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1
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 (June 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 (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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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 (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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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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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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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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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 (July 11, 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.
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Cui, James. "QIC Program and Model Selection in GEE Analyses." Stata Journal: Promoting communications on statistics and Stata 7, no. 2 (June 2007): 209–20. http://dx.doi.org/10.1177/1536867x0700700205.
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The generalized estimating equation (GEE) approach is a widely used statistical method in the analysis of longitudinal data in clinical and epidemiological studies. It is an extension of the generalized linear model (GLM) method to correlated data such that valid standard errors of the parameter estimates can be drawn. Unlike the GLM method, which is based on the maximum likelihood theory for independent observations, the gee method is based on the quasilikelihood theory and no assumption is made about the distribution of response observations. Therefore, Akaike's information criterion, a widely used method for model selection in glm, is not applicable to gee directly. However, Pan (Biometrics 2001; 57: 120–125) proposed a model-selection method for gee and termed it quasilikelihood under the independence model criterion. This criterion can also be used to select the best-working correlation structure. From Pan's methods, I developed a general Stata program, qic, that accommodates all the distribution and link functions and correlation structures available in Stata version 9. In this paper, I introduce this program and demonstrate how to use it to select the best working correlation structure and the best subset of covariates through two examples in longitudinal studies.
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Ye, Peng, Wan Tang, Jiang He, and Hua He. "A GEE-type approach to untangle structural and random zeros in predictors." Statistical Methods in Medical Research 28, no. 12 (November 26, 2018): 3683–96. http://dx.doi.org/10.1177/0962280218812228.
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Count outcomes with excessive zeros are common in behavioral and social studies, and zero-inflated count models such as zero-inflated Poisson (ZIP) and zero-inflated Negative Binomial (ZINB) can be applied when such zero-inflated count data are used as response variable. However, when the zero-inflated count data are used as predictors, ignoring the difference of structural and random zeros can result in biased estimates. In this paper, a generalized estimating equation (GEE)-type mixture model is proposed to jointly model the response of interest and the zero-inflated count predictors. Simulation studies show that the proposed method performs well for practical settings and is more robust for model misspecification than the likelihood-based approach. A case study is also provided for illustration.
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N. Price, Gregory, and Juliet U. Elu. "Does regional currency integration ameliorate global macroeconomic shocks in sub-Saharan Africa? The case of the 2008-2009 global financial crisis." Journal of Economic Studies 41, no. 5 (September 2, 2014): 737–50. http://dx.doi.org/10.1108/jes-08-2011-0092.
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Purpose – The purpose of this paper is to consider whether regional currency integration in sub-Saharan Africa ameliorates global macroeconomic shocks by considering the impact of the 2008-2009 global financial crisis on economic growth. This suggests that Central Africa Franc Zone (CFAZ) eurocurrency union membership amplifies the effects of global business cycles in sub-Saharan Africa. Design/methodology/approach – The authors estimate the parameters of a quantity theory model of economic growth within a Generalized Estimating Equation (GEE) Framework. Findings – Parameter estimates from GEE specifications reveal that the contraction in credit during the financial crisis of 2008-2009 had larger adverse growth effects on sub-Saharan African countries who were members of the CFAZ eurocurrency union. The authors also find that sub-Saharan African countries who were members of the CFAZ eurocurrency union were more likely to experience a contraction in credit. Originality/value – As far as the authors can discern, no existing empirical growth models use a GEE framework to estimate parameters of interest. The GEE parameter estimates are distribution-free, robust with respect to unknown forms of heteroskedasticity, and control for a wide variety of error structures that can induce bias in panel data parameter estimates.
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Zhu, Hong, Song Zhang, and Chul Ahn. "Sample size considerations for split-mouth design." Statistical Methods in Medical Research 26, no. 6 (August 24, 2015): 2543–51. http://dx.doi.org/10.1177/0962280215601137.
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Split-mouth designs are frequently used in dental clinical research, where a mouth is divided into two or more experimental segments that are randomly assigned to different treatments. It has the distinct advantage of removing a lot of inter-subject variability from the estimated treatment effect. Methods of statistical analyses for split-mouth design have been well developed. However, little work is available on sample size consideration at the design phase of a split-mouth trial, although many researchers pointed out that the split-mouth design can only be more efficient than a parallel-group design when within-subject correlation coefficient is substantial. In this paper, we propose to use the generalized estimating equation (GEE) approach to assess treatment effect in split-mouth trials, accounting for correlations among observations. Closed-form sample size formulas are introduced for the split-mouth design with continuous and binary outcomes, assuming exchangeable and “nested exchangeable” correlation structures for outcomes from the same subject. The statistical inference is based on the large sample approximation under the GEE approach. Simulation studies are conducted to investigate the finite-sample performance of the GEE sample size formulas. A dental clinical trial example is presented for illustration.
