Journal articles: 'Generalised estimating equations (GEE)'

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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, summarize general findings on the choice of the working correlation matrix, and show the existence of a dilemma for the optimal choice of the working correlation matrix for dichotomous dependent variables. Results: Biological and statistical arguments for choosing a specific working correlation matrix are given. Three approaches are described for overcoming the range restriction of the correlation coefficient. Conclusions: The three approaches described in this article for overcoming the range restrictions for dichotomous dependent variables in GEE models provide a simple and practical way for use in applications.

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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 (August 7, 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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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 (November 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 (November 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 (November 1, 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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Breitung, J., N. R. Chaganty, R. M. Daniel, M. G. Kenward, M. Lechner, P. Martus, R. T. Sabo, Y. G. Wang, and C. Zorn. "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 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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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 potential research interests regarding GEE are provided in the end.

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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 (January 4, 2003): 851–74. http://dx.doi.org/10.1081/sta-120018833.

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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 (December 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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Sahin, Fezan, Unal Ayranci, Setenay Oner, Canan Demirustu, Cengiz Bal, Ertugrul Colak, Cinar Yenilmez, Kazim Ozdamar, and Gulten Seber. "FACTORS INFLUENCING STUDENTS' SUCCESS: A GENERALIZED ESTIMATING EQUATIONS STUDY." Social Behavior and Personality: an international journal 35, no. 7 (January 1, 2007): 987–96. http://dx.doi.org/10.2224/sbp.2007.35.7.987.

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The goals of this study were to determine the factors influencing children's success according to Generalized Estimating Equations (GEE) through the Family Report Form (FRF; constituted by the authors), Teacher's Report Form (TRF; Achenbach, 1991b) and Child Behavior Check List (CBCL; Achenbach & Edelbrock, 1983), and to form a new and actual reference success form for Turkish children. The study sample included 4130 primary school children in Eskisehir, Turkey. School success was measured as the total scores that students achieved in the following classes: cultural, social, science, foreign language, computer, picture, music and job-occupation. The TRF, CBCL and FRF forms consist of the sum of subtests, which includes 9 factor scales, namely anxiety/depression, somatic complaints, social withdrawal, delinquent behavior, social problems, thought problems, attention deficits, aggressive behavior, and other. As the statistical analyses, GEE method, stepwise regression analysis and missing value analyses were used. The items that had the greatest negative effect on the students' success were “has poor school work” and “can't concentrate or can't pay attention for long”. On the other hand, the item that had the greatest positive effect on students' success was “feels he/she has to be perfect”. It may be recommended that students, their families, and teachers should be informed about factors influencing school success.

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Im, Seongah. "Performance of the Beta-Binomial Model for Clustered Binary Responses: Comparison with Generalized Estimating Equations." Journal of Modern Applied Statistical Methods 19, no. 1 (August 16, 2021): 2–25. http://dx.doi.org/10.22237/jmasm/1619482380.

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This study examined performance of the beta-binomial model in comparison with GEE using clustered binary responses resulting in non-normal outcomes. Monte Carlo simulations were performed under varying intracluster correlations and sample sizes. The results showed that the beta-binomial model performed better for small sample, while GEE performed well under large sample.

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Brobbey, Anita, Samuel Wiebe, Alberto Nettel-Aguirre, Colin Bruce Josephson, Tyler Williamson, Lisa M. Lix, and Tolulope T. Sajobi. "Repeated measures discriminant analysis using multivariate generalized estimation equations." Statistical Methods in Medical Research 31, no. 4 (December 13, 2021): 646–57. http://dx.doi.org/10.1177/09622802211032705.

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Discriminant analysis procedures that assume parsimonious covariance and/or means structures have been proposed for distinguishing between two or more populations in multivariate repeated measures designs. However, these procedures rely on the assumptions of multivariate normality which is not tenable in multivariate repeated measures designs which are characterized by binary, ordinal, or mixed types of response distributions. This study investigates the accuracy of repeated measures discriminant analysis (RMDA) based on the multivariate generalized estimating equations (GEE) framework for classification in multivariate repeated measures designs with the same or different types of responses repeatedly measured over time. Monte Carlo methods were used to compare the accuracy of RMDA procedures based on GEE, and RMDA based on maximum likelihood estimators (MLE) under diverse simulation conditions, which included number of repeated measure occasions, number of responses, sample size, correlation structures, and type of response distribution. RMDA based on GEE exhibited higher average classification accuracy than RMDA based on MLE especially in multivariate non-normal distributions. Three repeatedly measured responses namely severity of epilepsy, current number of anti-epileptic drugs, and parent-reported quality of life in children with epilepsy were used to demonstrate the application of these procedures.

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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 (March 28, 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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Rochon, J., I. R. König, A. Ziegler, and G. Dahmen. "Sample Size Calculations for Controlled Clinical Trials Using Generalized Estimating Equations (GEE)." Methods of Information in Medicine 43, no. 05 (2004): 451–56. http://dx.doi.org/10.1055/s-0038-1633896.

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Summary Objectives: Clinical trials with correlated response data based on generalized estimating equations (GEE) have become increasingly popular as they require smaller samples than classical methods that ignore the clustered nature of the data. We have recently derived the recommendation to use the independence estimating equations (IEE) as primary analysis in most controlled clinical trials instead of GEE with estimated correlations [1]. Although several approaches for sample size and power calculation have been proposed, we have shown that most of these procedures are very specific and not as general as required for designing clinical trials. Methods: We extended the previously developed SAS macro GEESIZE to overcome this restriction. Specifically, we have added the option of an independence working correlation matrix required for the IEE. Additionally, we have reformulated the hypotheses to allow for coding that includes an intercept term instead of the previously used analysis of variance coding. Results: To demonstrate the validity of GEESIZE we investigate the calculated sample sizes for specific models where closed formulae are available. For illustration, we utilize GEESIZE for planning a new trial on the treatment of hypertension and thereby exemplify its flexibility. Conclusions: We show that our freely available macro is a very general and useful tool for sample size calculation purposes in clinical trials with correlated data.

