评估用于预测个体治疗效应的预测建模策略在精准医学中的应用
Evaluating Predictive Modeling Strategies for Predicting Individual Treatment Effects in Precision Medicine
AI总结：
本文评估了多种建模策略在预测个体治疗效应中的性能，发现线性/投影方法表现稳健，而灵活学习者需强信号和充足样本量，强调外部验证的重要性。
摘要：
精准医学旨在为患者匹配产生最大益处的治疗方案。预测个体治疗效应（PITE）-即治疗组与对照组下预测结果的差异-量化了这种益处，但因其未观察到的反事实、高维性和复杂的交互作用而难以估计。我们使用结构化模拟框架比较了30多种建模策略，包括惩罚和投影方法、灵活学习者以及树集合，通过变化样本量、维度、多重共线性和交互复杂性来评估性能。性能通过均方根误差（RMSE）测量预测准确性，通过方向准确性（DIR）测量正确分类益处与危害。内部验证产生乐观的估计，而外部验证在分布偏移和更高阶交互作用下更清晰地揭示了模型的弱点。惩罚和投影方法-岭回归、Lasso、弹性网、偏最小二乘（PLS）和主成分回归（PCR）-始终实现了强劲的RMSE和DIR性能。灵活学习者仅在强信号和足够样本量下表现优异。结果强调了稳健的线性/投影默认设置以及严格外部验证的必要性。
英文摘要：
Precision medicine seeks to match patients with treatments that produce the greatest benefit. The Predicted Individual Treatment Effect (PITE)-the difference between predicted outcomes under treatment and control-quantifies this benefit but is difficult to estimate due to unobserved counterfactuals, high dimensionality, and complex interactions. We compared 30+ modeling strategies, including penalized and projection-based methods, flexible learners, and tree-ensembles, using a structured simulation framework varying sample size, dimensionality, multicollinearity, and interaction complexity. Performance was measured using root mean squared error (RMSE) for prediction accuracy and directional accuracy (DIR) for correctly classifying benefit versus harm. Internal validation produced optimistic estimates, whereas external validation with distributional shifts and higher-order interactions more clearly revealed model weaknesses. Penalized and projection-based approaches-ridge, lasso, elastic net, partial least squares (PLS), and principal components regression (PCR)-consistently achieved strong RMSE and DIR performance. Flexible learners excelled only under strong signals and sufficient sample sizes. Results highlight robust linear/projection defaults and the necessity of rigorous external validation.
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