Academic literature on the topic 'Generalized Estimating Equation approach (GEE)'
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Journal articles on the topic "Generalized Estimating Equation approach (GEE)"
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Shults, Justine, Sarah J. Ratcliffe, and Mary Leonard. "Improved Generalized Estimating Equation Analysis via xtqls for Quasi–Least Squares in Stata." Stata Journal: Promoting communications on statistics and Stata 7, no. 2 (2007): 147–66. http://dx.doi.org/10.1177/1536867x0700700201.
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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.
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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 efficiency may be reduced when the working correlation structure is wrong. GEE was designed to focus on the marginal mean and correlation structure as nuisiance treat. Implementation of GEE is usually limited to the number of working correlation structure (eg AR-1, exchangeable, independent, m-dependent and unstructured). To increase the efficiency of the GEE, has introduced a variation called the Generalized Estimating Equations order 2 (GEE2). GEE2 has been introduced to overcome the problem that considers correlation GEE as nuisiance, by applying the second equation to estimate covariance parameters and solved simultaneously with the first equation. This study used simulation data which are designed based on the the AR-1 and Exchangeable correlation structure, then estimation are done using theAR1 and exchangeable. For GEE2, estimation done by adding model for correlation link. The result is a link affects the efficiency of the model correlation is shown with standard error values generated by GEE2 method is smaller than the GEE method.
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Chaganty, N. Rao, Roy Sabo, and Yihao Deng. "Alternatives to Mixture Model Analysis of Correlated Binomial Data." ISRN Probability and Statistics 2012 (May 28, 2012): 1–10. http://dx.doi.org/10.5402/2012/896082.
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While univariate instances of binomial data are readily handled with generalized linear models, cases of multivariate or repeated measure binomial data are complicated by the possibility of correlated responses. Likelihood-based estimation can be applied by using mixture distribution models, though this approach can present computational challenges. The logistic transformation can be used to bypass these concerns and allow for alternative estimating procedures. One popular alternative is the generalized estimating equation (GEE) method, though systematic errors can lead to infeasible correlation estimates or nonconvergence problems. Our approach is the coupling of quasileast squares (QLSs) method with a rarely used matrix factorization, which achieves a simplified estimation platform—as compared to the mixture model approach—and does not suffer from the convergence problems in GEE method. A noncontrived example is provided that shows the mechanical breakdown of GEE using several statistical software packages and highlights the usefulness of the QLS approach.
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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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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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Lange, Christoph, and John C. Whittaker. "Mapping Quantitative Trait Loci Using Generalized Estimating Equations." Genetics 159, no. 3 (2001): 1325–37. http://dx.doi.org/10.1093/genetics/159.3.1325.
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AbstractA number of statistical methods are now available to map quantitative trait loci (QTL) relative to markers. However, no existing methodology can simultaneously map QTL for multiple nonnormal traits. In this article we rectify this deficiency by developing a QTL-mapping approach based on generalized estimating equations (GEE). Simulation experiments are used to illustrate the application of the GEE-based approach.
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Ghisletta, Paolo, and Dario Spini. "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 29, no. 4 (2004): 421–37. http://dx.doi.org/10.3102/10769986029004421.
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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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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 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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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 simulation study is carried out to assess the performance of the GEE estimator. A Quasi-Likelihood Information Criterion (QIC) is applied for the selection of the best fitting model. This approach performs well when the estimated population parameters depend on the individual heterogeneity and the nature of linear correlation among capture-recapture occasions.
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Turner, Elizabeth L., Lanqiu Yao, Fan Li, and Melanie Prague. "Properties and pitfalls of weighting as an alternative to multilevel multiple imputation in cluster randomized trials with missing binary outcomes under covariate-dependent missingness." Statistical Methods in Medical Research 29, no. 5 (2019): 1338–53. http://dx.doi.org/10.1177/0962280219859915.