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Ahn, Jeongmin, and Eunkyung Yoon. "Revisited Study on Korean Stops’ Perception Test using GEE (Generalized Estimating Equations)." Studies in Linguistics 50 (January 31, 2019): 1–18. http://dx.doi.org/10.17002/sil..50.201901.1.

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Lange, C. "A multivariate family-based association test using generalized estimating equations: FBAT-GEE." Biostatistics 4, no. 2 (April 1, 2003): 195–206. http://dx.doi.org/10.1093/biostatistics/4.2.195.

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Yonar, Harun, and Neslihan İyit. "Some Generalized Estimating Equations Models Based on Causality Tests for Investigation of The Economic Growth of The Country Groups." Foundations of Computing and Decision Sciences 46, no. 3 (September 1, 2021): 297–315. http://dx.doi.org/10.2478/fcds-2021-0019.

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Abstract In this study, investigation of the economic growth of the Organization for Economic Cooperation and Development (OECD) countries and the countries in different income groups in the World Data Bank is conducted by using causality analyses and Generalized Estimating Equations (GEEs) which is an extension of Generalized Linear Models (GLMs). Eight different macro-economic, energy and environmental variables such as the gross domestic product (GDP) (current US$), CO2 emission (metric tons per capita), electric power consumption (kWh per capita), energy use (kg of oil equivalent per capita), imports of goods and services (% of GDP), exports of goods and services (% of GDP), foreign direct investment (FDI) and population growth rate (annual %) have been used. These countries have been categorized according to their OECD memberships and income groups. The causes of the economic growth of these countries belonging to their OECD memberships and income groups have been determined by using the Toda-Yamamoto causality test. Furthermore, various GEE models have been established for the economic growth of these countries belonging to their OECD membership and income groups in the aspect of the above variables. These various GEE models for the investigation of the economic growth of these countries have been compared to examine the contribution of the causality analyses to the statistical model establishment. As a result of this study, the highlight is found as the use of causally-related variables in the causality-based GEE models is much more appropriate than in the non-causality based GEE models for determining the economic growth profiles of these countries.

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Wang, Ming, Lan Kong, Zheng Li, and Lijun Zhang. "Covariance estimators for generalized estimating equations (GEE) in longitudinal analysis with small samples." Statistics in Medicine 35, no. 10 (November 19, 2015): 1706–21. http://dx.doi.org/10.1002/sim.6817.

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Wang, Ming, Lan Kong, Zheng Li, and Lijun Zhang. "Covariance estimators for Generalized Estimating Equations (GEE) in longitudinal analysis with small samples." Statistics in Medicine 35, no. 28 (November 3, 2016): 5318–19. http://dx.doi.org/10.1002/sim.7131.

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Becker, Sara K. "Evaluating elbow osteoarthritis within the prehistoric Tiwanaku state using generalized estimating equations (GEE)." American Journal of Physical Anthropology 169, no. 1 (March 2019): 186–96. http://dx.doi.org/10.1002/ajpa.23806.

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Daza, Eric J., Michael G. Hudgens, and Amy H. Herring. "Estimating Inverse-probability Weights for Longitudinal Data with Dropout or Truncation: The Xtrccipw Command." Stata Journal: Promoting communications on statistics and Stata 17, no. 2 (June 2017): 253–78. http://dx.doi.org/10.1177/1536867x1701700202.

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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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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 (January 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 are used in the safety analysis of a site. With many years of data, however, it is necessary to account for the year-to-year variation, or trend, in accident counts because of the influence of factors that change every year. To capture this variation, the count for each year is treated as a separate observation. Unfortunately, the disaggregation of the data in this manner creates a temporal correlation that presents difficulties for traditional model calibration procedures. An application is presented of a generalized estimating equations (GEE) procedure to develop an APM that incorporates trend in accident data. Data for the application pertain to a sample of four-legged signalized intersections in Toronto, Canada, for the years 1990 through 1995. The GEE model incorporating the time trend is shown to be superior to models that do not accommodate trend and/or the temporal correlation in accident data.

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Fu, Liya, Zhuoran Yang, Mingtao Zhao, and Yan Zhou. "Efficient parameter estimation for multivariate accelerated failure time model via the quadratic inference functions method." Random Matrices: Theory and Applications 08, no. 04 (October 2019): 1950013. http://dx.doi.org/10.1142/s2010326319500138.

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A popular approach, generalized estimating equations (GEE), has been applied to the multivariate accelerated failure time (AFT) model of the clustered and censored data. However, this method needs to estimate the correlation parameters and calculate the inverse of the correlation matrix. Meanwhile, the efficiency of the parameter estimators is low when the correlation structure is misspecified and/or the marginal distribution is heavy-tailed. This paper proposes using the quadratic inference functions (QIF) with a mixture correlation structure to estimate the coefficients in the multivariate AFT model, which can avoid estimating the correlation parameters and computing the inverse matrix of the correlation matrix. Moreover, the estimator derived from the QIF is consistent and asymptotically normal. Simulation studies indicate that the proposed method outperforms the method based on GEE when the marginal distribution has a heavy tail. Finally, the proposed method is used to analyze a real dataset for illustration.

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Manuel, Lourenço, Jackelya Araujo Da Silva, and João Domingos Scalon. "Driving factors toward adoption of improved maize varieties in Mozambique. An approach based on generalized estimating equations for spatial structured data / Determinantes da adopção de variedades melhoradas de milho: Uma abordagem baseada em equações de estimação generalizadas para dados com estrutura espacial." Brazilian Journal of Development 8, no. 1 (January 17, 2022): 4287–302. http://dx.doi.org/10.34117/bjdv8n1-284.

