Joint Modeling of Longitudinal and Survival Data with Censored Single-index Varying Coefficient Models

TL;DR

This paper introduces a novel joint modeling approach that combines longitudinal and survival data using a single-index varying coefficient model, addressing dependency and censoring issues in medical research.

Contribution

It extends traditional single-index models to a joint framework with varying coefficients and nonparametric methods to handle censored survival data.

Findings

Demonstrates improved modeling of dependency between data types

Shows effectiveness through numerical experiments

Addresses censoring with nonparametric synthetic data regression

Abstract

In medical and biological research, longitudinal data and survival data types are commonly seen. Traditional statistical models mostly consider to deal with either of the data types, such as linear mixed models for longitudinal data, and the Cox models for survival data, while they do not adjust the association between these two different data types. It is desirable to have a joint modeling approach which accomadates both data types and the dependency between them. In this paper, we extend traditional single-index models to a new joint modeling approach, by replacing the single-index component to a varying coefficient component to deal with longitudinal outcomes, and accomadate the random censoring problem in survival analysis by nonparametric synthetic data regression for the link function. Numerical experiments are conducted to evaluate the finite sample performance.