Abstract
Longitudinal data sets are comprised of repeated observations of an outcome and a set of covariates for each of many subjects. One objective of statistical analysis is to describe the marginal expectation of the outcome variable as a function of the covariates while accounting for the correlation among the repeated observations for a given subject. This paper proposes a unifying approach to such analysis for a variety of discrete and continuous outcomes. A class of generalized estimating equations (GEEs) for the regression parameters is proposed. The equations are extensions of those used in quasi-likelihood (Wedderburn, 1974, Biometrika 61, 439-447) methods. The GEEs have solutions which are consistent and asymptotically Gaussian even when the time dependence is misspecified as we often expect. A consistent variance estimate is presented. We illustrate the use of the GEE approach with longitudinal data from a study of the effect of mothers' stress on children's morbidity.
Keywords
CovariateGeneralized estimating equationGeeEstimating equationsMathematicsOutcome (game theory)StatisticsMarginal modelLongitudinal dataEconometricsRegression analysisVariance (accounting)Computer scienceMaximum likelihoodData miningMathematical economics
Related Publications
Analysis of Longitudinal Data
1. Introduction 2. Design considerations 3. Exploring longitudinal data 4. General linear models 5. Parametric models for covariance structure 6. Analysis of variance methods 7....
MCMC Methods for Multi-Response Generalized Linear Mixed Models: The<b>MCMCglmm</b><i>R</i>Package
Generalized linear mixed models provide a flexible framework for modeling a range of data, although with non-Gaussian response variables the likelihood cannot be obtained in clo...
Estimating the Mean and Variance of Normal Populations from Singly Truncated and Doubly Truncated Samples
This paper is concerned with the problem of estimating the mean and variance of normal populations from singly and doubly truncated samples having known truncation points. Maxim...
A Linear Spatial Correlation Model, with Applications to Positron Emission Tomography
Abstract A simple spatial-correlation model is presented for repeated measures data. Correlation between observations on the same subject is assumed to decay as a linear functio...
Longitudinal Design and Longitudinal Analysis
The desirability of longitudinal data is generally accepted among researchers in aging and gerontology. In recent years, a great deal of such data have been collected either in ...
Publication Info
- Year
- 1986
- Type
- article
- Volume
- 42
- Issue
- 1
- Pages
- 121-121
- Citations
- 7742
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
7742
OpenAlex
Cite This
Scott L. Zeger,
Kung‐Yee Liang
(1986).
Longitudinal Data Analysis for Discrete and Continuous Outcomes.
Biometrics
, 42
(1)
, 121-121.
https://doi.org/10.2307/2531248
Identifiers
- DOI
- 10.2307/2531248