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Journal articles on the topic 'Generalized estimating equations model'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 January 2023

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1

Vens, M., and A. Ziegler. "Generalized Estimating Equations." Methods of Information in Medicine 49, no. 05 (2010): 421–25. http://dx.doi.org/10.3414/me10-01-0026.

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Summary Background: Generalized estimating equations (GEE) are an extension of generalized linear models (GLM) in that they allow adjusting for correlations between observations. A major strength of GEE is that they do not require the correct specification of the multivariate distribution but only of the mean structure. Objectives: Several concerns have been raised about the validity of GEE when applied to dichotomous dependent variables. In this contribution, we summarize the theoretical findings concerning efficiency and validity of GEE. Methods: We introduce the GEE in a formal way, summari

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2

Feddag, M.-L. "Generalized Estimating Equations to Binary Probit Model." Communications in Statistics - Theory and Methods 43, no. 19 (2014): 3997–4010. http://dx.doi.org/10.1080/03610926.2012.712186.

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3

Feddag, Mohand L., and Mounir Mesbah. "Generalized estimating equations for longitudinal mixed Rasch model." Journal of Statistical Planning and Inference 129, no. 1-2 (2005): 159–79. http://dx.doi.org/10.1016/j.jspi.2004.06.045.

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4

Carey, Vincent J., and You-Gan Wang. "Working covariance model selection for generalized estimating equations." Statistics in Medicine 30, no. 26 (2011): 3117–24. http://dx.doi.org/10.1002/sim.4300.

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5

Breitung, J., N. R. Chaganty, R. M. Daniel, et al. "Discussion of “Generalized Estimating Equations: Notes on the Choice of the Working Correlation Matrix”." Methods of Information in Medicine 49, no. 05 (2010): 426–32. http://dx.doi.org/10.1055/s-0038-1625133.

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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 aspe

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6

Fang, Fang, Jialiang Li, and Jingli Wang. "Optimal model averaging estimation for correlation structure in generalized estimating equations." Communications in Statistics - Simulation and Computation 48, no. 5 (2018): 1574–93. http://dx.doi.org/10.1080/03610918.2017.1419260.

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7

Shen, Chung-Wei, and Yi-Hau Chen. "Model Selection for Generalized Estimating Equations Accommodating Dropout Missingness." Biometrics 68, no. 4 (2012): 1046–54. http://dx.doi.org/10.1111/j.1541-0420.2012.01758.x.

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8

Lipi, Nasrin, Mohammad Samsul Alam, and Syed Shahadat Hossain. "A Generalized Estimating Equations Approach for Modeling Spatially Clustered Data." Austrian Journal of Statistics 50, no. 4 (2021): 36–52. http://dx.doi.org/10.17713/ajs.v50i4.1097.

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Clustering in spatial data is very common phenomena in various fields such as disease mapping, ecology, environmental science and so on. Analysis of spatially clustered data should be different from conventional analysis of spatial data because of the nature of clusters in the data. Because it is expected that the observations of same cluster are more similar than the observations from different clusters. In this study, a method has been proposed for the analysis of spatially clustered areal data based on generalized estimating equations which were originally developed for analyzing longitudin

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9

Lo, Chi Ho, Wing Kam Fung, and Zhong Yi Zhu. "Structural Parameter Estimation Using Generalized Estimating Equations for Regression Credibility Models." ASTIN Bulletin 37, no. 02 (2007): 323–43. http://dx.doi.org/10.2143/ast.37.2.2024070.

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A generalized estimating equations (GEE) approach is developed to estimate structural parameters of a regression credibility model with independent or moving average errors. A comprehensive account is given to illustrate how GEE estimators are worked out within an extended Hachemeister (1975) framework. Evidenced by results of simulation studies, the proposed GEE estimators appear to outperform those given by Hachemeister, and have led to a remarkable improvement in accuracy of the credibility estimators so constructed.

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10

Lo, Chi Ho, Wing Kam Fung, and Zhong Yi Zhu. "Structural Parameter Estimation Using Generalized Estimating Equations for Regression Credibility Models." ASTIN Bulletin 37, no. 2 (2007): 323–43. http://dx.doi.org/10.1017/s0515036100014896.

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A generalized estimating equations (GEE) approach is developed to estimate structural parameters of a regression credibility model with independent or moving average errors. A comprehensive account is given to illustrate how GEE estimators are worked out within an extended Hachemeister (1975) framework. Evidenced by results of simulation studies, the proposed GEE estimators appear to outperform those given by Hachemeister, and have led to a remarkable improvement in accuracy of the credibility estimators so constructed.