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Maize is one of the main economic crops and staple food in Mozambique. However, despite the importance of the crop in the country, maize productivity is still low due to several factors including low adoption of improved agricultural technologies. This paper aimed to identify the main factors driving adoption of improved maize varieties applying generalized estimating equations (GEE). The motivation for this class of models is due to the fact that adoption of improved maize varieties is a spatial auto correlated variable and the traditional probit and logit models widely applied in studies of adoption of agricultural technologies do not take into account the structure of correlation existing in the response variable. The study uses data from Integrated Agrarian Survey of 2012 (IAI 2012). The proportion of small farmers who adopted improved maize varieties per district was used as response variable and a set of nine variables were used as covariates classified in social, economic, institutional and technologic factors. The spatial auto correlation of the dependent variable was assessed by global and local Moran indexes. Two classes of models were fitted: The traditional logistic regression (logit model) and the generalized estimating equations approach. The inclusion of spatial auto correlation in GEE was carried out inserting the Moran’s index in the working correlation matrix. The results have shown that the GEE approach for spatial lattice data was the best and all factors analysed in the study including the spatial dependency are the main factors driving adoption of improved maize varieties in Mozambique.

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M, Amany, Mousa, Ahmed A, El sheikh, Fatma El Zahraa S. Salama, and Ahmed M. Gad. "Reviewing of Different Methods for Handling Longitudinal Count data." Journal of University of Shanghai for Science and Technology 23, no. 08 (August 7, 2021): 195–206. http://dx.doi.org/10.51201/jusst/21/08349.

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In this paper, we will review the methods that used to handle longitudinal data in the case of marginal models when inferences about the population average are the primary focus [1] or when future applications of the results require the expectation of the response as a function of the current covariates [7]. We will review the generalized estimating equations method (GEE), quadratic inference functions (QIF), generalized quasi likelihood (GQL) and the generalized method of moments (GMM). These methods will be reviewed by discussing its advantages and disadvantages in more details.

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Rogers, Paul, and Julie Stoner. "Assessment of a Modified Sandwich Estimator for Generalized Estimating Equations with Application to Opioid Poisoning in MIMIC-IV ICU Patients." Stats 4, no. 3 (August 12, 2021): 650–64. http://dx.doi.org/10.3390/stats4030039.

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Longitudinal data is encountered frequently in many healthcare research areas to include the critical care environment. Repeated measures from the same subject are expected to correlate with each other. Models with binary outcomes are commonly used in this setting. Regression models for correlated binary outcomes are frequently fit using generalized estimating equations (GEE). The Liang and Zeger sandwich estimator is often used in GEE to produce unbiased standard error estimation for regression coefficients in large sample settings, even when the covariance structure is misspecified. The sandwich estimator performs optimally in balanced designs when the number of participants is large with few repeated measurements. The sandwich estimator’s asymptotic properties do not hold in small sample and rare-event settings. Under these conditions, the sandwich estimator underestimates the variances and is biased downwards. Here, the performance of a modified sandwich estimator is compared to the traditional Liang-Zeger estimator and alternative forms proposed by authors Morel, Pan, and Mancl-DeRouen. Each estimator’s performance was assessed with 95% coverage probabilities for the regression coefficients using simulated data under various combinations of sample sizes and outcome prevalence values with independence and autoregressive correlation structures. This research was motivated by investigations involving rare-event outcomes in intensive care unit settings.

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Glover, Ian D., David C. Barrett, and Kristen K. Reyher. "Little association between birth weight and health of preweaned dairy calves." Veterinary Record 184, no. 15 (March 1, 2019): 477. http://dx.doi.org/10.1136/vr.105062.

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Intrauterine growth retardation (IUGR) may result in reduced birthweight and detrimental physiological alterations in neonates. This prospective cohort study was designed to assess if there exists an association between birthweight of dairy calves and incidence of bovine respiratory disease (BRD), neonatal calf diarrhoea (NCD) or mortality during the pre-weaning period. Calves (n=476) on 3 farms in South West England were weighed at birth. Farmers kept records of treatments for NCD and BRD and calves were assessed weekly using clinical scoring systems (Wisconsin Calf Health Scores, California Calf Health Scores and Faeces Scores). Missing data were present in several variables. Multiple imputation coupled with generalised estimating equations (MI-GEE analysis) was employed to analyse associations between several calf factors, including birthweight, and probability of a case of BRD or NCD. Associations between calf factors and mortality were assessed using multiple logistic regression. Associations between birthweight and disease incidence were scarce. Birthweight was associated with odds of a positive Faeces Score on one farm only in the MI-GEE analysis (O.R. 1.03, 95% C.I. 1.0005–1.05, P=0.046). Birthweight was not associated with probability of mortality. This research suggests that birthweight, and therefore IUGR, is not associated with health of pre-weaned dairy calves.

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Harding, Katharine, Elaine Kingwell, Mark Wardle, Feng Zhu, Neil Robertson, and Helen Tremlett. "WED 167 Socioeconomic status and progression of disability in ms." Journal of Neurology, Neurosurgery & Psychiatry 89, no. 10 (September 13, 2018): A21.1—A21. http://dx.doi.org/10.1136/jnnp-2018-abn.75.

