An overview of methods for the analysis of longitudinal data

TLDR

Longitudinal studies offer advantages over cross‑sectional designs by capturing within‑subject changes over time. The paper reviews statistical methods for analyzing discrete and continuous longitudinal data. The review covers marginal, transition, and random‑effects models, generalized estimating equations, and illustrates them with a 2×2 crossover trial and a randomized longitudinal study.

Abstract

This paper reviews statistical methods for the analysis of discrete and continuous longitudinal data. The relative merits of longitudinal and cross-sectional studies are discussed. Three approaches, marginal, transition and random effects models, are presented with emphasis on the distinct interpretations of their coefficients in the discrete data case. We review generalized estimating equations for inferences about marginal models. The ideas are illustrated with analyses of a 2 x 2 crossover trial with binary responses and a randomized longitudinal study with a count outcome.