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11

Tsai, Miao-Yu, Chia-Ni Sun, and Chao-Chun Lin. "Concordance correlation coefficients estimated by modified variance components and generalized estimating equations for longitudinal overdispersed Poisson data." Statistical Methods in Medical Research 31, no. 2 (2021): 267–86. http://dx.doi.org/10.1177/09622802211065156.

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For longitudinal overdispersed Poisson data sets, estimators of the intra-, inter-, and total concordance correlation coefficient through variance components have been proposed. However, biased estimators of quadratic forms are used in concordance correlation coefficient estimation. In addition, the generalized estimating equations approach has been used in estimating agreement for longitudinal normal data and not for longitudinal overdispersed Poisson data. Therefore, this paper proposes a modified variance component approach to develop the unbiased estimators of the concordance correlation c

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12

Lee, Ji-Hyun, and Bahjat F. Qaqish. "A Latent Changepoint Model Using A Generalized Estimating Equations Approach." Communications in Statistics - Theory and Methods 34, no. 5 (2005): 1233–42. http://dx.doi.org/10.1081/sta-200056815.

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13

Niu, Yi, Xiaoguang Wang, Hui Cao, and Yingwei Peng. "Variable selection via penalized generalized estimating equations for a marginal survival model." Statistical Methods in Medical Research 29, no. 9 (2020): 2493–506. http://dx.doi.org/10.1177/0962280220901728.

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Clustered and multivariate survival times, such as times to recurrent events, commonly arise in biomedical and health research, and marginal survival models are often used to model such data. When a large number of predictors are available, variable selection is always an important issue when modeling such data with a survival model. We consider a Cox’s proportional hazards model for a marginal survival model. Under the sparsity assumption, we propose a penalized generalized estimating equation approach to select important variables and to estimate regression coefficients simultaneously in the

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14

Feddag, Mohand-Larbi, Ion Grama, and Mounir Mesbah. "Generalized Estimating Equations (GEE) for Mixed Logistic Models." Communications in Statistics - Theory and Methods 32, no. 4 (2003): 851–74. http://dx.doi.org/10.1081/sta-120018833.

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15

Chiou, Sy Han, Sangwook Kang, Junghi Kim, and Jun Yan. "Marginal semiparametric multivariate accelerated failure time model with generalized estimating equations." Lifetime Data Analysis 20, no. 4 (2014): 599–618. http://dx.doi.org/10.1007/s10985-014-9292-x.

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16

Shen, Chung-Wei, and Yi-Hau Chen. "Model selection of generalized estimating equations with multiply imputed longitudinal data." Biometrical Journal 55, no. 6 (2013): 899–911. http://dx.doi.org/10.1002/bimj.201200236.

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17

Kundu, Prosenjit, Runlong Tang, and Nilanjan Chatterjee. "Generalized meta-analysis for multiple regression models across studies with disparate covariate information." Biometrika 106, no. 3 (2019): 567–85. http://dx.doi.org/10.1093/biomet/asz030.

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Summary Meta-analysis is widely popular for synthesizing information on common parameters of interest across multiple studies because of its logistical convenience and statistical efficiency. We develop a generalized meta-analysis approach to combining information on multivariate regression parameters across multiple studies that have varying levels of covariate information. Using algebraic relationships among regression parameters in different dimensions, we specify a set of moment equations for estimating parameters of a maximal model through information available from sets of parameter esti

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18

Westgate, Philip M. "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." Clinical Trials 16, no. 1 (2018): 41–51. http://dx.doi.org/10.1177/1740774518803635.

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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 orde

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19

Seals, Samantha R., and Inmaculada B. Aban. "Analysis of the 17-segment left ventricle model using generalized estimating equations." Journal of Nuclear Cardiology 23, no. 5 (2015): 1110–11. http://dx.doi.org/10.1007/s12350-015-0186-4.

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20

Tsai, Miao-Yu, Jung-Feng Wang, and Jia-Ling Wu. "Generalized estimating equations with model selection for comparing dependent categorical agreement data." Computational Statistics & Data Analysis 55, no. 7 (2011): 2354–62. http://dx.doi.org/10.1016/j.csda.2011.02.002.

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21

Ristl, Robin, Ludwig Hothorn, Christian Ritz, and Martin Posch. "Simultaneous inference for multiple marginal generalized estimating equation models." Statistical Methods in Medical Research 29, no. 6 (2019): 1746–62. http://dx.doi.org/10.1177/0962280219873005.