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The generalized estimating equation (GEE) approach can be used to analyze cluster randomized trial data to obtain population-averaged intervention effects. However, most cluster randomized trials have some missing outcome data and a GEE analysis of available data may be biased when outcome data are not missing completely at random. Although multilevel multiple imputation for GEE (MMI-GEE) has been widely used, alternative approaches such as weighted GEE are less common in practice. Using both simulations and a real data example, we evaluate the performance of inverse probability weighted GEE vs. MMI-GEE for binary outcomes. Simulated data are generated assuming a covariate-dependent missing data pattern across a range of missingness clustering (from none to high), where all covariates are measured at baseline and are fully observed (i.e. a type of missing-at-random mechanism). Two types of weights are estimated and used in the weighted GEE: (1) assuming no clustering of missingness (W-GEE) and (2) accounting for such clustering (CW-GEE). Results show that, even in settings with high missingness clustering, CW-GEE can lead to more bias and lower coverage than W-GEE, whereas W-GEE and MMI-GEE provide comparable results. W-GEE should be considered a viable strategy to account for missing outcomes in cluster randomized trials.
Dissertations / Theses on the topic "Generalized Estimating Equation approach (GEE)"
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Lyth, Johan. "En jämförelse mellan individers självuppskattade livskvalitet och samhällets hälsopreferenser : En paneldatastudie av hjärtpatienter." Thesis, Linköpings universitet, Matematiska institutionen, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-15095.
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Objective: In recent years there has been an increasing interest within the clinical (medical) science in measuring people’s health. When estimating quality of life, present practise is to use the EQ-5D questionnaire and an index which weighs the different questions. The question is what happens if the individuals estimate there own health, would it differ from the public preferences? The aim is to make a new prediction model based on the opinion of patients and compare it to the present model based on public preferences. Method: A sample of 362 patients with unstable coronary artery disease from the Frisc II trial, valued their quality of life in the acute phase and after 3, 6 and 12 months. The EQ-5D question form and also the Time Trade-off method (TTO), a direct method of valuing health was used. A regression technique managing panel data had to be used in estimating TTO by the EQ-5D and other variables like gender and age. Result: Different regression techniques vary in estimating parameters and standard errors. A Generalized Estimating Equation approach with empirical correlation structure is the most suitable regression technique for the data material. A model based on the EQ-5D question form and a continuous age variable proves to be the best model for an index derived by individuals. The difference between heart patients own opinion of health and the public preferences differs a great amount in the severe health conditions, but are rather small for healthy patients. Of the total 243 health conditions, only eight of the conditions were estimated higher by the public index. Conclusions: As the differences between the approaches are significantly large the choice of index could affect the decision making in a health economic study.
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Sagara, Issaka. "Méthodes d'analyse statistique pour données répétées dans les essais cliniques : intérêts et applications au paludisme." Thesis, Aix-Marseille, 2014. http://www.theses.fr/2014AIXM5081/document.
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De nombreuses études cliniques ou interventions de lutte ont été faites ou sont en cours en Afrique pour la lutte contre le fléau du paludisme. En zone d'endémie, le paludisme est une maladie récurrente. La revue de littérature indique une application limitée des outils statistiques appropriés existants pour l'analyse des données récurrentes de paludisme. Nous avons mis en oeuvre des méthodes statistiques appropriées pour l'analyse des données répétées d'essais thérapeutiques de paludisme. Nous avons également étudié les mesures répétées d'hémoglobine lors du suivi de traitements antipaludiques en vue d'évaluer la tolérance ou sécurité des médicaments en regroupant les données de 13 essais cliniques.Pour l'analyse du nombre d'épisodes de paludisme, la régression binomiale négative a été mise en oeuvre. Pour modéliser la récurrence des épisodes de paludisme, quatre modèles ont été utilisés : i) Les équations d'estimation généralisées (GEE) utilisant la distribution de Poisson; et trois modèles qui sont une extension du modèle Cox: ii) le modèle de processus de comptage d'Andersen-Gill (AG-CP), iii) le modèle de processus de comptage de Prentice-Williams-Peterson (PWP-CP); et iv) le modèle de Fragilité partagée de distribution gamma. Pour l'analyse de sécurité, c'est-à-dire l'évaluation de l'impact de traitements antipaludiques sur le taux d'hémoglobine ou la survenue de l'anémie, les modèles linéaires et latents généralisés mixtes (« GLLAMM : generalized linear and latent mixed models ») ont été mis en oeuvre. Les perspectives sont l'élaboration de guides de bonnes pratiques de préparation et d'analyse ainsi que la création d'un entrepôt des données de paludisme<br>Numerous clinical studies or control interventions were done or are ongoing in Africa for malaria control. For an efficient control of this disease, the strategies should be closer to the reality of the field and the data should be analyzed appropriately. In endemic areas, malaria is a recurrent disease. Repeated malaria episodes are common in African. However, the literature review indicates a limited application of appropriate statistical tools for the analysis of recurrent malaria data. We implemented appropriate statistical methods for the analysis of these data We have also studied the repeated measurements of hemoglobin during malaria treatments follow-up in order to assess the safety of the study drugs by pooling data from 13 clinical trials.For the analysis of the number of malaria episodes, the negative binomial regression has been implemented. To model the recurrence of malaria episodes, four models were used: i) the generalized estimating equations (GEE) using the Poisson distribution; and three models that are an extension of the Cox model: ii) Andersen-Gill counting process (AG-CP), iii) Prentice-Williams-Peterson counting process (PWP-CP); and (iv) the shared gamma frailty model. For the safety analysis, i.e. the assessment of the impact of malaria treatment on hemoglobin levels or the onset of anemia, the generalized linear and latent mixed models (GLLAMM) has been implemented. We have shown how to properly apply the existing statistical tools in the analysis of these data. The prospects of this work remain in the development of guides on good practices on the methodology of the preparation and analysis and storage network for malaria data
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Purnomo, Jerry Dwi Trijoyo, and 溥杰瑞. "A Modified Generalized Estimating Equation (GEE) Approach for Latent Class Models with Covariate Effects on Measured and Underlying Variables." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/t79rdw.