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There is evidence that socioeconomic status (SES) is associated with multiple sclerosis (MS) incidence; however it is less clear whether there is also an association with long-term prognosis.3113 patients were selected from the MS registries of British Columbia, Canada (n=2069), and Cardiff, Wales (n=1044). SES, based on neighbourhood-level average income, was measured at onset of MS. Cox proportional hazards regression was used to analyse the association of SES with time to sustained and confirmed EDSS 6.0 and EDSS 4.0. The association between SES and EDSS scores was assessed longitudinally by a linear regression model fitted using generalised estimating equations (GEE) with an exchangeable working correlation structure. All models were adjusted for age at onset, sex, year of onset, initial course and DMT. The cohorts were analysed individually and results combined using meta-analysis.SES was associated with hazard of reaching EDSS 6.0 (adjusted hazard ratio [aHR]=0.90, 95% CI 0.89–0.91), and 4.0 (aHR=0.93, 0.88–0.98). GEE modelling confirmed association of SES with EDSS (β=−0.13, [−0.18- −0.08], p<0.001). We found evidence that lower SES is associated with poorer outcomes. Reasons for this are complex but may include lifestyle or comorbidity. Our findings are relevant for planning and development of MS services.

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Bingefors, Kerstin, Dag Isacson, Lars Von Knorring, Björn Smedby, Lisa Ekselius, and Lawrence L. Kupper. "Antidepressant-treated Patients in Ambulatory Care." British Journal of Psychiatry 168, no. 3 (March 1996): 292–98. http://dx.doi.org/10.1192/bjp.168.3.292.

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BackgroundDespite the problems involved in treating depression and concomitant medical disease, there are virtually no longitudinal studies on drug utilisation among depressed patients.MethodUse of prescription drugs among all first-time users of antidepressants in a defined population five years before and six years after the index (first) treatment was compared to a referent group without antidepressant treatment. The generalised estimating equations (GEE) method was used for analysis.ResultsThe antidepressant-treated group used considerably more non-psychotropic drugs during the whole study period than the referent group. They also used more psychotropic drugs, a use which increased in connection with the initiation of antidepressant treatment and stayed high for a further five years.ConclusionsThe high use of prescription drugs indicated widespread somatic and psychiatric health problems during the whole study period. Antidepressant-treated patients are at risk for drug interactions and adverse effects, and would benefit from a closer collaboration between psychiatry and medicine.

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Ho, Janice D., James F. Tsang, Kylie A. Scoggan, and William D. Leslie. "Urinary Iodine Clearance following Iodinated Contrast Administration: A Comparison of Euthyroid and Postthyroidectomy Subjects." Journal of Thyroid Research 2014 (2014): 1–4. http://dx.doi.org/10.1155/2014/580569.

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Purpose.To compare iodine clearance following iodinated contrast administration in thyroidectomised thyroid cancer patients and euthyroid individuals.Methods.A convenience population (6 thyroidectomised thyroid cancer patients and 7 euthyroid controls) was drawn from patients referred for iodinated contrast-enhanced computed tomography (CT) studies. Subjects had sequential urine samples collected up to 6 months (50 samples from the thyroidectomised and 63 samples from the euthyroid groups).t-tests and generalised estimating equations (GEE) were used to test for group differences in urinary iodine creatinine ratios.Results.Groups had similar urinary iodine creatinine ratios at baseline, with a large increase 2 weeks following iodinated contrast (P=0.005). Both groups had a return of urinary iodine creatinine ratios to baseline by 4 weeks, with no significant group differences overall or at any time point.Conclusions.Thyroidectomised patients did not have a significantly different urinary iodine clearance than euthyroid individuals following administration of iodinated contrast. Both had a return of urinary iodine creatinine ratios to baseline within 4 weeks.

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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 (October 8, 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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Yang, Tim Z. T., Marcus Y. Chen, Tim R. H. Read, Robert Needleman, Catriona S. Bradshaw, Ria Fortune, Christopher K. Fairley, and Eric P. F. Chow. "Sampling technique and detection rates of oropharyngeal and anorectal gonorrhoea using nucleic acid amplification tests in men who have sex with men." Sexually Transmitted Infections 94, no. 4 (November 13, 2017): 287–92. http://dx.doi.org/10.1136/sextrans-2017-053339.

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ObjectivesThe objective of this study was to examine the associations between clinicians’ self-reported sampling technique and the detection rate of gonorrhoea at the oropharynx and anorectum using a highly sensitive nucleic acid amplification test (NAAT).MethodsWe analysed oropharyngeal and anorectal gonorrhoea swab results among men who have sex with men attending the Melbourne Sexual Health Centre (MSHC) between March 2015 and December 2016. Swabs were tested by NAAT using the Aptima Combo 2 transcription-mediated amplification assay due to its high sensitivity. Clinicians at MSHC were invited to complete a questionnaire on sampling techniques in November 2016. Univariable generalised estimating equations (GEE) logistic regressions were performed to determine the association between gonorrhoea detection rates and clinicians’ sampling technique. Patients’ epidemiological risk factors were included in the multivariable GEE logistic model.ResultsA total of 2605 oropharyngeal gonorrhoea and 2392 anorectal gonorrhoea swab results were analysed. There was no significant difference in the detection rates of gonorrhoea between the 23 clinicians at the oropharynx (range 3.6%–16.9%, median 8.2%, P=0.302) or and anorectum (range 2.4%–17.3%, median 10.5%, P=0.177). Variations in clinicians’ self-reported sampling technique were not associated with oropharyngeal or anorectal gonorrhoea detection rates after adjusting for patients’ epidemiological risk factors.ConclusionsThis study shows that differences in clinicians’ self-reported sampling technique did not result in measurable differences in the detection rate for oropharyngeal or anorectal gonorrhoea when using NAAT.

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da Silva, José Luiz P., and Enrico A. Colosimo. "Comments on ‘Covariance estimators for generalized estimating equations (GEE) in longitudinal analysis with small samples’." Statistics in Medicine 35, no. 28 (November 3, 2016): 5315–17. http://dx.doi.org/10.1002/sim.7053.

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Webers, Casper, Sofia Ramiro, Robert Landewé, Désirée van der Heijde, Filip van den Bosch, Maxime Dougados, Astrid van Tubergen, and Annelies Boonen. "Sick leave and its predictors in ankylosing spondylitis: long-term results from the Outcome in Ankylosing Spondylitis International Study." RMD Open 4, no. 2 (November 2018): e000766. http://dx.doi.org/10.1136/rmdopen-2018-000766.

