A review on linear mixed models for longitudinal data, possibly subject to dropout
- 1 December 2001
- journal article
- review article
- Published by SAGE Publications in Statistical Modelling
- Vol. 1 (4) , 235-269
- https://doi.org/10.1177/1471082x0100100402
Abstract
Many approaches are available for the analysis of continuous longitudinal data. Over the last couple of decades, a lot of emphasis has been put on the linear mixed model. The current paper is dedicated to an overview of this approach, with emphasis on model formulation, interpretation and inference. Advantages as well as drawbacks are discussed, and guidelines are given for general statistical practice. Special attention is given to the problem of missing data, i.e., the case where not all data are present as planned in the original design of the study.Keywords
This publication has 76 references indexed in Scilit:
- A General Maximum Likelihood Analysis of Variance Components in Generalized Linear ModelsBiometrics, 1999
- Randomized Phase III Trial Comparing the New Potent and Selective Third-Generation Aromatase Inhibitor Vorozole With Megestrol Acetate in Postmenopausal Advanced Breast Cancer PatientsJournal of Clinical Oncology, 1999
- Inference for Non-random SamplesJournal of the Royal Statistical Society Series B: Statistical Methodology, 1997
- Bayesian Data AnalysisPublished by Taylor & Francis ,1995
- Informative Drop-Out in Longitudinal Data AnalysisJournal of the Royal Statistical Society Series C: Applied Statistics, 1994
- Modeling the Progression of HIV InfectionJournal of the American Statistical Association, 1991
- Sensitivity Analysis in Linear RegressionWiley Series in Probability and Statistics, 1988
- Selection Modeling Versus Mixture Modeling with Nonignorable NonresponsePublished by Springer Nature ,1986
- Bayesian inference for variance components using only error contrastsBiometrika, 1974
- The Analysis of Incomplete DataPublished by JSTOR ,1971