arXiv subjects

Tatiana Krikella

Publications and source records attributed to Tatiana Krikella.

3 recordsLinked to original sources

arXiv2026

A General Mixture Loss Function to Optimize a Personalized Predictive Model

Advances in precision medicine increasingly drive methodological innovation in health research. A key development is the use of personalized prediction models (PPMs), which are fit using a similar subpopulation tailored to a specific index patient, and have been shown to outperform one-size-fits-all models, particularly in terms of model discrimination performance. We propose a generalized loss function that enables tuning of the subpopulation size used to fit a PPM. This loss function allows joint optimization of discrimination and calibration, allowing both the performance measures and their relative weights to be specified by the user. To reduce computational burden, we conducted extensive simulation studies to identify practical bounds for the grid of subpopulation sizes. Based on these results, we recommend using a lower bound of 20\% and an upper bound of 70\% of the entire training dataset. We apply the proposed method to both simulated and real-world datasets and demonstrate that previously observed relationships between subpopulation size and model performance are robust. Furthermore, we show that the choice of performance measures in the loss function influences the optimal subpopulation size selected. These findings support the flexible and computationally efficient implementation of PPMs in precision health research.

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arXiv2026

Joint estimation of the basic reproduction number and serial interval using Sequential Bayes

Early in an infectious disease outbreak, timely and accurate estimation of the basic reproduction number ($R_0$) and the serial interval (SI) is critical for understanding transmission dynamics and informing public health responses. While many methods estimate these quantities separately, and a small number jointly estimate them from incidence data, existing joint approaches are largely likelihood-based and do not fully exploit prior information. We propose a novel Bayesian framework for the joint estimation of $R_0$ and the serial interval using only case count data, implemented through a sequential Bayes approach. Our method assumes an SIR model and employs a mildly informative joint prior constructed by linking log-Gamma marginal distributions for $R_0$ and the SI via a Gaussian copula, explicitly accounting for their dependence. The prior is updated sequentially as new incidence data become available, allowing for real-time inference. We assess the performance of the proposed estimator through extensive simulation studies under correct model specification as well as under model misspecification, including when the true data come from an SEIR or SEAIR model, and under varying degrees of prior misspecification. Comparisons with the widely used White and Pagano likelihood-based joint estimator show that our approach yields substantially more precise and stable estimates of $R_0$, with comparable or improved bias, particularly in the early stages of an outbreak. Estimation of the SI is more sensitive to prior misspecification; however, when prior information is reasonably accurate, our method provides reliable SI estimates and remains more stable than the competing approach. We illustrate the practical utility of the proposed method using Canadian COVID-19 incidence data at both national and provincial levels.

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arXiv2025

A Personalized Predictive Model that Jointly Optimizes Discrimination and Calibration

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.

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