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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 mo

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22

Hooker, Giles, Stephen P. Ellner, Laura De Vargas Roditi, and David J. D. Earn. "Parameterizing state–space models for infectious disease dynamics by generalized profiling: measles in Ontario." Journal of The Royal Society Interface 8, no. 60 (2010): 961–74. http://dx.doi.org/10.1098/rsif.2010.0412.

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Parameter estimation for infectious disease models is important for basic understanding (e.g. to identify major transmission pathways), for forecasting emerging epidemics, and for designing control measures. Differential equation models are often used, but statistical inference for differential equations suffers from numerical challenges and poor agreement between observational data and deterministic models. Accounting for these departures via stochastic model terms requires full specification of the probabilistic dynamics, and computationally demanding estimation methods. Here, we demonstrate

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23

Vantas, Konstantinos, Epaminondas Sidiropoulos, and Chris Evangelides. "Estimating Rainfall Erosivity from Daily Precipitation Using Generalized Additive Models." Environmental Sciences Proceedings 2, no. 1 (2020): 21. http://dx.doi.org/10.3390/environsciproc2020002021.

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One of the most important natural processes responsible for soil loss is rainfall-induced erosion. The calculation of rainfall erosivity, as defined in the Universal Soil Loss Equation, requires the availability of rainfall data, either continuous breakpoint, or pluviograph, with sampling intervals on the order of minutes. Due to the limited temporal coverage and spatial scarcity of such data, worldwide, alternative equations have been developed that utilize coarser rainfall records, in an effort to estimate erosivity equivalently to that calculated using pluviograph data. This paper presents

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24

Koper, Nicola, and Micheline Manseau. "Generalized estimating equations and generalized linear mixed-effects models for modelling resource selection." Journal of Applied Ecology 46, no. 3 (2009): 590–99. http://dx.doi.org/10.1111/j.1365-2664.2009.01642.x.

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25

Nikita, Efthymia. "The use of generalized linear models and generalized estimating equations in bioarchaeological studies." American Journal of Physical Anthropology 153, no. 3 (2013): 473–83. http://dx.doi.org/10.1002/ajpa.22448.

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26

Zubair, Seema, and Sanjoy K. Sinha. "Marginal models for longitudinal count data with dropouts." Journal of Statistical Research 54, no. 1 (2020): 27–42. http://dx.doi.org/10.47302/jsr.2020540102.

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In this article, we investigate marginal models for analyzing incomplete longitudinal count data with dropouts. Specifically, we explore commonly used generalized estimating equations and weighted generalized estimating equations for fitting log-linear models to count data in the presence of monotone missing responses. A series of simulations were carried out to examine the finite-sample properties of the estimators in the presence of both correctly specified and misspecified dropout mechanisms. An application is provided using actual longitudinal survey data from the Health and Retirement Stu

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27

Danlami, Nasiru, Madzlan Napiah, Ahmad Farhan M. Sadullah, and Nura Bala. "Estimating Annual Road Deaths: Comparison Between Temporal Causal Models and Generalized Estimating Equations." Advanced Science Letters 24, no. 11 (2018): 8679–82. http://dx.doi.org/10.1166/asl.2018.12323.

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28

Imori, Shinpei. "Model Selection Criterion Based on the Multivariate Quasi-Likelihood for Generalized Estimating Equations." Scandinavian Journal of Statistics 42, no. 4 (2015): 1214–24. http://dx.doi.org/10.1111/sjos.12160.

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29

Spiess, Martin, Daniel Fernández, Thuong Nguyen, and Ivy Liu. "Generalized estimating equations to estimate the ordered stereotype logit model for panel data." Statistics in Medicine 39, no. 14 (2020): 1919–40. http://dx.doi.org/10.1002/sim.8520.

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30

Oh, Sohee, K. C. Carriere, and Taesung Park. "Model diagnostic plots for repeated measures data using the generalized estimating equations approach." Computational Statistics & Data Analysis 53, no. 1 (2008): 222–32. http://dx.doi.org/10.1016/j.csda.2008.07.022.

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31

Gosho, Masahiko. "Model selection in the weighted generalized estimating equations for longitudinal data with dropout." Biometrical Journal 58, no. 3 (2015): 570–87. http://dx.doi.org/10.1002/bimj.201400045.

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32

Miroshnychenko, V. "Generalized least squares estimates for mixture of nonlinear regressions." Bulletin of Taras Shevchenko National University of Kyiv. Series: Physics and Mathematics, no. 3 (2018): 25–29. http://dx.doi.org/10.17721/1812-5409.2018/3.3.

