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.
翻译:方法:我们模拟了RCT数据,使用了对平均治疗效果的不同假设、风险基准预测指数(PI)、其与治疗的相互作用(无、线性、二次或非双向),以及与治疗有关的伤害的程度(没有或经常独立于PI)。 我们预测了绝对效益,使用的是:具有持续相对治疗效果的模型;PI的分层;模型包括与PI的治疗的线性互动;模型包括治疗与PI的有限立方螺旋(RCS)转化的相互作用;利用Akaike的信息标准进行适应性方法;我们利用根平均值差、线性、二次或非单向偏差以及歧视和校准的衡量方法评估了预测性能。结果:线性互动模型展示了最佳或近于最佳的性性效果,在样本规模中等的多个模拟假设情景(N=4,250名病人;~785次事件)。RCS模型最适合从限制的立方螺旋线性螺旋线(RCS)疗法与PI的转化;使用Akaike的信息标准度调整方法;我们利用根平均值评价了预测性性性性性性性性性性性性工作,特别是当需要的GI的样本分析规模更大时,这些测试性评估性影响时,这种试验规模时,应当为最优化。