  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.
