Random-Effects Models for Longitudinal Data#
Nan Laird 1982#
The paper addresses the statistical modeling of longitudinal data, which involves repeated measurements on the same subjects over time. The primary objective is to develop and present a framework for analyzing such data using random-effects models.
Key Contributions#
Introduction of Random-Effects Models: Laird’s paper introduces the concept of random-effects models for longitudinal data. These models account for the correlation between repeated measurements within the same subject and allow for the incorporation of both fixed and random effects.
Hierarchical Structure: The paper discusses the hierarchical structure of the random-effects model, where individual subject-specific effects are considered as random variables following a certain distribution. This structure captures the underlying variability among subjects.
Likelihood-Based Estimation: Laird presents likelihood-based methods for estimating the model parameters, including the variance components of the random effects. Maximum likelihood estimation plays a central role in this approach.
Comparison with Other Models: The paper compares random-effects models with alternative approaches, such as pooled cross-sectional models and fixed-effects models. It highlights the advantages of random-effects models in capturing subject-specific variability.
Application Examples: The paper provides practical examples of the application of random-effects models to real longitudinal data, demonstrating theirflexibility and usefulness in various fields, including medicine and social sciences.
Discussion of Assumptions: Laird discusses the assumptions underlying random-effects models, such as the distributional assumptions for random effects and the choice of correlation structure. This discussion helps researchers make informed modeling decisions.
Statistical Software: The paper mentions the availability of statistical software for fitting random-effects models, which has since become widely used in longitudinal data analysis.
In summary, Laird’s 1982 paper “Random-Effects Models for Longitudinal Data” is a foundational work in the field of longitudinal data analysis. It introduces the concept of random-effects models and provides a comprehensive framework for modeling and analyzing repeated measurements over time.The paper’s contributions have had a lasting impact on statistical methodology and have been widely applied in various fields to address the challenges posed by longitudinal data.
Conversation with Nan Laird#
Summary#
Presentation#
Paper#
If you are unable to view the interview, please watch it here. If you are unable to view any of the documents above, please download the summary, presentation, and paper.