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ObjectiveTo investigate the occurrence of ankylosing spondylitis (AS)-related sick leave (SL) over 6 12 years and explore factors predicting first and recurrent SL.MethodsData from employed patients from the Outcome in Ankylosing Spondylitis International Study were used. At each visit, patients indicated the occurrence of SL (yes/no) in the previous inter-assessment period. Cox regressions predicted a first episode of SL. Generalised estimating equations (GEE) explored the association between SL and (time-lagged) predictors. To investigate whether SL predicts new SL, SL in the first year was included as covariate in a separate analysis.Results139 patients (76% males, mean (SD) age 38.7 (10.0) years) were at risk for SL for an average period of 7.9 years, of whom 88 (63%) reported any SL. In both the Cox baseline predictors model (HR (95% CI)) and the time-varying GEE models (OR (95% CI)), AS Disease Activity Score (1.67, 1.23 to 2.28 (HR); 1.48, 1.07 to 2.03 (OR)); Bath AS Disease Activity Index (1.33, 1.18 to 1.51 (HR); 1.31, 1.15 to 1.49 (OR)), Bath AS Functional Index (1.17, 1.02 to 1.34 (HR); 1.31, 1.16 to 1.47 (OR)) and comorbidity at baseline (GEE only, 1.52, 1.00 to 2.29 (OR)) were associated with SL in separate models, but only in patients with low educational attainment. SL in the first year was an independent predictor of SL over time (OR: 2.62 to 8.37 in different models, all p<0.05).ConclusionDisease activity and physical function predicted first and recurrent SL, but only in patients with low educational attainment. Prior SL results in future SL, and SL should therefore be a signal for support to prevent future adverse work outcome.

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Iachina, Maria, Bent Jørgensen, Kaare Christensen, and Ivan Iachine. "Analysis of Functional Abilities for Elderly Danish Twins Using GEE Models." Twin Research 5, no. 4 (August 1, 2002): 289–93. http://dx.doi.org/10.1375/twin.5.4.289.

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AbstractIn this work we present a new method for genetic analysis of twin data which is based on generalized estimating equations and allows for analysis of various response types (e.g., continuous, binary, counts) combined with estimation of residual correlations. The new approach allows for control of covariates of any kind (e.g., continuous, counts) by modeling the dependence of mean and variance on background variables. The proposed method was applied to identify the covariates that have a significant influence on elderly people’s functional abilities, and find the estimates for the correlation coefficients of residuals for MZ and DZ twins in a sample of 2401 Danish twin 75 years of age or older. The bootstrap method was used to obtain standard errors for correlation coefficients. It was shown, that the chosen covariates have similar effects on MZ and DZ twins, and that the residual correlation in MZ twins is significantly higher than in DZ twins, which indicates that genetic factors play an etiological role in the determination of physical status of elderly people, controlled for 10 background variables.

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Giuffrè, O., A. Granà, T. Giuffrè, R. Marino, and S. Marino. "Estimating the Safety Performance Function for Urban Unsignalized Four-Legged One-Way Intersections in Palermo, Italy." Archives of Civil Engineering 60, no. 1 (March 1, 2014): 41–54. http://dx.doi.org/10.2478/ace-2014-0002.

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Abstract Starting from consideration that urban intersections are sites with promise for safety and operational improvements, the paper describes the steps taken to develop a crash predictive model for estimating the safety performance of urban unsignalized intersections located in Palermo, Italy. The focus is on unsignalized four-legged one-way intersections widespread in Italian downtowns. The sample considered in the study consist of 92 intersections in Palermo, Italy. For the study were collected crashes occurred in the sites during the years 2006-2012, geometric design and functional characteristics and traffic flow. Results showed that data were overdispersed and NB1 distributed. In order to account for the correlation within responses Generalized Estimating Equations (GEE) were used under different working correlation matrices.

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Morina, Fisnik, Ajshe Komoni, Duresa Kilaj, Durim Selmonaj, and Simon Grima. "The Effect of Health Expenditure on Life Expectancy." International Journal of Sustainable Development and Planning 17, no. 5 (August 31, 2022): 1389–401. http://dx.doi.org/10.18280/ijsdp.170502.

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With this paper, we aim to analyse the effect of health expenditures and funding on the national life expectancy of OECD countries. We considered the influence of exogenous factors such as health expenditure, GDP per capita and productivity, population, infant mortality rates, potential years of life lost, deaths from cancer and the suicide rate. We used secondary data gathered between 2005 to 2018 from the annual reports of the OECD, the IMF and the World Bank. To derive the empirical results, econometric models such as linear regression, random effect, fixed effect, Hausman - Taylor Regression, GMM Model - Arellano Bond Estimation, Generalized Estimating Equations (GEE Model) and linear trend analysis through the historical and comparative method were used. Results show that health expenditures positively affect the national life expectancy of OECD countries, showing the impact and causality of national longevity in OECD countries.

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Barcenilla-Wong, A. L., J. S. Chen, M. J. Cross, and L. M. March. "The Impact of Fracture Incidence on Health Related Quality of Life among Community-Based Postmenopausal Women." Journal of Osteoporosis 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/717914.