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博士<br>國立交通大學<br>統計學研究所<br>106<br>Recently, the regression extension of latent class analysis (RLCA) models have played an important role in many fields of research. RLCA models establish the relationship between primary covariates and latent class membership as well as the mediated direct effect of secondary covariates on measured responses. They have proven helpful for analyzing the relationship between measured multiple responses and covariates of interest. In this paper, we propose a generalized estimating equation (GEE) approach for the parameter estimation of RLCA models. This approach allows the specification of a working covariance that can ease the specification of the true covariance structure. We detail several structures of working covariance, iterative algorithms of Gauss-Newton methods for parameter estimation, and procedures for obtaining covariances of parameter estimators. An analysis of variables that probably affect the frailty of patients with cancer is used for illustration.
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Hasan, Md Tariqul. "Analyzing longitudinally correlated failure time data : a generalized estimating equation approach /." 2001.
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Song, Xiaoyu. "A New Estimating Equation Based Approach for Secondary Trait Analyses in Genetic Case-control Studies." Thesis, 2015. https://doi.org/10.7916/D8T15DB5.
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Background/Aims: Case-control designs are commonly employed in genetic association studies. In addition to the primary trait of interest, data on additional secondary traits, related to the primary trait, are often collected. Traditional association analyses between genetic variants and secondary traits can be biased in such cases, and several methods have been proposed to address this issue, including the inverse-probability-of-sampling-weighted (IPW) approach and semi-parametric maximum likelihood (SPML) approach.
Methods: Here, we propose a set of new estimating equation based approach that combines observed and counter-factual outcomes to provide unbiased estimation of genetic associations with secondary traits. We extend the estimating equation framework to both generalized linear models (GLM) and non-parametric regressions, and compare it with the existing approaches.
Results: We demonstrate analytically and numerically that our proposed approach provides robust and fairly efficient unbiased estimation in all simulations we consider. Unlike existing methods, it is less sensitive to the sampling scheme and underlying disease model specification. In addition, we illustrate our new approach using two real data examples. The first one is to analyze the binary secondary trait diabetes under GLM framework using a stroke case-control study. The second one is to analyze the continuous secondary trait serum IgE levels under linear and quantile regression models using an asthma case-control study.
Conclusion: The proposed new estimating equation approach is able to accommodate a wide range of regressions, and it outperforms the existing approaches in some scenarios we consider.
Conference papers on the topic "Generalized Estimating Equation approach (GEE)"
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Awalluddin, Asep S., Inge Wahyuni, and Hilda Nurmuslimah. "Analysis of Longitudinal Regression Model Using the Generalized Estimating Equation (GEE) for the Child Welfare Composite Index (CWCI) in West Java." In 1st International Conference on Mathematics and Mathematics Education (ICMMEd 2020). Atlantis Press, 2021. http://dx.doi.org/10.2991/assehr.k.210508.094.
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Galski, Roberto Luiz, Heitor Patire Ju´nior, Fabiano Luis de Sousa, Jose´ Nivaldo Hinckel, Pedro Lacava, and Fernando Manuel Ramos. "GEO + ES Hybrid Optimization Algorithm Applied to the Parametric Thermal Model Estimation of a 200N Hydrazine Thruster." In ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/detc2011-47584.
