An Application of Maximum Likelihood and Generalized Estimating Equations to the Analysis of Ordinal Data from a Longitudinal Study with Cases Missing at Random

This longitudinal study explores differences between native Dutch and immigrant Moroccan adolescents in the relationship between internalizing and externalizing problems across time. By using generalized estimating equations (GEE), the strength and stability of associations between internalizing and externalizing problems in 159 Moroccan and 159 Dutch adolescents was studied over a period of 4 years. No differences in strength of co-occurring problems were found between Moroccan and Dutch adolescents. However, for Moroccan adolescents, associations between problems increased over time, whereas in Dutch adolescents, associations remained stable. The increase of co-occurring problems may be a result of undertreatment and increasing complexity of problems in Moroccans during adolescence. The results of this study imply that investigating processes leading to co-occurring problems in subgroups of adolescents, such as immigrant youths, is needed to optimize prevention and intervention efforts.

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Estimating Inverse-probability Weights for Longitudinal Data with Dropout or Truncation: The Xtrccipw Command

The Stata Journal Promoting communications on statistics and Stata ◽

10.1177/1536867x1701700202 ◽

2017 ◽

Vol 17 (2) ◽

pp. 253-278 ◽

Cited By ~ 4

Author(s):

Eric J. Daza ◽

Michael G. Hudgens ◽

Amy H. Herring

Keyword(s):

Longitudinal Study ◽

Longitudinal Data ◽

Simulation Study ◽

Generalized Estimating Equations ◽

Estimating Equations ◽

Missing At Random ◽

Drop Out ◽

Longitudinal Outcomes ◽

Inverse Probability ◽

Inverse Probability Weights

Individuals may drop out of a longitudinal study, rendering their outcomes unobserved but still well defined. However, they may also undergo truncation (for example, death), beyond which their outcomes are no longer meaningful. Kurland and Heagerty (2005, Biostatistics 6: 241–258) developed a method to conduct regression conditioning on nontruncation, that is, regression conditioning on continuation (RCC), for longitudinal outcomes that are monotonically missing at random (for example, because of dropout). This method first estimates the probability of dropout among continuing individuals to construct inverse-probability weights (IPWs), then fits generalized estimating equations (GEE) with these IPWs. In this article, we present the xtrccipw command, which can both estimate the IPWs required by RCC and then use these IPWs in a GEE estimator by calling the glm command from within xtrccipw. In the absence of truncation, the xtrccipw command can also be used to run a weighted GEE analysis. We demonstrate the xtrccipw command by analyzing an example dataset and the original Kurland and Heagerty (2005) data. We also use xtrccipw to illustrate some empirical properties of RCC through a simulation study.

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An Introduction to Generalized Estimating Equations and an Application to Assess Selectivity Effects in a Longitudinal Study on Very Old Individuals

Journal of Educational and Behavioral Statistics ◽

10.3102/10769986029004421 ◽

2004 ◽

Vol 29 (4) ◽

pp. 421-437 ◽

Cited By ~ 171

Author(s):

Paolo Ghisletta ◽

Dario Spini

Keyword(s):

Social Sciences ◽

Longitudinal Study ◽

Generalized Estimating Equations ◽

Oldest Old ◽

Estimating Equations ◽

Correlated Data ◽

Regression Coefficients ◽

Unbiased Estimation ◽

The Social ◽

Generalized Estimating

Correlated data are very common in the social sciences. Most common applications include longitudinal and hierarchically organized (or clustered) data. Generalized estimating equations (GEE) are a convenient and general approach to the analysis of several kinds of correlated data. The main advantage of GEE resides in the unbiased estimation of population-averaged regression coefficients despite possible misspecification of the correlation structure. This article aims to provide a concise, nonstatistical introduction to GEE. To illustrate the method, an analysis of selectivity effects in the Swiss Interdisciplinary Longitudinal Study on the Oldest Old is presented.

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Discussion of “Generalized Estimating Equations: Notes on the Choice of the Working Correlation Matrix”

Methods of Information in Medicine ◽

10.1055/s-0038-1625133 ◽

2010 ◽

Vol 49 (05) ◽

pp. 426-432 ◽

Cited By ~ 4

Author(s):

J. Breitung ◽

N. R. Chaganty ◽

R. M. Daniel ◽

M. G. Kenward ◽

M. Lechner ◽

...

Keyword(s):

Correlation Matrix ◽

Generalized Estimating Equations ◽

Linear Models ◽

Generalized Method Of Moments ◽

Estimating Equations ◽

Missing At Random ◽

Careful Analysis ◽

Estimation Efficiency ◽

Working Correlation ◽

Generalized Estimating

Summary Objective: To discuss generalized estimating equations as an extension of generalized linear models by commenting on the paper of Ziegler and Vens “Generalized Estimating Equations: Notes on the Choice of the Working Correlation Matrix”. Methods: Inviting an international group of experts to comment on this paper. Results: Several perspectives have been taken by the discussants. Econometricians have established parallels to the generalized method of moments (GMM). Statisticians discussed model assumptions and the aspect of missing data. Applied statisticians commented on practical aspects in data analysis. Conclusions: In general, careful modeling correlation is encouraged when considering estimation efficiency and other implications, and a comparison of choosing instruments in GMM and generalized estimating equations (GEE) would be worthwhile. Some theoretical drawbacks of GEE need to be further addressed and require careful analysis of data. This particularly applies to the situation when data are missing at random.

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