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This prospective study aimed to examine the impact of fracture incidence on health-related quality of life (HRQOL) among postmenopausal women. Study subjects were Australian female community-dwellers in the Global Longitudinal Study of Osteoporosis in Women (GLOW). Self-administered questionnaires were collected annually from 2007 to 2010. Outcomes were the Medical Outcomes Study Short Form-36 (SF-36 physical function (SF36PFS) and vitality (SF36VS) scores), European Quality of Life (EQ-5D), and self-reported general health (GH) of excellent/good. Questionnaires were divided into prior to, the 1st, the 2nd, and the 3rd year after incident fracture assessments. Generalized linear models with generalised estimating equations (GEE) were employed for the analysis. The 2,872 participants (age: median 65; interquartile range 60–73 years) provided a total of 10,436 assessments including 266, 165 and 76 assessments for the 1st, the 2nd, and the 3rd year after incident fracture, respectively. Multivariate adjustments showed reductions in HRQOL measures peaking at the 1st year for SF36VS (coefficient −3.0; 95% CI: −5.1, −0.8) and EQ-5D (coefficient −0.03; 95% CI: −0.06, −0.00) and at the 2nd year for SF36PFS (coefficient −3.0; 95% CI: −5.6, −0.5) and GH (odds ratio 0.92; 95% CI: 0.70, 1.19). Fracture incidence reduced HRQOL including vitality and physical function among relatively young, healthy postmenopausal women and the reduction in European Quality of Life measure was clinically important.

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GLEASON, B. L., S. FOSTER, G. E. WILT, B. MILES, B. LEWIS, K. CAUTHEN, M. KING, et al. "Geospatial analysis of household spread of Ebola virus in a quarantined village – Sierra Leone, 2014." Epidemiology and Infection 145, no. 14 (August 22, 2017): 2921–29. http://dx.doi.org/10.1017/s0950268817001856.

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SUMMARYWe performed a spatial-temporal analysis to assess household risk factors for Ebola virus disease (Ebola) in a remote, severely-affected village. We defined a household as a family's shared living space and a case-household as a household with at least one resident who became a suspect, probable, or confirmed Ebola case from 1 August 2014 to 10 October 2014. We used Geographic Information System (GIS) software to calculate inter-household distances, performed space-time cluster analyses, and developed Generalized Estimating Equations (GEE). Village X consisted of 64 households; 42% of households became case-households over the observation period. Two significant space-time clusters occurred among households in the village; temporal effects outweighed spatial effects. GEE demonstrated that the odds of becoming a case-household increased by 4·0% for each additional person per household (P< 0·02) and 2·6% per day (P< 0·07). An increasing number of persons per household, and to a lesser extent, the passage of time after onset of the outbreak were risk factors for household Ebola acquisition, emphasizing the importance of prompt public health interventions that prioritize the most populated households. Using GIS with GEE can reveal complex spatial-temporal risk factors, which can inform prioritization of response activities in future outbreaks.

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Chonde, Meshe, Katharyn L. Flickinger, Matthew L. Sundermann, Allison C. Koller, David D. Salcido, Cameron Dezfulian, James J. Menegazzi, and Jonathan Elmer. "Intra-Arrest Administration of Cyclosporine and Methylprednisolone Does Not Reduce Postarrest Myocardial Dysfunction." BioMed Research International 2019 (June 11, 2019): 1–7. http://dx.doi.org/10.1155/2019/6539050.

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Objective. To determine whether the administration of intra-arrest cyclosporine (CCY) and methylprednisolone (MP) preserves left ventricular ejection fraction (LVEF) and cardiac output (CO) after return of spontaneous circulation (ROSC). Methods. Eleven, 25-30kg female swine were randomized to receive 10mg/kg CCY + 40mg MP or placebo, anesthetized and given a transthoracic shock to induce ventricular fibrillation. After 8 minutes, standard CPR was started. After two additional minutes, the experimental agent was administered. Animals with ROSC were supported for up to 12h with norepinephrine as needed. Echocardiography was performed at baseline, and 1, 2, 6 and 12h post-ROSC. Analysis was performed using generalized estimating equations (GEE) after downsampling continuously sampled data to 5 minute epochs. Results. Eight animals (64%) achieved ROSC after a median of 7 [IQR 5-13] min of CPR, 2 [ IQR 1-3] doses of epinephrine and 2 [IQR 1-5] defibrillation shocks. Animals receiving CCY+MP had higher post ROSC MAP (GEE coefficient -10.2, P = <0.01), but reduced cardiac output (GEE coefficient 0.8, P = <0.01) compared to placebo. There was no difference in LVEF or vasopressor use between arms. Conclusions. Intra-arrest cyclosporine and methylprednisolone decreased post-arrest cardiac output and increased mean arterial pressure without affecting left ventricular ejection fraction.

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Handschin, Jonnie, Brian Hitsman, Sonja Blazekovic, Anna Veluz-Wilkins, E. Paul Wileyto, Frank T. Leone, Rebecca L. Ashare, and Robert A. Schnoll. "Factors Associated with Adherence to Transdermal Nicotine Patches within a Smoking Cessation Effectiveness Trial." Journal of Smoking Cessation 13, no. 1 (March 9, 2017): 33–43. http://dx.doi.org/10.1017/jsc.2017.2.

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Introduction: Adherence to transdermal nicotine patches, one of the most popular and effective treatments for nicotine dependence, remains very low and is a strong predictor of cessation rates.Aims: This study examined individual factors related to adherence as well as differences over time between adherent (>85% of daily patch use) and non-adherent participants (<85% of daily patch use).Methods: We analysed data from 440 participants who received 8 weeks of 21 mg transdermal nicotine and four behavioural counselling sessions within an effectiveness trial that examined the effects of long-term treatment. Multiple logistical regression assessed baseline variables associated with patch adherence and generalised estimating equations (GEE) were used to evaluate changes in craving and withdrawal, depressive and anxiety symptoms, substitute and complementary reinforcers, and side effects between participants who were or were not adherent.Results: Adherence to patch use was strongly associated with smoking cessation at week 8 (p < 0.05). In a logistic regression model, being female, living with a child or children, and higher self-reported anxiety symptoms were predictive of lower patch adherence (p < 0.05). In the GEE analysis, adherence was significantly associated with a greater reduction in craving, a greater engagement in substitute reinforcers, and a greater decrease in complementary reinforcers over time (p < 0.05).Conclusions: Difficulties adhering to transdermal nicotine patches may be related to psychiatric comorbidity, difficulty managing nicotine craving, and challenges with engaging in substitute reinforcers and reducing exposure to complementary reinforcers. These constructs may serve as targets for interventions designed to increase treatment adherence.Trial registration: ClinicalTrials.gov Identifier: NCT01047527

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Hoffman, Ronalda, Helen Bibby, David Bennett, Emily Klineberg, Alexandra Rushworth, and Susan Towns. "Family functioning as a protective factor in treating adolescents with complex medico-psychosocial presentations." International Journal of Adolescent Medicine and Health 28, no. 4 (November 1, 2016): 437–44. http://dx.doi.org/10.1515/ijamh-2015-0037.

