arXiv subjects

J. T. Korley

Publications and source records attributed to J. T. Korley.

4 recordsLinked to original sources

arXiv2026

A Bayesian Longitudinal Spatial Normative Model for Individualized Brain Deviation Mapping

Normative modeling enables individualized characterization of structural brain deviations by evaluating subjects against a reference population rather than a group average. Most existing implementations treat brain regions independently and remain cross-sectional, despite the availability of repeated neuroimaging measurements and the well-documented spatial organization of neuroanatomical variation. We propose a Bayesian longitudinal spatial normative model that jointly captures within-subject temporal dependence and spatially structured subject-specific deviations within a unified hierarchical framework. The individualized deviation map is treated as a latent spatial process with an explicit posterior distribution, yielding a principled Bayes estimator under squared error loss rather than an ad hoc residual summary. Across six simulation scenarios encompassing varying spatial dependence, nonlinear trajectories, irregular visit schedules, and missing follow-up, the proposed model consistently reduced deviation-map reconstruction error relative to independent cross-sectional and longitudinal non-spatial benchmarks while maintaining stable calibration. In an application to OASIS-3 structural MRI data, the model reduced RMSE by 54% relative to the independent cross-sectional model and by 45% relative to the longitudinal non-spatial model. Regional deviation burden was concentrated in the temporal pole, entorhinal cortex, inferior temporal cortex, posterior cingulate, and parahippocampal cortex, consistent with regions implicated in early Alzheimer-type neurodegeneration. Subject-level profiles revealed substantial heterogeneity in regional abnormality patterns, including marked multiregional deviation with preserved global cognitive scores.

stat.ME↗

arXiv2025

Robust Survival Estimation under Interval Censoring: Expectation-Maximization and Bayesian Accelerated Failure Time Assessment via Simulation and Application

Interval censoring occurs when event times are only known to fall between scheduled assessments, a common design in clinical trials, epidemiology, and reliability studies. Standard right-censoring methods, such as Kaplan-Meier and Cox regression, are not directly applicable and can produce biased results. This study compares three complementary approaches for interval-censored survival data. First, the Turnbull nonparametric maximum likelihood estimator (NPMLE) via the EM algorithm recovers the survival distribution without strong assumptions. Second, Weibull and log-normal accelerated failure time (AFT) models with interval likelihoods provide smooth, covariate-adjusted survival curves and interpretable time-ratio effects. Third, Bayesian AFT models extend these tools by quantifying posterior uncertainty, incorporating prior information, and enabling interval-aware model comparisons via PSIS-LOO cross-validation. Simulations across generating distributions, censoring intensities, sample sizes, and covariate structures evaluated the integrated squared error (ISE) for curve recovery, integrated Brier score (IBS) for prediction, and coverage for uncertainty calibration. Results show that the EM achieves the lowest ISE for distribution recovery, AFT models improve predictive performance when families are correctly specified, and Bayesian AFT offers calibrated uncertainty and principled model selection. An application to the ovarian cancer dataset, restructured into interval-censored form, demonstrates the workflow in practice: the EM algorithm reveals the baseline shape, parametric AFT provides covariate-adjusted predictions, and Bayesian AFT validates model adequacy through posterior predictive checks. Together, these methods form a tiered strategy: EM for shape discovery, AFT for covariate-driven prediction, and Bayesian AFT for complete uncertainty quantification and model comparison.

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arXiv2025

Individualized Treatment Effects in Advanced Prostate Cancer: A Causal-Survival Modeling Approach to Risk-Guided Therapy

We conducted a proof-of-concept evaluation of individualized treatment effect (ITE) estimation using survival data from a randomized trial of 475 men with advanced prostate cancer treated with high- versus low-dose diethylstilbestrol (DES). A Weibull accelerated failure time (AFT) model with interaction terms for treatment-by-age and treatment-by-log tumor size was used to capture subgroup-specific treatment effects. The estimated main effect of high-dose DES indicated a time ratio of 0.582 (95% CI: [0.306, 1.110]), reflecting reduced survival at the reference levels of age and tumor size. However, interaction-adjusted ITEs revealed marked effect modification: younger patients (e.g., age 50 years) had over fourfold expected survival gains (time ratio 4.09), whereas older patients (e.g., age 80 years) experienced reduced benefit (time ratio 0.71). Similarly, patients with larger tumors (log size $\sim$4.25, $\sim$70 $cm^2$) derived a stronger benefit (time ratio 1.89) than those with smaller tumors. To evaluate the reliability of these individualized estimates, both the delta method and bootstrap resampling were applied for uncertainty quantification, producing closely aligned intervals across the risk spectrum. This analysis illustrates how parametric survival models with clinically motivated interactions and robust inference procedures can yield interpretable patient-level treatment effect estimates, even in moderately sized oncology trials.

stat.AP↗

arXiv2025

Evaluation of Time Series Forecasting Models for Predicting Lung Cancer Mortality Rates in the United States: A Comparison with Altuhaifa (2023) Study

This paper evaluates the performance of the following time series forecasting models - Simple Exponential Smoothing (SES), Holt's Double Exponential Smoothing (HDES), and Autoregressive Integrated Moving Average (ARIMA) - in predicting lung cancer mortality rates in the United States. It builds upon the work of Altuhaifa, which used Surveillance, Epidemiology, and End Results (SEER) data from 1975-2018 to evaluate these models. Altuhaifa's study found that ARIMA (0,2,2), SES with smoothing parameter $α=0.995$, and HDES with parameters $α=0.4$ and $β=0.9$ were the optimal models from their analysis, with HDES providing the lowest Root Mean Squared Error (RMSE) of 132.91. The paper extends the dataset to 2021 and re-evaluates the models. Using the same SEER data from 1975-2021, it identifies ARIMA (0,2,2), SES ($α=0.999$), and HDES ($α=0.5221$, $β=0.5219$) as the best-fitting models. Interestingly, ARIMA (0,2,2) and HDES yield the lowest RMSE of 2.56. To obtain forecasts with higher accuracy, an average model (HDES-ARIMA) consisting of HDES and ARIMA was constructed to leverage their strengths. The HDES-ARIMA model also achieves an RMSE of 2.56. The forecast from the average model suggests declining lung cancer mortality rates in the United States. The study highlights how expanding datasets and re-evaluating models can provide updated insights. It recommends further analysis using monthly data separated by gender, ethnicity, and state to understand lung cancer mortality dynamics in the United States. Overall, advanced time series methods like HDES and ARIMA show strong potential for accurately forecasting this major public health issue.

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