Robust modified profile estimating function with application to the generalized estimating equation

2008 ◽  
Vol 138 (7) ◽  
pp. 2029-2044 ◽  
Author(s):  
Molin Wang ◽  
John J. Hanfelt
Keyword(s):  
Estimating Equation ◽  
Generalized Estimating Equation ◽  
Estimating Function ◽  
Generalized Estimating

Models for Longitudinal Data: A Generalized Estimating Equation Approach

Biometrics ◽  
1988 ◽  
Vol 44 (4) ◽  
pp. 1049 ◽  
Author(s):  
Scott L. Zeger ◽  
Kung-Yee Liang ◽  
Paul S. Albert
Keyword(s):  
Longitudinal Data ◽  
Estimating Equation ◽  
Generalized Estimating Equation ◽  
Equation Approach ◽  
Generalized Estimating

scholarly journals Improved Generalized Estimating Equation Analysis via xtqls for Quasi–Least Squares in Stata

2007 ◽  
Vol 7 (2) ◽  
pp. 147-166 ◽  
Author(s):  
Justine Shults ◽  
Sarah J. Ratcliffe ◽  
Mary Leonard
Keyword(s):  
Least Squares ◽  
Estimating Equation ◽  
Generalized Estimating Equation ◽  
Iterative Approach ◽  
Current Estimate ◽  
Cross Sectional ◽  
Equation Analysis ◽  
Correlation Structures ◽  
Correlation Parameters ◽  
Generalized Estimating

Quasi–least squares (QLS) is an alternative method for estimating the correlation parameters within the framework of the generalized estimating equation (gee) approach for analyzing correlated cross-sectional and longitudinal data. This article summarizes the development of qls that occurred in several reports and describes its use with the user-written program xtqls in Stata. Also, it demonstrates the following advantages of qls: (1) qls allows some correlation structures that have not yet been implemented in the framework of gee, (2) qls can be applied as an alternative to gee if the gee estimate is infeasible, and (3) qls uses the same estimating equation for estimation of β as gee; as a result, qls can involve programs already available for gee. In particular, xtqls calls the Stata program xtgee within an iterative approach that alternates between updating estimates of the correlation parameter α and then using xtgee to solve the gee for β at the current estimate of α. The benefit of this approach is that after xtqls, all the usual postregression estimation commands are readily available to the user.

scholarly journals Simultaneous inference for multiple marginal generalized estimating equation models

2019 ◽  
Vol 29 (6) ◽  
pp. 1746-1762 ◽  
Author(s):  
Robin Ristl ◽  
Ludwig Hothorn ◽  
Christian Ritz ◽  
Martin Posch
Keyword(s):  
Estimating Equations ◽  
Estimating Equation ◽  
Small Sample ◽  
Regression Coefficients ◽  
Generalized Estimating Equation ◽  
Type I ◽  
Simultaneous Inference ◽  
Multiple Endpoints ◽  
Repeated Observations ◽  
Generalized Estimating

Motivated by small-sample studies in ophthalmology and dermatology, we study the problem of simultaneous inference for multiple endpoints in the presence of repeated observations. We propose a framework in which a generalized estimating equation model is fit for each endpoint marginally, taking into account dependencies within the same subject. The asymptotic joint normality of the stacked vector of marginal estimating equations is used to derive Wald-type simultaneous confidence intervals and hypothesis tests for multiple linear contrasts of regression coefficients of the multiple marginal models. The small sample performance of this approach is improved by a bias adjustment to the estimate of the joint covariance matrix of the regression coefficients from multiple models. As a further small sample improvement a multivariate t-distribution with appropriate degrees of freedom is specified as reference distribution. In addition, a generalized score test based on the stacked estimating equations is derived. Simulation results show strong control of the family-wise type I error rate for these methods even with small sample sizes and increased power compared to a Bonferroni-Holm multiplicity adjustment. Thus, the proposed methods are suitable to efficiently use the information from repeated observations of multiple endpoints in small-sample studies.

A readily available improvement over method of moments for intra-cluster correlation estimation in the context of cluster randomized trials and fitting a GEE–type marginal model for binary outcomes

2018 ◽  
Vol 16 (1) ◽  
pp. 41-51 ◽  
Author(s):  
Philip M Westgate
Keyword(s):  
Correlation Coefficient ◽  
Randomized Trials ◽  
Estimating Equation ◽  
Generalized Estimating Equation ◽  
Cluster Randomized Trials ◽  
Coefficient Estimation ◽  
Pseudo Likelihood ◽  
Cluster Randomized ◽  
Cluster Correlation ◽  
Generalized Estimating

Background/aims Cluster randomized trials are popular in health-related research due to the need or desire to randomize clusters of subjects to different trial arms as opposed to randomizing each subject individually. As outcomes from subjects within the same cluster tend to be more alike than outcomes from subjects within other clusters, an exchangeable correlation arises that is measured via the intra-cluster correlation coefficient. Intra-cluster correlation coefficient estimation is especially important due to the increasing awareness of the need to publish such values from studies in order to help guide the design of future cluster randomized trials. Therefore, numerous methods have been proposed to accurately estimate the intra-cluster correlation coefficient, with much attention given to binary outcomes. As marginal models are often of interest, we focus on intra-cluster correlation coefficient estimation in the context of fitting such a model with binary outcomes using generalized estimating equations. Traditionally, intra-cluster correlation coefficient estimation with generalized estimating equations has been based on the method of moments, although such estimators can be negatively biased. Furthermore, alternative estimators that work well, such as the analysis of variance estimator, are not as readily applicable in the context of practical data analyses with generalized estimating equations. Therefore, in this article we assess, in terms of bias, the readily available residual pseudo-likelihood approach to intra-cluster correlation coefficient estimation with the GLIMMIX procedure of SAS (SAS Institute, Cary, NC). Furthermore, we study a possible corresponding approach to confidence interval construction for the intra-cluster correlation coefficient. Methods We utilize a simulation study and application example to assess bias in intra-cluster correlation coefficient estimates obtained from GLIMMIX using residual pseudo-likelihood. This estimator is contrasted with method of moments and analysis of variance estimators which are standards of comparison. The approach to confidence interval construction is assessed by examining coverage probabilities. Results Overall, the residual pseudo-likelihood estimator performs very well. It has considerably less bias than moment estimators, which are its competitor for general generalized estimating equation–based analyses, and therefore, it is a major improvement in practice. Furthermore, it works almost as well as analysis of variance estimators when they are applicable. Confidence intervals have near-nominal coverage when the intra-cluster correlation coefficient estimate has negligible bias. Conclusion Our results show that the residual pseudo-likelihood estimator is a good option for intra-cluster correlation coefficient estimation when conducting a generalized estimating equation–based analysis of binary outcome data arising from cluster randomized trials. The estimator is practical in that it is simply a result from fitting a marginal model with GLIMMIX, and a confidence interval can be easily obtained. An additional advantage is that, unlike most other options for performing generalized estimating equation–based analyses, GLIMMIX provides analysts the option to utilize small-sample adjustments that ensure valid inference.

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