine" is an important driver of methodological research on the identification of predictive biomarkers. Most methods proposed so far for this purpose have been developed for the use on randomized data only. However, especially for rare cancers, data from clinical registries or observational studies might be the only available data source. For such data, methods for an unbiased estimation of the average treatment effect are well established. Research on confounder adjustment when investigating the heterogeneity of treatment effects and the variables responsible for this is usually restricted to regression modelling. In this paper, we demonstrate how the predMOB, a tree-based method that specifically searches for predictive factors, can be combined with common strategies for confounder adjustment (covariate adjustment, matching, Inverse Probability of Treatment Weighting (IPTW)). In an extensive simulation study, we show that covariate adjustment allows the correct identification of predictive factors in the presence of confounding whereas IPTW fails in situations in which the true predictive factor is not completely independent of the confounding mechanism. A combination of both, covariate adjustment and IPTW performs as well as covariate adjustment alone, but might be more robust in complex settings. An application to the German Breast Cancer Study Group (GBSG) Trial 2 illustrates these conclusions.