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Oliver, Melody, Philip John Schluter, and Grant Schofield. "A New Approach for the Analysis of Accelerometer Data Measured on Preschool Children." Journal of Physical Activity and Health 8, no. 2 (February 2011): 296–304. http://dx.doi.org/10.1123/jpah.8.2.296.
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Background:Accelerometers are widely used to quantify physical activity (PA) in preschoolers, yet no ‘best practice’ method for data treatment exists. The purpose of this study was to develop a robust method for data reduction using contemporary statistical methods and apply it to preschoolers’ accelerometer data.Methods:Children age 2 to 5 years were recruited in Auckland, New Zealand, and asked to wear accelerometers over 7 days. Average daily PA rates per second were derived for participants, estimated using negative binomial generalized estimating equation (GEE) models. Overall participant rates were derived and compared using normal GEE models. Descriptive information for data analyzed were compared with that derived using traditional data inclusion approaches.Results:Data were gathered from 78 of the 93 enrolled children over a median of 7 days. Daily PA rates ranged from 1.27 to 17.64 counts per second (median 5.70). Compared with traditional approaches, this method had many advantages, including improved data retention, the computation of a continuous measure, and facilitating powerful multivariable regression analyses, while providing similar descriptive information to existing methods.Conclusion:PA rates were successfully calculated for preschoolers’ activity description and advantages of the approach identified. This method holds promise for future use and merits further application and enhancement.
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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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Tian, Suyan, Chi Wang, and Mayte Suarez-Farinas. "GEE-TGDR: A Longitudinal Feature Selection Algorithm and Its Application to lncRNA Expression Profiles for Psoriasis Patients Treated with Immune Therapies." BioMed Research International 2021 (April 9, 2021): 1–9. http://dx.doi.org/10.1155/2021/8862895.
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With the fast evolution of high-throughput technology, longitudinal gene expression experiments have become affordable and increasingly common in biomedical fields. Generalized estimating equation (GEE) approach is a widely used statistical method for the analysis of longitudinal data. Feature selection is imperative in longitudinal omics data analysis. Among a variety of existing feature selection methods, an embedded method—threshold gradient descent regularization (TGDR)—stands out due to its excellent characteristics. An alignment of GEE with TGDR is a promising area for the purpose of identifying relevant markers that can explain the dynamic changes of outcomes across time. We proposed a new novel feature selection algorithm for longitudinal outcomes—GEE-TGDR. In the GEE-TGDR method, the corresponding quasilikelihood function of a GEE model is the objective function to be optimized, and the optimization and feature selection are accomplished by the TGDR method. Long noncoding RNAs (lncRNAs) are posttranscriptional and epigenetic regulators and have lower expression levels and are more tissue-specific compared with protein-coding genes. So far, the implication of lncRNAs in psoriasis remains largely unexplored and poorly understood even though some evidence in the literature supports that lncRNAs and psoriasis are highly associated. In this study, we applied the GEE-TGDR method to a lncRNA expression dataset that examined the response of psoriasis patients to immune treatments. As a result, a list including 10 relevant lncRNAs was identified with a predictive accuracy of 70% that is superior to the accuracies achieved by two competitive methods and meaningful biological interpretation. A widespread application of the GEE-TGDR method in omics longitudinal data analysis is anticipated.
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Begum, Musammet Rasheda, and Soma Chowdhury Biswas. "Changes in Depressive Symptoms During the Antenatal Period: A Cohort Study from Bangladesh." Indian Journal of Psychological Medicine 42, no. 6 (October 20, 2020): 519–24. http://dx.doi.org/10.1177/0253717620954337.
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Background: Antenatal depressive symptoms adversely affect the health and well-being of women, babies, and their families. This study aimed to explore the prevalence and probable associated factors of depressivse symptoms. Methods: A prospective cohort approach was adopted, and 435 pregnant women were assessed longitudinally at an antenatal clinic in the first, second, and third trimesters. Edinburgh Postnatal Depression Scale (EPDS) was used to measure self-reported depressive symptoms. The multivariate approach of the general linear model was used to evaluate the mean differences of depression scores among the trimesters. To investigate the associated factors of depressive symptoms, generalized estimating equation (GEE) was used to take into account the clustering effect. Results: More than half (58.13%) of the pregnant women had antenatal depressive symptoms in at least one trimester. Depressive symptoms decreased throughout the pregnancy; they were higher in the first trimester and declined toward the third trimester. Significant mean differences were found in depression scores among the three trimesters. GEE showed that trimester, education, family income, and body mass index (BMI) are significantly associated with antenatal depressive symptoms. Conclusions: First trimester is the most critical period for identification of depressive symptoms. At-risk women need to be treated. Proper education achievement, economic solvency, and adequate nutrition could reduce the depressive symptoms.