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We consider data in which each observed subject belongs to one of different subpopulations (components). The true number of component which a subject belongs to is unknown, but the researcher knows the probabilities that a subject belongs to a given component (concentration of the component in the mixture). The concentrations are different for different observations. So the distribution of the observed data is a mixture of components’ distributions with varying concentrations. A set of variables is observed for each subject. Dependence between these variables is described by a nonlinear regres

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33

Ma, Yanyuan, and Marc G. Genton. "Explicit estimating equations for semiparametric generalized linear latent variable models." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 72, no. 4 (2010): 475–95. http://dx.doi.org/10.1111/j.1467-9868.2010.00741.x.

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34

Corrente, JosÉ Eduardo, and Maria Del Pilar DÍAz. "Ordinal models and generalized estimating equations to evaluate disease severity." Journal of Applied Statistics 30, no. 4 (2003): 425–39. http://dx.doi.org/10.1080/0266476032000035458.

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35

Hwang, Heungsun, and Yoshio Takane. "Estimation of Growth Curve Models with Structured Error Covariances by Generalized Estimating Equations." Behaviormetrika 32, no. 2 (2005): 155–63. http://dx.doi.org/10.2333/bhmk.32.155.

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36

Akanda, Md Abdus Salam, and Russell Alpizar-Jara. "A Generalized Estimating Equations Approach to Model Heterogeneity and Time Dependence in Capture-Recapture Studies." European Journal of Ecology 3, no. 1 (2017): 9–17. http://dx.doi.org/10.1515/eje-2017-0002.

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AbstractIndividual heterogeneity in capture probabilities and time dependence are fundamentally important for estimating the closed animal population parameters in capture-recapture studies. A generalized estimating equations (GEE) approach accounts for linear correlation among capture-recapture occasions, and individual heterogeneity in capture probabilities in a closed population capture-recapture individual heterogeneity and time variation model. The estimated capture probabilities are used to estimate animal population parameters. Two real data sets are used for illustrative purposes. A si

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37

Vonesh, Edward F., Hao Wang, Lei Nie, and Dibyen Majumdar. "Conditional Second-Order Generalized Estimating Equations for Generalized Linear and Nonlinear Mixed-Effects Models." Journal of the American Statistical Association 97, no. 457 (2002): 271–83. http://dx.doi.org/10.1198/016214502753479400.

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38

Hidayati, Rizka Dwi, I. Made Tirta, and Yuliani Setia Dewi. "The Efficiency of First (GEE1) and Second (GEE2) Order “Generalized Estimating Equations” for Longitudinal Data." Jurnal ILMU DASAR 15, no. 1 (2014): 29. http://dx.doi.org/10.19184/jid.v15i1.553.

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The approach of GEE focuses on a linear model for the mean of the observations in the cluster without full specification the distribution of full-on observation. GEE is a marginal model where is not based on the full likelihood of the response, but only based on the relationship between the mean (first moment) and variance (second moment) as well as the correlation matrix. The advantage of GEE is that the mean of parameter are estimated consistently regardless whether the correlation structure is specified correctly or not, as long as the mean has the correct specifications. However, the effic

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39

Inatsu, Yu, and Shinpei Imori. "Model selection criterion based on the prediction mean squared error in generalized estimating equations." Hiroshima Mathematical Journal 48, no. 3 (2018): 307–34. http://dx.doi.org/10.32917/hmj/1544238030.

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40

von Ranson, KM, SL Rosenthal, FM Biro, LM Lewis, and PA Succop. "Longitudinal risk of std acquisition in adolescent girls using a generalized estimating equations model." Journal of Pediatric and Adolescent Gynecology 13, no. 2 (2000): 87. http://dx.doi.org/10.1016/s1083-3188(00)00013-9.

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41

Cosbuc, Mircea I., Cristian Gatu, Ana Colubi, and Erricos John Kontoghiorghes. "A Generalized Singular Value Decomposition Strategy for Estimating the Block Recursive Simultaneous Equations Model." Computational Economics 50, no. 3 (2016): 503–15. http://dx.doi.org/10.1007/s10614-016-9595-y.

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42

Koper, Nicola, and Micheline Manseau. "A guide to developing resource selection functions from telemetry data using generalized estimating equations and generalized linear mixed models." Rangifer 32, no. 2 (2012): 195. http://dx.doi.org/10.7557/2.32.2.2269.

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Resource selection functions (RSF) are often developed using satellite (ARGOS) or Global Positioning System (GPS) telemetry datasets, which provide a large amount of highly correlated data. We discuss and compare the use of generalized linear mixed-effects models (GLMM) and generalized estimating equations (GEE) for using this type of data to develop RSFs. GLMMs directly model differences among caribou, while GEEs depend on an adjustment of the standard error to compensate for correlation of data points within individuals. Empirical standard errors, rather than model-based standard errors, mus

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43

Wang, Ming. "Generalized Estimating Equations in Longitudinal Data Analysis: A Review and Recent Developments." Advances in Statistics 2014 (December 1, 2014): 1–11. http://dx.doi.org/10.1155/2014/303728.

