Longitudinal Data Analysis

Abstract

Longitudinal studies are quite common in modern clinical trials and cohort studies. Unlike cross-sectional designs, where observations from study subjects are available only at a single time point, individuals in longitudinal or cohort studies are assessed repeatedly over time. By taking advantages of multiple snapshots of a group over time, data from longitudinal studies captures both between-individual differences and within-individual dynamics, affording the opportunity to study more complicated biological, psychological, and behavioral hypotheses than their crosssectional counterparts. For example, if we want to test whether exposure to some chemical agent can cause some type of cancer, the between-subject difference observed in crosssectional data can only provide evidence of an association or correlation between the exposure and disease. The within-individual dynamics in longitudinal data allows for inference of a causal nature for such a relationship. Longitudinal data presents multiple methodological challenges in study designs and data analyses. The primary problem is the correlation among the repeated responses of the same subject. Classic models for cross-sectional data analysis such as multiple linear and logistic regressions are based on the independence of observations and thus in general do not apply to longitudinal data. For example, in