arXiv subjects

Joel A. Dubin

Publications and source records attributed to Joel A. Dubin.

9 recordsLinked to original sources

arXiv2026

A New Trained Supervised Method for Calculating Patient Similarity

Personalized predictive modelling has been growing rapidly with the increasing availability of Electronic Health Records. This approach aims to improve a model's predictive performance by fitting a unique model to each individual. We train the model on a subset of the training data consisting of individuals similar to the individual being predicted, identified through some similarity metric. Earlier studies show that using a personalized model trained on a customized subset of the data leads to better prediction than using a global model trained on the full dataset. In this work, we develop a new patient similarity metric to improve the prediction of a personalized model for binary response data. Specifically, we introduce a weighted cosine similarity metric that extends the standard cosine similarity metric by assigning predictor-specific weights when computing similarity between participants. These weights are estimated using a supervised approach with the relaxed adaptive group lasso. Results from simulation studies and an analysis of intensive care unit data show that although our proposed similarity metric leads to a slight deterioration in calibration, it produces substantial gains in discrimination. Overall predictive performance measured by the Brier Score improves because the increase in discrimination outweighs the loss in calibration; therefore, our proposed similarity metric more effectively identifies similar participants, resulting in improved predictive accuracy.

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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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arXiv2025

Dynamic Prediction of Joint Longitudinal-Survival Models Using a Similarity-Based Approach

Longitudinal and time-to-event data are often analyzed in biomarker research to study the association between the longitudinal biomarker measurements and the event-time outcome, in which the longitudinal information contributes to the probability of the outcome of interest. An attractive nature of fitting a joint model on this type of data is that we can dynamically predict the survival probability as additional longitudinal information becomes available. We propose a new similarity-based method for the dynamic prediction of joint models where we consider training the model on only a targeted subset of the data to obtain an improved outcome prediction. Through comprehensive simulation study and an application to intensive care unit data, we demonstrate that the predictive performance of the dynamic prediction of joint models can be improved with our proposed similarity-based approach.

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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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arXiv2024

Model Selection for Causal Modeling in Missing Exposure Problems

In causal inference, properly selecting the propensity score (PS) model is an important topic and has been widely investigated in observational studies. There is also a large literature focusing on the missing data problem. However, there are very few studies investigating the model selection issue for causal inference when the exposure is missing at random (MAR). In this paper, we discuss how to select both imputation and PS models, which can result in the smallest root mean squared error (RMSE) of the estimated causal effect in our simulation study. Then, we propose a new criterion, called ``rank score'' for evaluating the overall performance of both models. The simulation studies show that the full imputation plus the outcome-related PS models lead to the smallest RMSE and the rank score can help select the best models. An application study is conducted to quantify the causal effect of cardiovascular disease (CVD) on the mortality of COVID-19 patients.

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arXiv2024

Causal Inference on Missing Exposure via Robust Estimation

How to deal with missing data in observational studies is a common concern for causal inference. When the covariates are missing at random (MAR), multiple approaches have been provided to help solve the issue. However, if the exposure is MAR, few approaches are available and careful adjustments on both missingness and confounding issues are required to ensure a consistent estimate of the true causal effect on the response. In this article, a new inverse probability weighting (IPW) estimator based on weighted estimating equations (WEE) is proposed to incorporate weights from both the missingness and propensity score (PS) models, which can reduce the joint effect of extreme weights in finite samples. Additionally, we develop a triple robust (TR) estimator via WEE to further protect against the misspecification of the missingness model. The asymptotic properties of WEE estimators are proved using properties of estimating equations. Based on the simulation studies, WEE methods outperform others including imputation-based approaches in terms of bias and variability. Finally, an application study is conducted to identify the causal effect of the presence of cardiovascular disease on mortality for COVID-19 patients.

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arXiv2022

Functional Spherical Autocorrelation: A Robust Estimate of the Autocorrelation of a Functional Time Series

We propose a new autocorrelation measure for functional time series that we term spherical autocorrelation. It is based on measuring the average angle between lagged pairs of series after having been projected onto the unit sphere. This new measure enjoys several complimentary advantages compared to existing autocorrelation measures for functional data, since it both 1) describes a notion of sign or direction of serial dependence in the series, and 2) is more robust to outliers. The asymptotic properties of estimators of the spherical autocorrelation are established, and are used to construct confidence intervals and portmanteau white noise tests. These confidence intervals and tests are shown to be effective in simulation experiments, and demonstrated in applications to model selection for daily electricity price curves, and measuring the volatility in densely observed asset price data.

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arXiv2020

Evaluating real-time probabilistic forecasts with application to National Basketball Association outcome prediction

Motivated by the goal of evaluating real-time forecasts of home team win probabilities in the National Basketball Association, we develop new tools for measuring the quality of continuously updated probabilistic forecasts. This includes introducing calibration surface plots, and simple graphical summaries of them, to evaluate at a glance whether a given continuously updated probability forecasting method is well-calibrated, as well as developing statistical tests and graphical tools to evaluate the skill, or relative performance, of two competing continuously updated forecasting methods. These tools are studied by means of a Monte Carlo simulation study of simulated basketball games, and demonstrated in an application to evaluate the continuously updated forecasts published by the United States-based multinational sports network ESPN on its principle webpage {\tt espn.com}. This application lends statistical evidence that the forecasts published there are well-calibrated, and exhibit improved skill over several naïve models, but do not demonstrate significantly improved skill over simple logistic regression models based solely on a measurement of each teams' relative strength, and the evolving score difference throughout the game.

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arXiv2019

Similarity-based Random Survival Forest

Predicting time-to-event outcomes in large databases can be a challenging but important task. One example of this is in predicting the time to a clinical outcome for patients in intensive care units (ICUs), which helps to support critical medical treatment decisions. In this context, the time to an event of interest could be, for example, survival time or time to recovery from a disease/ailment observed within the ICU. The massive health datasets generated from the uptake of Electronic Health Records (EHRs) are quite heterogeneous as patients can be quite dissimilar in their relationship between the feature vector and the outcome, adding more noise than information to prediction. In this paper, we propose a modified random forest method for survival data that identifies similar cases in an attempt to improve accuracy for predicting time-to-event outcomes; this methodology can be applied in various settings, including with ICU databases. We also introduce an adaptation of our methodology in the case of dependent censoring. Our proposed method is demonstrated in the Medical Information Mart for Intensive Care (MIMIC-III) database, and, in addition, we present properties of our methodology through a comprehensive simulation study. Introducing similarity to the random survival forest method indeed provides improved predictive accuracy compared to random survival forest alone across the various analyses we undertook.

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