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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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Bartlett, Jonathan D., Fergus O’Connor, Nathan Pitchford, Lorena Torres-Ronda, and Samuel J. Robertson. "Relationships Between Internal and External Training Load in Team-Sport Athletes: Evidence for an Individualized Approach." International Journal of Sports Physiology and Performance 12, no. 2 (February 2017): 230–34. http://dx.doi.org/10.1123/ijspp.2015-0791.
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Purpose:The aim of this study was to quantify and predict relationships between rating of perceived exertion (RPE) and GPS training-load (TL) variables in professional Australian football (AF) players using group and individualized modeling approaches.Methods:TL data (GPS and RPE) for 41 professional AF players were obtained over a period of 27 wk. A total of 2711 training observations were analyzed with a total of 66 ± 13 sessions/player (range 39–89). Separate generalized estimating equations (GEEs) and artificial-neural-network analyses (ANNs) were conducted to determine the ability to predict RPE from TL variables (ie, session distance, high-speed running [HSR], HSR %, m/min) on a group and individual basis.Results:Prediction error for the individualized ANN (root-mean-square error [RMSE] 1.24 ± 0.41) was lower than the group ANN (RMSE 1.42 ± 0.44), individualized GEE (RMSE 1.58 ± 0.41), and group GEE (RMSE 1.85 ± 0.49). Both the GEE and ANN models determined session distance as the most important predictor of RPE. Furthermore, importance plots generated from the ANN revealed session distance as most predictive of RPE in 36 of the 41 players, whereas HSR was predictive of RPE in just 3 players and m/min was predictive of RPE in just 2 players.Conclusions:This study demonstrates that machine learning approaches may outperform more traditional methodologies with respect to predicting athlete responses to TL. These approaches enable further individualization of load monitoring, leading to more accurate training prescription and evaluation.
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Serdarević, Nino, Ajla Muratović-Dedić, and Inela Karić. "Doing Business with the State and Firms’ Growth. Grasping Invisible Relational Capital." Scientific Annals of Economics and Business 63, no. 3 (November 1, 2016): 415–28. http://dx.doi.org/10.1515/saeb-2016-0131.
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Abstract This study investigates relationships between reported assets growth, human capital effectiveness, ability to do business with state and firms' growth. Longitudinal data were extracted from annual financial reports. Sample includes 80 companies in construction industry of Bosnia and Herzegovina from 2008-2013. Generalized estimating equations (GEE) approach is used for investigation of previously mentioned associations. We found that working with the state in Bosnian construction sector is dominant factor for outstanding increase in net reported income, while the human capital efficiency is negatively associated to its change. These findings support the theory of markets with asymmetric information, suggesting that the relational and social capital of the firm in the imperfect markets, where the state is dominant customer, drives the growth and that precedes firm’s investments into development of intellectual capital.
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Morrow, Michaele, and Robert Ricketts. "State Conformity with Federal Tax Changes." Journal of the American Taxation Association 32, no. 2 (September 1, 2010): 27–51. http://dx.doi.org/10.2308/jata.2010.32.2.27.
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ABSTRACT: We examine the decisions by state legislatures to conform to or decouple from federal tax legislation. We use a generalized estimating equations (GEE) approach to estimate the parameters of a model of factors associated with the state legislative decisions to conform their own tax systems to accommodate 11 federal tax reductions implemented between 2002 and 2008. Our model predicts that state legislators consider political, budgetary, and ease of compliance factors when considering whether to adopt federal tax changes into their own states’ tax codes. Our results are generally consistent with expectations, identifying several factors that are associated with the probability that a state will incorporate revenue-reducing tax changes implemented by the federal government. Our results also suggest that although state legislators appear to value conformity with the tax initiatives of the federal government, given rising budgetary pressures, state conformity with such legislation is likely to decrease in coming years.
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Eisenacher, M., M. Riesbeck, W. Gaebel, W. Köpcke, and S. A. Seuchter. "Methods for Predictor Analysis of Repeated Measurements: Application to Psychiatric Data." Methods of Information in Medicine 43, no. 02 (2004): 184–91. http://dx.doi.org/10.1055/s-0038-1633857.