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In the present paper, a hybrid version of the Generalized Extremal Optimization (GEO) and Evolution Strategies (ES) algorithms [1], developed in order to conjugate the convergence properties of GEO with the self-tuning characteristics present in the ES, is applied to the estimation of the temperature distribution of the film cooling near the internal wall of a thruster. The temperature profile is determined through an inverse problem approach using the hybrid. The profile was obtained for steady-state conditions, were the external wall temperature along the thruster is considered as a known input. The Boltzmann’s equation parameters [2], which define the cooling film temperature profile, are the design variables. Results using simulated data showed that this approach was efficient in recuperating those parameters. The approach showed here can be used on the design of thrusters with lower wall temperatures, which is a desirable feature of such devices.
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Abdullayev, P. S. "Fuzzy Statistics Approach for Aviation GTE Condition Estimation Technique." In ASME 8th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2006. http://dx.doi.org/10.1115/esda2006-95791.
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In this paper is shown that the probability-statistic methods application, especially at the early stage of the aviation gas turbine engine (GTE) technical condition diagnosing, when the flight information has property of the fuzzy, limitation and uncertainty is unfounded. Hence is considered the efficiency of application of new technology Soft Computing at these diagnosing stages with the using of the Fuzzy Logic and Neural Networks methods. Training with high accuracy of fuzzy multiple linear and non-linear models (fuzzy regression equations) which received on the statistical fuzzy data basis is made. Thus for GTE technical condition more adequate model making are analysed dynamics of skewness and kurtosis coefficients’ changes. Researches of skewness and kurtosis coefficients values’ changes show that, distributions of GTE work parameters have fuzzy character. Hence consideration of fuzzy skewness and kurtosis coefficients is expedient. Investigation of the basic characteristics changes’ dynamics of GTE work parameters allows to draw conclusion on necessity of the Fuzzy Statistical Analysis at preliminary identification of the engines’ technical condition. Researches of correlation coefficients values’ changes shows also on their fuzzy character. Therefore for models choice the application of the Fuzzy Correlation Analysis results is offered. For checking of models adequacy is considered the Fuzzy Multiple Correlation Coefficient of Fuzzy Multiple Regression. At the information sufficiency is offered to use recurrent algorithm of aviation GTE technical condition identification (Hard Computing technology is used) on measurements of input and output parameters of the multiple linear and nonlinear generalised models at presence of noise measured (the new recursive Least Squares Method (LSM)). The developed GTE condition monitoring system provides stage-by-stage estimation of engine technical conditions. As application of the given technique the estimation of the new operating aviation engine temperature condition was made.
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Yang, Qingcai, Yunpeng Cao, Fang Yu, Jianwei Du, and Shuying Li. "Health Estimation of Gas Turbine: A Symbolic Linearization Model Approach." In ASME Turbo Expo 2017: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/gt2017-64071.
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This paper is mainly concerned with the health estimation of a gas turbine using a symbolic linearization model approach. Health parameters will change with the degradation of gas turbine performance. Monitoring and evaluating these health parameters can assist in the development of predictive control techniques and maintenance schedules. Currently, various health parameter estimation methods have been studied extensively, but there have been less related studies on how to obtain statespace models. In this paper, a symbolic linearization model method is presented to overcome the shortcoming of high time consumption suffered by existing methods. In this method, each component model of the dynamic nonlinear gas turbine model is decomposed into several sub-modules, each of which contains a simple nonlinear equation. By means of symbolic computation, a linear model of the components is derived by linearizing these sub-modules, and then the generalized linear state-space model of the gas turbine is derived from the relationship among the components. In the generalized linear state-space model, the Jacobian matrices are functions of the parameters under a steady-state operating condition. Therefore, it is easy to obtain a linear model that represents the dynamics of the gas turbine under a given operating condition. To estimate the health parameters of a gas turbine, a piecewise linear model is developed using the proposed approach, and this model is verified in a simulation environment. The results show that the developed piecewise linear model can capture the behavior of a gas turbine quite closely. Then, a linearized Kalman filter is designed for estimating the health parameters under steady-state and transient conditions. The results show that the generalized linear model established using the presented method can be used to accurately estimate the health parameters of a gas turbine.