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#

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