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Summary Objectives: In schizophrenia research, little attention yet has been directed on methods for analyzing data from studies with repeated measurements over time. Motivation for this research stems from a project within the German Research Network on Schizophrenia, in which an algorithm is developed to guide prodrome-based early intervention strategies in stable first episode patients. Methods: We present two different approaches for the analysis of correlated response data, the Generalized Estimating Equations (GEE) method and the Artificial Neural Network (ANN) approach. We illustrate the methods using the data of the A.N.I. study, which is one of the largest German multicenter treatment studies in regard to the long-term treatment of schizophrenia conducted between 1983 and 1989. Results: The results of statistical model selection prior to GEE analysis and various data presentation methods for ANNs are presented. The primary goal of our evaluation is to investigate if the defined prodromes are valid predictors for relapse. Additionally, it is shown that both methods are applicable on a realistic data set. Conclusions: It is concluded that both methods are suitable for predictor analysis especially since all variable time points of the patients are included instead of only selected, so that it can be assumed that results are not biased. With the GEE method a test of association for each predictor can be performed whereas with ANNs a general proposition can be made for pro-dromes depending on the type of data presentation. Using the A.N.I. data the prodrome ‘trouble sleeping’ seems to be the most informative predictor. Finally, the important differences of the two methods are discussed.
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Nieman, David, Arnoud Groen, Artyom Pugachev, and Gianmarco Vacca. "Detection of Functional Overreaching in Endurance Athletes Using Proteomics." Proteomes 6, no. 3 (September 1, 2018): 33. http://dx.doi.org/10.3390/proteomes6030033.
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No reliable biomarkers exist to identify athletes in various training states including functional overreaching (FOR), non-functional overreaching (NFOR), and overtraining syndrome (OTS). Participants (N = 10, age 38.3 ± 3.4 years) served as their own controls and in random, counterbalanced order either ran/cycled 2.5 h (70.0 ± 3.7% VO2max) three days in a row (FOR) or sat in the lab (rest) (separated by three weeks; 7:00–9:30 am, overnight fasted state). Participants provided fingerprick samples for dried blood spot samples (DBS) pre- and post-exercise/rest, and then during two recovery days. DBS proteins were measured with nanoLC-MS in data-independent acquisition (DIA) mode, and 593 proteins were identified and quantified. Proteins were considered for the FOR cluster if they were elevated during one of the two recovery days but not more than one of the exercise days (compared to rest). The generalized estimating equation (GEE) was used to identify proteins linked to FOR. A total of 13 proteins was linked to FOR and most were associated with the acute phase response and innate immune system activation. This study used a system-wide proteomics approach to define a targeted panel of blood proteins related to FOR that could form the basis of future NFOR- and OTS-based studies.
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Iwata, Yusuke, Toru Okamura, David Zurakowski, and Richard A. Jonas. "Using activated clotting time to estimate intraoperative aprotinin concentration." Perfusion 24, no. 6 (November 2009): 397–400. http://dx.doi.org/10.1177/0267659109358471.
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Background: The use of aprotinin during cardiopulmonary bypass may be associated with renal dysfunction due to renal excretion of excess drug. We hypothesized that the difference between standard celite activated clotting time (ACT), which is prolonged by aprotinin, and kaolin ACT could provide an estimate of aprotinin blood level. Methods: Fresh porcine blood was collected from six donor pigs and heparinized. Blood was stored at 4°C, rewarmed and aprotinin was added: 0, 100, 200, and 400 kallikrein inhibitor units/ml. Specimens were incubated at 37°C. Two pairs of ACT tubes (one celite and one kaolin) were measured at 37°C and 20°C using two Hemochron 401 machines. A generalized estimating equation (GEE) statistical approach was used to estimate actual aprotinin from differences in celite and kaolin ACT. Result: There was a significant relationship of the form y = exp(a+bx) between aprotinin concentration and the difference between celite and kaolin ACT at both 37°C (R2 = 0.858) and 20°C (R2 = 0.743). Conclusion: The time difference between celite and kaolin ACT may be a simple and inexpensive method for measuring the blood level of aprotinin during cardiopulmonary bypass. This technique may improve patient-specific dosing of aprotinin and reduce the risk of postoperative renal complications.
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Gasparinho, Carolina, Aguinaldo Kanjungo, Félix Zage, Isabel Clemente, Ana Santos-Reis, Miguel Brito, José Carlos Sousa-Figueiredo, Filomeno Fortes, and Luzia Gonçalves. "Impact of Annual Albendazole versus Four-Monthly Test-and-Treat Approach of Intestinal Parasites on Children Growth—A Longitudinal Four-Arm Randomized Parallel Trial during Two Years of a Community Follow-Up in Bengo, Angola." Pathogens 10, no. 3 (March 7, 2021): 309. http://dx.doi.org/10.3390/pathogens10030309.
