Withdrawn - 09. Neural Network-Based High-Dimensional Survival Analysis with Measurement Error

Although measurement error (ME) in the survival model has been addressed in some studies, attempts to account for ME in high-dimensional data for survival models are limited. We propose a neural network-based corrected score (NNCS) approach to simultaneously correct for biases caused by ME in both functional and scalar covariates. The NNCS approach approximates the conditional expectation of the latent true measures based on repeated observed measures. This approximation is incorporated into a corrected Cox score function, yielding estimators that are both consistent and asymptotically normal. The NNCS approach is flexible and data-driven, does not require strict parametric assumptions on the variables, and is adaptive to various survival models. Furthermore, it can capture complex nonlinear relationships between the latent true measures and observed measures. Simulation studies demonstrate that NNCS estimators consistently exhibit smaller bias across various scenarios. The proposed approach is applied to examine how device-based physical activity and self-reported sugar intake relate to overall death risk among U.S. adults.

Neural Network

Survival Analysis

High Dimension

Measurement Error

physical activity 

Mid-Level

Influence

Women in Statistics and Data Science 2025