Individualized treatment effect was predicted best by modeling baseline risk in interaction with treatment assignment
通过基线风险与治疗分配交互预测个体化治疗效应AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要比较随机试验中异质性治疗效应及个体绝对获益的预测方法。
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通过基线风险与治疗分配交互预测个体化治疗效应
- 研究问题
- 在随机试验中，基线风险与治疗效应关系可能呈线性或非线性时，哪种风险建模方式更准确预测个体绝对获益？
- 方法与思路
- 作者模拟不同平均效应、风险交互形状与治疗伤害，比较恒定相对效应、风险分层、线性交互、样条交互和自适应模型。
- 材料陈述的贡献
- 摘要称线性交互在样本量 4,250 的许多情景中最优或接近最优；样条在强非线性及较大样本中较好，并以试验说明。
- 阅读关注点
- 应核对基线风险的估计方式、效应预测目标、误差与校准指标，以及非线性程度、样本量和治疗伤害如何影响比较。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收未核实；结论来自特定模拟和试验说明，不能据此认定交互模型普遍最优。
原文摘要
Objective: To compare different risk-based methods for optimal prediction of treatment effects. Methods: We simulated RCT data using diverse assumptions for the average treatment effect, a baseline prognostic index of risk (PI), the shape of its interaction with treatment (none, linear, quadratic or non-monotonic), and the magnitude of treatment-related harms (none or constant independent of the PI). We predicted absolute benefit using: models with a constant relative treatment effect; stratification in quarters of the PI; models including a linear interaction of treatment with the PI; models including an interaction of treatment with a restricted cubic spline (RCS) transformation of the PI; an adaptive approach using Akaike's Information Criterion. We evaluated predictive performance using root mean squared error and measures of discrimination and calibration for benefit. Results: The linear-interaction model displayed optimal or close-to-optimal performance across many simulation scenarios with moderate sample size (N=4,250 patients; ~ 785 events). The RCS-model was optimal for strong non-linear deviations from a constant treatment effect, particularly when sample size was larger (N=17,000). The adaptive approach also required larger sample sizes. These findings were illustrated in the GUSTO-I trial. Conclusion: An interaction between baseline risk and treatment assignment should be considered to improve treatment effect predictions.