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Malnutrition and intestinal parasites continue to have serious impacts on growth and cognitive development of children in Angola. A longitudinal four-arm randomized parallel trial was conducted to investigate if deworming with a single annual dose of albendazole (annual-ALB) or a four-monthly test-and-treat (4TT) intestinal parasites approach at individual or household levels improve nutritional outcomes of pre-school children in Bengo province. Children with intestinal parasites (n = 121) were randomly assigned (1:1:1:1) to arm A1: annual-ALB*individual level; A2: annual-ALB*household level; A3: 4TT*individual; and A4: 4TT*household level. At baseline, 4, 8, 12, 16, 20, and 24 months of follow-up, growth was assessed by height, weight, height-for-age, weight-for-height, weight-for-age, and mid-upper arm circumference. Intention-to-treat analysis was done using non-parametric approach, mixed effect models, and generalized estimating equations (GEE). Initially, 57% and 26% of the children were infected by Giardia lamblia and Ascaris lumbricoides, respectively. This study did not show that a 4TT intestinal parasites approach results on better growth outcomes of children (height, weight, HAZ, WAZ, WHZ and MUACZ) when compared with annual ALB, with exception of height and WHZ using GEE model at 5% level. Positive temporal effects on most nutrition outcomes were observed. Implementing a longitudinal study in a poor setting is challenging and larger sample sizes and ‘pure and clean’ data are difficult to obtain. Nevertheless, learned lessons from this intensive study may contribute to future scientific research and to tailor multidisciplinary approaches to minimize malnutrition and infections in resource-poor countries.
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Hassen, Hamid Yimam, Hilde Bastiaens, Kathleen Van Royen, and Steven Abrams. "Socioeconomic and behavioral determinants of cardiovascular diseases among older adults in Belgium and France: A longitudinal analysis from the SHARE study." PLOS ONE 15, no. 12 (December 4, 2020): e0243422. http://dx.doi.org/10.1371/journal.pone.0243422.
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Despite advances in the healthcare system, cardiovascular diseases (CVDs) are still an important public health problem with disparities in the burden within and between countries. Studies among the adult population documented that socioeconomic and environmental factors play a role in the incidence and progression of CVDs. However, evidence is scarce on the socioeconomic determinants and the interplay with behavioral risks among older adults. Therefore, we identified socioeconomic and behavioral determinants of CVDs among older adults. Our sample consisted of 14,322 people aged 50 years and above from Belgium and France who responded to the waves 4, 5, 6 and/or 7 of the Survey of Health Ageing and Retirement in Europe. The effect of determinants on the occurrence of CVD was examined using a Generalized Estimating Equation (GEE) approach for binary longitudinal data. The overall rate of heart attack was 8.3%, which is 7.6% in Belgium and 9.1% in France. Whereas, 2.6% and 2.3% in Belgium and France, respectively, had experienced stroke. In the multivariable GEE model, older age [AOR: 1.057, 95%CI: 1.055–1.060], living in large cities [AOR: 1.14, 95%CI: 1.07–1.18], and retirement [AOR: 1.21, 95%CI: 1.16–1.31] were associated with higher risk of CVD. Furthermore, higher level of education [AOR: 0.82, 95%CI: 0.79–0.90], upper wealth quantile [AOR: 0.82, 95%CI: 0.76–0.86] and having social support [AOR: 0.81, 95%CI: 0.77–0.84] significantly lowers the odds of having CVD. A higher hand grip strength was also significantly associated with lower risk of CVD [AOR: 0.987, 95%CI: 0.984–0.990]. This study demonstrated that older adults who do not have social support, live in big cities, belong to the lowest wealth quantile, and have a low level of education have a higher likelihood of CVD. Therefore, community-based interventions aimed at reducing cardiovascular risks need to give more emphasis to high-risk retired older adults with lower education, no social support and those who live in large cities.
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Tejeda Almonte, Jose Antonio, Andréia Hartwig, and Taianan Alves Uzeda Luna. "A habilidade do gestor CFO e sua relação com a eficiência das empresas listadas na bolsa de valores brasileira." Revista Mineira de Contabilidade 21, no. 3 (December 23, 2020): 11–19. http://dx.doi.org/10.51320/rmc.v21i3.1108.
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Este estudo analisou o efeito da capacidade gerencial do diretor financeiro (CFO) na qualidade dos resultados divulgados pelas firmas listadas na B3. Para obter a capacidade gerencial do CFO foi utilizada a abordagem de Demerjian et al. (2013), na qual se estima a eficiência total da empresa mediante um método de fronteira eficiente e o resíduo da equação é tomado para criação da medida. Foi utilizada a reapresentação dos resultados contábeis como proxy de qualidade de resultados da firma. Foi considerado como eficiência da firma a capacidade de agregar valor aos insumos adquiridos ou consumidos. A eficiência foi estimada através do método de Stochastic Production Frontier (SFA), mediante um fixed effects production frontier model. Para verificar se a parcela de eficiência da firma atribuível a capacidade do CFO e a sua equipe tem relação com a qualidade dos resultados foi estimado um modelo de regressão probit em painel mediante o generalized estimating equation (GEE) approach para descobrir o efeito médio na população da variável que representa a capacidade gerencial, Mhabil, em relação ao efeito específico individual. As evidências empíricas indicam que maior capacidade gerencial do CFO tem relação negativa com a probabilidade de reapresentação dos relatórios financeiros e, consequentemente, maior a qualidade dos resultados para as empresas. Os resultados indicam que a escolha do gestor CFO é importante para o mercado de capitais, demonstrando que CFOs mais capazes implicam em menores chances de reapresentação dos resultados financeiros. Para a análise foram utilizadas informações anuais das empresas brasileiras listadas na B3 no período de 2010 a 2017.
