Diagnostic techniques in generalized estimating equations

Venezuela, Maria Kelly; Botter, Denise Aparecida; Sandoval, Mônica C.

doi:10.1080/10629360600780488

Cited by 35 publications

(35 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…() or Venezuela et al. (). Generalised additive models for location, scale and shape proposed by Rigby and Stasinopoulos () may also be employed for such purposes, but in general, are useful only for large sets of data.…”

Section: Discussionmentioning

confidence: 99%

Graphical Tools for Detecting Departures from Linear Mixed Model Assumptions and Some Remedial Measures

Singer

Rocha

Nobre

2016

Int Statistical Rev

View full text Add to dashboard Cite

Summary We review some results on the analysis of longitudinal data or, more generally, of repeated measures via linear mixed models starting with some exploratory statistical tools that may be employed to specify a tentative model. We follow with a summary of inferential procedures under a Gaussian set‐up and then discuss different diagnostic methods focusing on residual analysis but also addressing global and local influence. Based on the interpretation of diagnostic plots related to three types of residuals (marginal, conditional and predicted random effects) as well as on other tools, we proceed to identify remedial measures for possible violations of the proposed model assumptions, ranging from fine‐tuning of the model to the use of elliptically symmetric or skew‐elliptical linear mixed models as well as of robust estimation methods. We specify many results available in the literature in a unified notation and highlight those with greater practical appeal. In each case, we discuss the availability of model diagnostics as well as of software and give general guidelines for model selection. We conclude with analyses of three practical examples and suggest further directions for research.

show abstract

Section: Discussionmentioning

confidence: 99%

Graphical Tools for Detecting Departures from Linear Mixed Model Assumptions and Some Remedial Measures

Singer

Rocha

Nobre

2016

Int Statistical Rev

View full text Add to dashboard Cite

show abstract

“…Preisser and Qaqish (1996) and Venezuela et al (2007) proposed a GEE deletion diagnostic for examining influence on regression coefficients, fitted values, goodness-of-fit statistics, or a combination of these, but not on the variance of the estimated coefficients. Influence measures have been defined based on the volume of confidence ellipsoids for detecting the influence of subjects on the efficiency of coefficient estimation.…”

Section: Discussionmentioning

confidence: 99%

Influence measures based on the volume of confidence ellipsoids for GEE

Pardo

Sanz²

2012

Biometrical J

View full text Add to dashboard Cite

The generalized estimating equations (GEE) derived by Liang and Zeger to analyze longitudinal data have been used in a wide range of medical and biological applications. To make regression a useful and meaningful statistical tool, emphasis should be placed not only on inference or fitting, but also on diagnosing potential data problems. Most of the usual diagnostics for linear regression models have been generalized for GEE. However, global influence measures based on the volume of confidence ellipsoids are not available for GEE analysis. This article presents an extension of these measures that is valid for correlated-measures regression analysis using GEEs. The proposed measures are illustrated by an analysis of epileptic seizure count data arising from a study of prograbide as an adjuvant therapy for partial seizures and some simulated data sets.

show abstract

“…Nevertheless, more research is needed for residual analysis for GLMM as already indicated in Dean and Nielsen (2007). Diagnostic tools for the GEE are presented, for example, in Venezuela et al (2007), Venezuela et al (2011), and Vens and Ziegler (2011) to detect influential and outlying observations using the projection (hat) matrix, Cook's distance, standardized residuals, and half-normal plots of absolute residuals with simulated envelopes, the latter only for constant scale parameter as proposed in Park and Shin (1998). Some diagnostic tools and references are presented in Table 5.…”

Section: Discussionmentioning

confidence: 99%

“…Plots of the studentized residuals are discussed in Venezuela et al (2007Venezuela et al ( , 2011 as well as Vens and Ziegler (2011). These residuals are presented in Fig.…”

Section: Analysis Via Gee-based Modelsmentioning

confidence: 99%

Competing regression models for longitudinal data

2012

View full text Add to dashboard Cite

The choice of an appropriate family of linear models for the analysis of longitudinal data is often a matter of concern for practitioners. To attenuate such difficulties, we discuss some issues that emerge when analyzing this type of data via a practical example involving pretest-posttest longitudinal data. In particular, we consider log-normal linear mixed models (LNLMM), generalized linear mixed models (GLMM), and models based on generalized estimating equations (GEE). We show how some special features of the data, like a nonconstant coefficient of variation, may be handled in the three approaches and evaluate their performance with respect to the magnitude of standard errors of interpretable and comparable parameters. We also show how different diagnostic tools may be employed to identify outliers and comment on available software. We conclude by noting that the results are similar, but that GEE-based models may be preferable when the goal is to compare the marginal expected responses.

show abstract

Diagnostic techniques in generalized estimating equations

Cited by 35 publications

References 13 publications

Graphical Tools for Detecting Departures from Linear Mixed Model Assumptions and Some Remedial Measures

Graphical Tools for Detecting Departures from Linear Mixed Model Assumptions and Some Remedial Measures

Influence measures based on the volume of confidence ellipsoids for GEE

Competing regression models for longitudinal data

Contact Info

Product

Resources

About