Thursday, Aug 7: 8:30 AM - 10:20 AM
4210
Contributed Papers
Music City Center
Room: CC-202B
Main Sponsor
Biometrics Section
Presentations
Let X be a positive random variable with support on the positive real line. The log normal distribution for X is an example of transformation giving us Normal distribution. Technically, ln(X) is normally distributed. So we want to develop a class of 3 parameter distributions on the positive real line that can be transformed into a normal distribution. The transformation we want to consider is the Box-Cox transformation. It was shown no Box-Cox transformation of X can be normally distributed. By modifying the Box-Cox transformation slightly, we show that our new class of distributions is a transformable into a Normal distribution. In addition, we examine several properties of the new class of distributions algebraically and graphically.
Keywords
Box - Cox transformations
Log normal distribution
Survival Analysis
Effective monitoring of medical performance is crucial for improving healthcare quality. By identifying deteriorating performance early, prospective monitoring systems enable prompt investigations and timely corrective actions, ultimately reducing complications and mortality rates. Given this importance, post-treatment outcomes, such as survival times, are typically collected over time, leading to continuous data streams. Many existing methods for monitoring survival times focus on detecting proportional increases in hazard rates, which limits their ability to identify a broader range of performance changes, including non-proportional increases and changes in the relationships between survival times and risk factors. To address this gap, we develop a dynamic risk-adjusted survival time monitoring method for medical performance surveillance. Its key feature is the use of a newly proposed dynamic Cox model, which allows both the baseline hazard and the regression coefficients to vary over time, providing an accurate representation of the temporal dynamics in medical processes. Both theoretical and numerical studies demonstrate the effectiveness of our method in practice.
Keywords
Medical performance
Survival times
Monitoring
Dynamic Cox model
Risk adjustment
Healthcare quality
Co-Author
Kai Yang, Medical College of Wisconsin
First Author
Haoran Teng, Medical college of Wisconsin
Presenting Author
Haoran Teng, Medical college of Wisconsin
Likelihood-based inference under non-convex constraints on model parameters has become increasingly common in biomedical research. In this paper, we establish large-sample properties of the maximum likelihood estimator when the true parameter value lies at the boundary of a non-convex parameter space. We further derive the asymptotic distribution of the likelihood ratio test statistic under non-convex constraints on model parameters. A general Monte Carlo procedure for generating the limiting distribution is provided. The theoretical results are demonstrated by five examples in Anderson's stereotype logistic regression model, genetic association studies, gene-environment interaction tests, cost-constrained linear regression, and fairness-constrained linear regression.
Keywords
Likelihood ratio test
Metric projection
Non-standard condition
Depression and Alzheimer's Disease (AD) are both highly prevalent among older adults, yet the causal relationship between them remains underexplored. Using datasets from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, we examine whether geriatric depression has a significant causal effect on the risk of AD and investigate the mediating role of key biological and clinical mediators. To estimate these causal effects consistently, we control for ultra-high-dimensional potential confounders, including DNA methylation levels, applying a ball correlation-based method for confounder selection within the mediation analysis. To ensure robustness against model misspecification, we adopt a robust mediation analysis framework. Our findings indicate a significantly positive causal effect of geriatric depression on AD risk. Based on these insights, new prevention and treatment strategies for geriatric depression and Alzheimer's disease can be proposed by targeting the identified confounders and mediators.
Keywords
mediation analysis
geriatric depression
Alzheimer's disease
causal inference
DNA methylation
The United States Renal Data System (USRDS), funded by the National Institute of Diabetes and Digestive and Kidney Diseases, is national data system that collects, analyzes, and disseminate information on chronic kidney disease (CKD) and end-stage kidney disease (ESKD) in the United States (usrds.org). It includes data on nearly all patients on dialysis in the US. In this talk we will discuss several challenges in modeling CKD and ESKD patient outcomes: 1) profiling health-care providers; 2) joint model including multivariate joint modeling of longitudinal, recurrent, and terminal outcomes and spatiotemporal modeling of patient outcomes, including longitudinal hospitalization and mortality. We will present several frequentist and Bayesian approaches to addressing large data size and high-dimensional parameters associated with modeling spatial effects and/or parametrization of time-varying/dynamic effects of risk factors on patient outcomes. The discussion will highlight opportunities and open challenges in modeling patient outcomes using the USRDS database.
Keywords
Joint modeling
High-dimensional parameters
Time-varying coefficients
Large population database
End-stage kidney disease
Chronic kidney disease
Co-Author
Damla Senturk, University of California-Los Angeles
First Author
Danh Nguyen, University of California-Irvine
Presenting Author
Danh Nguyen, University of California-Irvine
As biomedical studies increasingly gather complex, high-dimensional physiological data, effective variable selection methods are essential to manage this complexity and enhance accuracy in survival models. We propose a flexible penalized variable selection method for a functional Cox model with multiple functional and scalar covariates, utilizing the group minimax concave penalty (MCP) which automatically integrates smoothness into the estimation of functional coefficients. Additionally, we introduce a novel framework for selecting smoothing parameters within the Extended Bayesian Information Criteria (EBIC), distinguished by a new method for calculating degrees of freedom. Through a simulation study, we demonstrate the method's ability to perform accurate variable selection and parameter estimation. The method is applied to National Health and Nutrition Examination Survey (NHANES) data, identifying the key temporally varying distributional features of physical activity and demographic predictors related to all-cause mortality. This analysis sheds light on the intricate relationship between physical activity and all-cause mortality among older US adults.
Keywords
Functional data analysis
Survival analysis
Variable selection
NHANES