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Geoffroy, M. C., L. Li, and C. Power. "Depressive symptoms and body mass index: co-morbidity and direction of association in a British birth cohort followed over 50 years." Psychological Medicine 44, no. 12 (February 19, 2014): 2641–52. http://dx.doi.org/10.1017/s0033291714000142.
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BackgroundAn unhealthy body mass index (BMI) has been associated with depression but the direction of association is uncertain. Our aim was to estimate the co-morbidity and direction of association between BMI and depressive symptoms at several ages, from childhood to mid-adulthood.MethodThe data were from 18 558 individuals born in 1 week in March 1958, in England, Scotland and Wales, with follow-up at ages 7, 11, 16, 23, 33, 42, 45 and 50 years. Depression (scores ⩾90th percentile) was identified from child/adolescent (teacher questionnaires) and adult (self-complete questionnaires and clinical interview) measures. BMI (kg/m2) measured in child/adolescence and adulthood was classified as underweight, normal, overweight or obese.ResultsIn cross-sectional analyses, obesity and underweight (not overweight) from 11 to 45 years were associated respectively with 1.3–2.1 and 1.5–2.3 times the risk of depression compared with normal weight. Using the time-lagged generalized estimating equation (GEE) approach, we tested (a) whether underweight or obesity at prior ages (7 to 45 years) predicted subsequent risk of depression (11 to 50 years), adjusting for baseline depression; and (b) whether depression at prior ages (7 to 42 years) predicted subsequent risk of underweight or obesity (11 to 45 years), adjusting for baseline BMI. In longitudinal analyses, underweight predicted subsequent depression in both sexes [odds ratio (OR) 1.25, 95% confidence interval (CI) 1.11–1.40] and depression predicted subsequent underweight in males only (OR 1.84, 95% CI 1.52–2.23). Obesity predicted subsequent depressive symptoms in females only (OR 1.34, 95% CI 1.14–1.56), but depression did not predict obesity.ConclusionsClinicians should consider screening routinely for depression patients with unhealthy BMI, namely underweight and obesity, and vice versa.
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Navadeh, Soodabeh, Ali Mirzazadeh, Willi McFarland, Phillip Coffin, Mohammad Chehrazi, Kazem Mohammad, Maryam Nazemipour, Mohammad Ali Mansournia, Lawrence C. McCandless, and Kimberly Page. "Unsafe Injection Is Associated with Higher HIV Testing after Bayesian Adjustment for Unmeasured Confounding." Archives of Iranian Medicine 23, no. 12 (December 1, 2020): 848–55. http://dx.doi.org/10.34172/aim.2020.113.
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Background: To apply a novel method to adjust for HIV knowledge as an unmeasured confounder for the effect of unsafe injection on future HIV testing. Methods: The data were collected from 601 HIV-negative persons who inject drugs (PWID) from a cohort in San Francisco. The panel-data generalized estimating equations (GEE) technique was used to estimate the adjusted risk ratio (RR) for the effect of unsafe injection on not being tested (NBT) for HIV. Expert opinion quantified the bias parameters to adjust for insufficient knowledge about HIV transmission as an unmeasured confounder using Bayesian bias analysis. Results: Expert opinion estimated that 2.5%–40.0% of PWID with unsafe injection had insufficient HIV knowledge; whereas 1.0%–20.0% who practiced safe injection had insufficient knowledge. Experts also estimated the RR for the association between insufficient knowledge and NBT for HIV as 1.1-5.0. The RR estimate for the association between unsafe injection and NBT for HIV, adjusted for measured confounders, was 0.96 (95% confidence interval: 0.89,1.03). However, the RR estimate decreased to 0.82 (95% credible interval: 0.64, 0.99) after adjusting for insufficient knowledge as an unmeasured confounder. Conclusion: Our Bayesian approach that uses expert opinion to adjust for unmeasured confounders revealed that PWID who practice unsafe injection are more likely to be tested for HIV – an association that was not seen by conventional analysis.
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Merante, Serena, Cristiana Pascutto, Chiara Elena, Barbara Rocca, Rita Zappatore, Paola Maria Cavigliano, Mario Cazzola, and Ester Orlandi. "A Novel Approach to a Retrospective Longitudinal Analysis of Dose Change or Discontinuation of Imatinib Therapy in Chronic Phase–Chronic Myeloid Leukemia." Blood 124, no. 21 (December 6, 2014): 4543. http://dx.doi.org/10.1182/blood.v124.21.4543.4543.
