An overview of methods for the analysis of longitudinal data
Copyright policy )An overview of methods for the analysis of longitudinal data
AbstractThis 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 × 2 crossover trial with binary responses and a randomized longitudinal study with a count outcome.
- Johns Hopkins University United States
- JOHNS HOPKINS UNIVERSITY United States
Random Allocation, Cross-Sectional Studies, Models, Statistical, Statistics as Topic, Linear Models, Humans, Longitudinal Studies, Mathematical Computing
Random Allocation, Cross-Sectional Studies, Models, Statistical, Statistics as Topic, Linear Models, Humans, Longitudinal Studies, Mathematical Computing
selected citations These citations are derived from selected sources.This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).582 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 1% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 0.1% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
Citations provided by BIP!Popularity provided by BIP!