A Review on Joint Models in Biometrical Research

Abstract

In biometrical research joint modeling of longitudinal measures and event time data has become a very popular tool to account for informative dropouts, to incorporate longitudinal trends and to link disease processes that occur simultaneously. This article reviews the work in that area of recent fruitful research by classifying approaches on joint models in three categories: Approaches with focus on serial trends, approaches with focus on event time data and approaches with equal focus on both outcomes. Typically longitudinal measures and event time data are modeled jointly by introducing shared random effects or by considering conditional distributions together with marginal distributions. We present the approaches in a uniform nomenclature, comment on sub-models applied to longitudinal measures and event time data outcomes individually and exemplify applications in biometrical research. The increasing variety of joint model approaches are a promising framework to shed light on biometrical questions associated with interacting outcomes.

Key Words:

• Joint model
• Shared random effects
• Mixed effects model
• Survival analysis