In medical studies, time-to-event outcomes such as time to death or relapse of a disease are routinely recorded along with longitudinal data that are observed intermittently during the follow-up period. For various reasons, marginal approaches to model the event time, corresponding to separate approaches for survival data/longitudinal data, tend to induce bias and lose efficiency. Instead, a joint modeling approach that brings the two types of data together can reduce or eliminate the bias and yield a more efficient estimation procedure. A well-established avenue for joint modeling is the joint likelihood approach that often produces semiparametric efficient estimators for the finite-dimensional parameter vectors in both models. Through a transformation survival model with an unspecified baseline hazard function, this review introduces joint modeling that accommodates both baseline covariates and time-varying covariates. The focus is on the major challenges faced by joint modeling and how they can be overcome. A review of available software implementations and a brief discussion of future directions of the field are also included.

Jane-Ling Wang

Qixian Zhong

Annual Review of Statistics and Its Application

统计及其应用年度回顾向统计学家和统计用户介绍主要方法学进展和允许实施的计算工具。

The Annual Review of Statistics and Its Application is an annual review journal that aims to introduce major methodological advancements in the field of statistics and the computational tools that facilitate their implementation to statisticians and statistical users.

《Annual Review of Statistics and Its Application》是一本年度回顾期刊，旨在向统计学家和统计用户介绍统计学领域的主要方法学进展及其应用的计算工具。

Category	Quartile
数学	1
数学, 数学跨学科应用	1
数学, 统计学与概率论	1

Joint Modeling of Longitudinal and Survival Data

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Journal Info

Category	Quartile
STATISTICS & PROBABILITY	1