Survival Analysis: Non-parametric, Machine Learning, and Measurement Error Latest Research

Wed, Aug 6, 2:00 PM - 3:50 PM Room CC-104A Music City Center

Milind PhadnisChair

Biometrics Section

7 Presentations

A Bayesian Varying-effect Scalar-on-function Survival Model with Measurement Error

2:05 PM - 2:20 PM

Co-authors: Joseph Yang, Roger S Zoh (Indiana University), Joseph Yang

Bayesian Scalar-on-function Physical activity Measurement error Instrumental variables Varying effect

A joint model of survival outcome and covariates subject to measurement errors and detection limits

2:20 PM - 2:35 PM

Co-authors: Xianhong Xie, Xianhong Xie

longitudinal data measurement error detection limit Cox proportional hazard model joint model Monte Carlo Expectation-Maximization

A versatile and powerful framework to identify patient subgroups using dense random survival forests

2:35 PM - 2:50 PM

Co-authors: Xingyu Li (MD Anderson), Qing Liu (Amgen Inc.), Xingyu Li (MD Anderson), Xun Jiang (Amgen), Amy Xia (Amgen), Peng Wei (University of Texas, MD Anderson Cancer Center), Brian Hobbs (University of Texas)

subgroup analysis survival analysis machine learning Phase III clinical trial random forest unsupervised learning

Nonparametric Efficient Estimation of Dynamic Treatment Regimes with Competing Risks

2:50 PM - 3:05 PM

Co-authors: Nitya Shah (University of Minnesota), Nitya Shah (University of Minnesota), Jared Huling (University of Minnesota)

dynamic treatment regimes competing risks causal inference observational data analysis nonparametric methods efficient estimation

Perturbation-Based Efficient Resampling Method for Variance Estimation in Survival Data Analysis

3:05 PM - 3:20 PM

Co-authors: Weixi Zhu, Weixi Zhu, Sy Han Chiou, Chuan-Fa Tang

Perturbation Variance Estimation Resampling Survival Data Bootstrap

Pseudo-Observations for Bivariate Survival Data

3:20 PM - 3:35 PM

Co-authors: Yael Travis-Lumer, Micha Mandel (The Hebrew University), Yael Travis-Lumer, Rebecca Betensky (NYU College of Global Public Health)

Censoring Generalized estimating equations;Generalized linear models Multi-variate survival analysis

When distributed learning meets heterogeneity: choice of methods for federated survival analysis

3:35 PM - 3:50 PM

Co-authors: Chongliang Luo (Washington University in St Louis), Yudong Wang (University of Pennsylvania, Perelman School of Medicine), Chongliang Luo (Washington University in St Louis), Yong Chen (University of Pennsylvania, Perelman School of Medicine)

Survival analysis Federated learning Cox pregression Heterogeneity Privacy-preserving