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Abstract Imatinib (IM) is a cornerstone in the treatment of chronic myeloid leukemia (CML). Dose change or discontinuation of IM in patients who experience sustained molecular response is a subject of debate. We retrospectively studied 142 CML patients in chronic phase (Table 1) treated with IM and followed-up during 2000-2013 at our institution. Dose changes, discontinuation of therapy, cytogenetic and molecular analyses were regularly recorded during follow-up. Patients’ history was subdivided into 483 treatment time-periods at constant dosage. Response was evaluated at the end of each treatment period. We assessed whether the probability of observing a complete cytogenetic response (CCyR) or a molecular response (MR: complete, CMR or major, MMR) or a progression were influenced by treatment dose and/or duration. We applied generalized estimating equation (GEE) logistic models for the analysis of longitudinal panel data. These models are designed to account for individual patient variation due to repeated measurements during each patient’s follow-up. Out of 483 time periods at constant IM dose, 236 were followed by dose modification, 116 by IM discontinuation, 29 by a change to other treatments due to tolerability issues or non-response; 102 were still ongoing without dose changes. Treatment response: 74% of time periods resulted in a CCyR and 2.3% showed no response; 31.9% showed a CMR, 29.6% showed a MMR (MR3, MR4, MR4.5), 35.6% a suboptimal response, 2.9% no MR. CMR+MMR was observed in 61.5% time periods. Periods at standard dose showed a higher response rate, both when considering CCyR (response rate after low, standard, high dose: 69.3%, 79.6%, 68%, respectively, P=0.023) and when considering MR (CMR after low, standard, high dose: 27%, 40.5%, 15.4%, respectively, P<0.001; CMR + MMR after low, standard, high dose: 53.4%, 69.8%, 52.3%, respectively, P=0.001). After adjusting for length of treatment period in a multivariate GEE model, dose lost significance and treatment duration was the only significant predictor of CCyR (P<0.001). In a GEE analysis of MR accounting for treatment duration, reduction in CMR rate after periods at high dose compared with periods at standard dose remained significant (P=0.025). Treatment dose lost significance when considering CMR+MMR. In 32 patients who discontinued IM therapy for >1 mos and then re-started IM, the CR rate after a period preceded by a treatment suspension was significantly higher than in other periods (CCyR: 85.7% vs. 73%, P=0.058; CMR 55.1% vs. 29.3%, P=0.001). In a GEE model with dose category, duration of treatment and previous suspension as covariates, both duration and previous suspension maintained a positive significant association to CCyR (P<0.001, P=0.034, respectively), to CMR (both P<0.001) and to CMR+MMR (P<0.001, P=0.006, respectively). Cytogenetic progression rate at the end of a period of IM treatment was 7.1% while molecular progression rate was 12.8%. Treatment dose was not associated to progression: cytogenetic progression after low, standard, high dose: 6.1%, 6.7%, 10.5%, respectively, P=0.428; molecular progression after low, standard, high dose: 14.8%, 11.3%, 13%, respectively, P=0.584. In a multivariate GEE analysis, neither dose nor duration of treatment predicted progression. After adding type of previous treatment (IM, other drug, no treatment) as a covariate, treatment periods preceded by at least 1 month of discontinuation had a significantly lower molecular progression rate (P=0.011). The possibility of varying dosage is often considered in “real-life” patient clinical management. In our study, 62% of time periods were followed by a dose change, but this did not affect response and progression rate after accounting for length of treatment period. The prompt response to resumed IM therapy suggests that patients may be candidates for intermittent therapy. Future studies could use the same statistical model to pick up individual patient variation in longitudinal data collected over time, including trials at reduced IM dose or of “on-off” therapy or on new TKI. Table 1. Patients’ characteristic Variable Description N. of patients 142 Sex M/F 58%/42% Age at diagnosis (years), median (range) 52 (18-78) N. of IM treatment periods 483 IM dosage, N (%) Low dose (100-300 mg/day) 163 (34%) Standard dose (400 mg/day) 242 (50%) High dose (450-800 mg/day) 78 (16%) Duration of IM at constant dosage (days), median (range) 258 (7-4526) Disclosures No relevant conflicts of interest to declare.
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Nestadt, G., C. Di, J. F. Samuels, Y. J. Cheng, O. J. Bienvenu, I. M. Reti, P. Costa, W. W. Eaton, and K. Bandeen-Roche. "Concordance between personality disorder assessment methods." Psychological Medicine 42, no. 3 (August 24, 2011): 657–67. http://dx.doi.org/10.1017/s0033291711001632.