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Abstract Purpose: Studies of adolescents with somatic symptoms with no identified organic cause make a clear connection between the health and wellbeing of the adolescent and the functioning of the family. There has been little systematic examination of the role of family functioning in treatment. We aim to determine whether there was an association between family functioning and treatment outcome in this 12 month prospective study. Methods: Fifty adolescents aged between 12 and 17 years (M=14.4, SD=1.32) and their parents were recruited. All adolescents presented with one or more physical symptoms as their primary complaint. Adolescent functioning and family relationships were measured at recruitment, 4 months into treatment and 12 months later. Generalised estimating equations (GEE) analyses were conducted to examine the relationships between the variables of interest. Results: Results suggested significant improvements in adolescent outcomes between baseline and 4 months (all p<0.01), which were sustained to 12 months (all p<0.01). Adolescents from healthier families at initial presentation had significantly improved psychosocial functioning over the course of the study (all p<0.001). Conclusion: Our findings highlight the importance of taking a family approach to the treatment of adolescents with medically unexplained symptoms.

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Kraenau Espinal, Erwin, and Ofelia Roque Paredes. "Modelos de ecuaciones de estimación generalizadas para el estudio de la evolución de pacientes con infarto agudo de miocardio." Paideia 5, no. 6 (September 15, 2017): 126–34. http://dx.doi.org/10.31381/paideia.v5i6.902.

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Objetivo: Utilizar los modelos GEE (Generalized Estimating Equations) para explicar los indicadores de evolución de los pacientes con IAM (Infarto Agudo de Miocardio). Materiales y Métodos: La información se obtuvo de los pacientes delHospital Dos de Mayo que estaban activos con IAM durante el año 2012. Resultados: Los mejores predictores para explicar el comportamiento del segmento STson el sexo con un OR=0.367 y otro diagnóstico adicional al IAM con OR=2.272,cuando la estructura de la correlación es independiente y OR=2.359 si la estructura de la correlación es AR(1). El predictor sexo resultó ser el único significativo para explicar la evolución de la onda T cuando la estructura de la correlación esintercambiable con un OR=9.89.

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Hoque, Md Erfanul, Mahfuzur Rahman Khokan, and Wasimul Bari. "On Selecting Relevant Covariates and Correlation Structure in Longitudinal Binary Model: Analyzing Impact of Height on Type II Diabetes." Austrian Journal of Statistics 44, no. 3 (September 14, 2015): 3–15. http://dx.doi.org/10.17713/ajs.v44i3.17.

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To examine the impact of height on the occurrence of Type II diabetes, a longitudinal binary data set has been analyzed. The relevant covariates were selected by using quasi-likelihood based information criteria (QIC) and correlation information criteria (CIC) was used to select the correlation structure appropriate for the repeated binary responses. The consistent and efficient estimates of regression parameters were obtained from the generalized estimating equations (GEE). With the selected covariates height, education level, gender and unstructured correlation structure, it is found that there exists a statistically significant inverse relationship between height of an individual and the development of Type II diabetes. Risk Ratios for different covariates along with standard errors and confidence intervals are also given.

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Gallis, John A., Fan Li, and Elizabeth L. Turner. "xtgeebcv: A command for bias-corrected sandwich variance estimation for GEE analyses of cluster randomized trials." Stata Journal: Promoting communications on statistics and Stata 20, no. 2 (June 2020): 363–81. http://dx.doi.org/10.1177/1536867x20931001.

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Cluster randomized trials, where clusters (for example, schools or clinics) are randomized to comparison arms but measurements are taken on individuals, are commonly used to evaluate interventions in public health, education, and the social sciences. Analysis is often conducted on individual-level outcomes, and such analysis methods must consider that outcomes for members of the same cluster tend to be more similar than outcomes for members of other clusters. A popular individual-level analysis technique is generalized estimating equations (GEE). However, it is common to randomize a small number of clusters (for example, 30 or fewer), and in this case, the GEE standard errors obtained from the sandwich variance estimator will be biased, leading to inflated type I errors. Some bias-corrected standard errors have been proposed and studied to account for this finite-sample bias, but none has yet been implemented in Stata. In this article, we describe several popular bias corrections to the robust sandwich variance. We then introduce our newly created command, xtgeebcv, which will allow Stata users to easily apply finite-sample corrections to standard errors obtained from GEE models. We then provide examples to demonstrate the use of xtgeebcv. Finally, we discuss suggestions about which finite-sample corrections to use in which situations and consider areas of future research that may improve xtgeebcv.

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Romero-Ortuno, Roman, Siobhan Scarlett, and Rose Anne Kenny. "17 Is Phenotypical Prefrailty all the Same? A Longitudinal Investigation of Two Prefrailty Subtypes in a Population-Based Longitudinal Study of Ageing." Age and Ageing 48, Supplement_3 (September 2019): iii1—iii16. http://dx.doi.org/10.1093/ageing/afz102.03.

