Precision medicine is accelerating rapidly in the field of health research. This includes fitting predictive models for individual patients based on patient similarity in an attempt to improve model performance. We propose an algorithm which fits a personalized predictive model (PPM) using an optimal size of a similar subpopulation that jointly optimizes model discrimination and calibration, as it is criticized that calibration is not assessed nearly as often as discrimination despite poorly calibrated models being potentially misleading. We define a mixture loss function that considers model discrimination and calibration, and allows for flexibility in emphasizing one performance measure over another. We empirically show that the relationship between the size of subpopulation and calibration is quadratic, which motivates the development of our jointly optimized model. We also investigate the effect of within-population patient weighting on performance and conclude that the size of subpopulation has a larger effect on the predictive performance of the PPM compared to the choice of weight function.

翻译：精准医疗在健康研究领域正加速发展，其中包含基于患者相似性为个体患者拟合预测模型，以期提升模型性能。我们提出一种算法，该算法利用最优规模的相似亚群拟合个性化预测模型，并联合优化模型的区分度与校准度——当前研究批评指出，尽管校准不良的模型可能产生误导，但其评估频率远不及区分度。我们定义了一个同时考虑模型区分度与校准度的混合损失函数，允许灵活地侧重某一性能指标。实证研究表明，亚群规模与校准度呈二次曲线关系，这一发现推动了联合优化模型的开发。我们还研究了群体内患者加权对性能的影响，并得出结论：相较于权重函数的选择，亚群规模对个性化预测模型的预测性能影响更大。