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Generalized Estimating Equation (GEE) is a marginal model popularly applied for longitudinal/clustered data analysis in clinical trials or biomedical studies. We provide a systematic review on GEE including basic concepts as well as several recent developments due to practical challenges in real applications. The topics including the selection of “working” correlation structure, sample size and power calculation, and the issue of informative cluster size are covered because these aspects play important roles in GEE utilization and its statistical inference. A brief summary and discussion of po

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44

Johnson, Timothy R., and Jee-Seon Kim. "A generalized estimating equations approach to mixed-effects ordinal probit models." British Journal of Mathematical and Statistical Psychology 57, no. 2 (2004): 295–310. http://dx.doi.org/10.1348/0007110042307177.

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45

Akanda, Md Abdus Salam, and Russell Alpizar-Jara. "A generalized estimating equations approach for capture–recapture closed population models." Environmental and Ecological Statistics 21, no. 4 (2014): 667–88. http://dx.doi.org/10.1007/s10651-014-0274-7.

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46

Lord, Dominique, and Bhagwant N. Persaud. "Accident Prediction Models With and Without Trend: Application of the Generalized Estimating Equations Procedure." Transportation Research Record: Journal of the Transportation Research Board 1717, no. 1 (2000): 102–8. http://dx.doi.org/10.3141/1717-13.

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Accident prediction models (APMs) are useful tools for estimating the expected number of accidents on entities such as intersections and road sections. These estimates typically are used in the identification of sites for possible safety treatment and in the evaluation of such treatments. An APM is, in essence, a mathematical equation that expresses the average accident frequency of a site as a function of traffic flow and other site characteristics. The reliability of an APM estimate is enhanced if the APM is based on data for as many years as possible, especially if data for those same years

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47

Schluchter, Mark D. "Flexible Approaches to Computing Mediated Effects in Generalized Linear Models: Generalized Estimating Equations and Bootstrapping." Multivariate Behavioral Research 43, no. 2 (2008): 268–88. http://dx.doi.org/10.1080/00273170802034877.

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48

Jentsch, Carsten, and Lena Reichmann. "Generalized Binary Time Series Models." Econometrics 7, no. 4 (2019): 47. http://dx.doi.org/10.3390/econometrics7040047.

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The serial dependence of categorical data is commonly described using Markovian models. Such models are very flexible, but they can suffer from a huge number of parameters if the state space or the model order becomes large. To address the problem of a large number of model parameters, the class of (new) discrete autoregressive moving-average (NDARMA) models has been proposed as a parsimonious alternative to Markov models. However, NDARMA models do not allow any negative model parameters, which might be a severe drawback in practical applications. In particular, this model class cannot capture

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49

Helms, Florian, Claudia Czado, and Susanne Gschlößl. "Calculation of LTC Premiums Based on Direct Estimates of Transition Probabilities." ASTIN Bulletin 35, no. 02 (2005): 455–69. http://dx.doi.org/10.2143/ast.35.2.2003462.

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In this paper we model the life-history of LTC-patients using a Markovian multi-state model in order to calculate premiums for a given LTC-plan. Instead of estimating the transition intensities in this model we use the approach suggested by Andersen et al. (2003) for a direct estimation of the transition probabilities. Based on the Aalen-Johansen estimator, an almost unbiased estimator for the transition matrix of a Markovian multi-state model, we calculate so-called pseudo-values, known from Jackknife methods. Further, we assume that the relationship between these pseudo-values and the covari

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50

Helms, Florian, Claudia Czado, and Susanne Gschlößl. "Calculation of LTC Premiums Based on Direct Estimates of Transition Probabilities." ASTIN Bulletin 35, no. 2 (2005): 455–69. http://dx.doi.org/10.1017/s0515036100014331.

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In this paper we model the life-history of LTC-patients using a Markovian multi-state model in order to calculate premiums for a given LTC-plan. Instead of estimating the transition intensities in this model we use the approach suggested by Andersen et al. (2003) for a direct estimation of the transition probabilities. Based on the Aalen-Johansen estimator, an almost unbiased estimator for the transition matrix of a Markovian multi-state model, we calculate so-called pseudo-values, known from Jackknife methods. Further, we assume that the relationship between these pseudo-values and the covari

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