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Abstract Background Fried’s frailty phenotype is defined by five criteria: exhaustion, unexplained weight loss, weakness, slowness and low physical activity. Pre-frailty (PF) meets one or two criteria. PF is of interest as a target for preventative strategies, but it is not known if it is a homogenous syndrome. The objective of this study was to compare the longitudinal trajectories of two PF groups: one defined by exhaustion and/or unexplained weight loss (PF1) and one defined by one or two of the following: weakness, slowness, low physical activity (PF2). Methods: Design and setting population-based longitudinal study of ageing. Subjects 1,660 PF participants aged ≥50 years from wave 1 of the study (2010), followed 2-yearly over 4 longitudinal waves (2012, 2014, 2016, 2018). Methods Generalised Estimating Equations (GEE) were used to assess the effect of PF type across waves to predict cumulative mortality and disability in basic (ADL) and independent (IADL) activities of daily living, adjusting for baseline characteristics (age, sex, education, living alone, self-rated health, comorbidity, body mass index). Results In wave 1, there were 687 PF1 and 973 PF2 participants. By wave 5, 64 (9.3%) PF1 and 145 (14.9%) PF2 participants had died. In PF1 participants, mean numbers of ADL and IADL disabilities both increased from 0.2 to 0.3 from wave 1 to wave 5, whilst in PF2 increases were from 0.2 to 0.5. Adjusted GEE models suggested significantly divergent trajectories of IADL disability by wave 2, ADL disability by wave 3, and mortality by wave 4. Conclusion Prefrailty may not be a homogenous biological syndrome. This may have relevance for the design of interventions to delay or reverse frailty in populations.

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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 (March 8, 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, must be used with either GLMMs or GEEs when developing RSFs. There are several important differences between these approaches; in particular, GLMMs are best for producing parameter estimates that predict how management might influence individuals, while GEEs are best for predicting how management might influence populations. As the interpretation, value, and statistical significance of both types of parameter estimates differ, it is important that users select the appropriate analytical method. We also outline the use of k-fold cross validation to assess fit of these models. Both GLMMs and GEEs hold promise for developing RSFs as long as they are used appropriately.

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Hasan, Md Enamul, Asma Ahmed, and Rowshonara Akter Akhi. "The Exogenous Shock of COVID-19: An Evidence from Financial Sector of an Emerging Economy." Business and Management Horizons 9, no. 2 (November 30, 2021): 27. http://dx.doi.org/10.5296/bmh.v9i2.19288.

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This paper analyses the effect of COVID-19 on the financial sector of Bangladesh. Particularly, it explores how this pandemic has affected this industry, considering firms’ past (pre-pandemic) financial characteristics. Employing the Generalised Estimation Equations (GEE) method with 1050 firm-year observations, which includes listed Banks, Financial Institutions, and Insurance companies’ data obtained from annual reports, datastream, and WHO, we found that firms with the larger size, more leverage, liquidity, and higher ROA is more resilient to stock return declines reacting to this pandemic. This study should be of interest to investors and regulators as it provides new evidence related to an industry’s pandemic and stock market response based on their prior financial characteristics. Besides, it will contribute to the extant literature of COVID-19 and the firm’s stock return from an emerging economy perspective.

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Gallis, John A., Xueqi Wang, Paul J. Rathouz, John S. Preisser, Fan Li, and Elizabeth L. Turner. "power swgee: GEE-based power calculations in stepped wedge cluster randomized trials." Stata Journal: Promoting communications on statistics and Stata 22, no. 4 (December 2022): 811–41. http://dx.doi.org/10.1177/1536867x221140953.

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Stepped wedge cluster randomized trials (SW-CRTs) are increasingly being used to evaluate interventions in medical, public health, educational, and social science contexts. With the longitudinal and crossover natures of an SW-CRT, complex analysis techniques are often needed, which makes appropriately powering SW-CRTs challenging. In this article, we introduce a newly developed SW-CRT power calculator embedded within the power command in Stata. The power calculator assumes a marginal model (that is, generalized estimating equations) for the primary analysis of SW-CRTs, for which other currently available SW-CRT power calculators may not be suitable. The command accommodates complete cross-sectional and closed-cohort designs and includes multilevel correlation structures appropriate for such designs. We discuss the methods and formulas underlying our SW-CRT calculator and provide illustrative examples of the use of power swgee. We provide suggestions about the choice of parameters in power swgee and conclude by discussing areas of future research that may improve the command.

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Bergström, Gunnar, Klas Gustafsson, Emmanuel Aboagye, Staffan Marklund, Gunnar Aronsson, Christina Björklund, and Constanze Leineweber. "A Resourceful Work Environment Moderates the Relationship between Presenteeism and Health. A Study Using Repeated Measures in the Swedish Working Population." International Journal of Environmental Research and Public Health 17, no. 13 (June 30, 2020): 4711. http://dx.doi.org/10.3390/ijerph17134711.

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The objective of this study was to investigate if the psychosocial work environment moderates the proposed negative impact of presenteeism on future general health. We expect that the negative impact of presenteeism on general health is weaker if the psychosocial work environment is resourceful, and more pronounced if the environment is stressful. Data were derived from the 2008–2018 biennial waves of the Swedish Longitudinal Occupational Survey of Health (SLOSH). The final analytic sample consisted of n = 15,779 individuals. We applied repeated measures regression analyses through generalized estimating equations (GEE). Results from the autoregressive GEE models showed statistically significant interaction terms between presenteeism and all four investigated moderators, i.e., job demands, job control, job support and job strain. The results indicate that the psychosocial work environment moderates the negative association between presenteeism and general health and illustrates a buffering effect of the psychosocial work environment. A possible explanation for these results may be that psychosocially resourceful work environments give room for adjustments in the work situation and facilitate recovery. The results also indicate that by investing the psychosocial work environment employers may be able to promote worker health as well as prevent reduced job performance due to presenteeism.

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Journal articles on the topic 'Generalised estimating equations (GEE)'

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