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BackgroundStudies have criticized the low level of agreement between the various methods of personality disorder (PD) assessment. This is an important issue for research and clinical purposes.MethodSeven hundred and forty-two participants in the Hopkins Epidemiology of Personality Disorders Study (HEPS) were assessed on two occasions using the Personality Disorder Schedule (PDS) and the International Personality Disorder Examination (IPDE). The concordance between the two diagnostic methods for all DSM-IV PDs was assessed using standard methods and also two item response analytic approaches designed to take account of measurement error: a latent trait-based approach and a generalized estimating equations (GEE)-based approach, with post-hoc adjustment.ResultsRaw criteria counts, using the intraclass correlation coefficient (ICC), κ and odds ratio (OR), showed poor concordance. The more refined statistical methods showed a moderate to moderately high level of concordance between the methods for most PDs studied. Overall, the PDS produced lower prevalences of traits but higher precision of measurement than the IPDE. Specific criteria within each PD showed varying endorsement thresholds and precision for ascertaining the disorder.ConclusionsConcordance in the raw measurement of the individual PD criteria between the two clinical methods is lacking. However, based on two statistical methods that adjust for differential endorsement thresholds and measurement error in the assessments, we deduce that the PD constructs themselves can be measured with a moderate degree of confidence regardless of the clinical approach used. This may suggest that the individual criteria for each PD are, in and of themselves, less specific for diagnosis, but as a group the criteria for each PD usefully identify specific PD constructs.
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Fernandez, Whitney Douglas, and Yannick Thams. "Board diversity and stakeholder management: the moderating impact of boards’ learning environment." Learning Organization 26, no. 2 (February 4, 2019): 160–75. http://dx.doi.org/10.1108/tlo-12-2017-0126.
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Purpose This paper aims to draw on insights from team learning theory and stakeholder theory to examine the influence of board composition on firms’ stakeholder management practices. The authors posit that board diversity is likely to enhance stakeholder management by shaping organizational goals by placing emphasis on the interests of a wide variety of stakeholders and providing firms with relevant knowledge to enhance their ability manage these interests. The authors further theorize on the moderating role of boards’ learning environment, which they conceptualized as an important complementary governance-related factor, likely to further boards’ ability to enhance stakeholder management. Design/methodology/approach The authors test these predictions using the generalized estimating equations (GEE) technique on a panel of S&P 500 firms observed from 2001 to 2011. Findings The findings provide evidence that more diverse boards in terms of gender, nationality and race/ethnicity are generally associated with more effective stakeholder management. Further, the findings also suggest that boards’ co-working experience moderates the relationship between gender and national diversity and stakeholder management. Originality/value While corporate governance research surveys many strategic implications of board composition, limited attention has been paid to the interplay of board characteristics with stakeholder management. This study is among the first to the authors’ knowledge to explore the impact of board diversity on stakeholder management using team learning research, thus drawing attention to the role of boards’ co-working experience in shaping their ability to impact firms’ outcomes.
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Mayo, Nancy E., Carolina Moriello, Susan C. Scott, Diana Dawes, Mohammad Auais, and Martin Chasen. "Pedometer-facilitated walking intervention shows promising effectiveness for reducing cancer fatigue: a pilot randomized trial." Clinical Rehabilitation 28, no. 12 (June 10, 2014): 1198–209. http://dx.doi.org/10.1177/0269215514536209.
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Objective: Mechanisms for cancer related fatigue suggest that exercise but “not too much and not too little” could be effective. This study aimed to investigate feasibility and estimate the potential effects of a walking exercise program in people with advanced cancer and fatigue. Design: A pilot randomized trial. Setting: McGill University Health Centre (MUHC), Montreal, Canada. Subjects: People with advanced cancer undergoing interdisciplinary assessment and rehabilitation with a fatigue level of 4 to 10 on a visual analogue scale. Interventions: An 8-week fatigue-adapted, walking intervention, facilitated using a pedometer (STEPS), and offered at the same time as or after rehabilitation. Measures: Measures of fatigue, physical function and well-being were administered at entry, and 8, 16 and 24 weeks. Generalized estimating equations (GEE) estimated the odds of response for people receiving the STEPS program in comparison to the odds of response in the controls (odds ratio, OR). Results: Twenty-six persons were randomized to three groups: during rehabilitation, after rehabilitation, and usual care. For the fatigue measures the OR for STEPS offered at any time using an intention-to-treat approach was 3.68 (95%CI: 1.05-12.88); for the physical function measures, the OR was 1.40 (95%CI: 0.41- 4.79) and 2.36 (95%CI: 0.66-8.51) for the well-being measures. Conclusion: Fifty percent of eligible people were able to participate. This small trial suggests that a personalized exercise program reduces fatigue and that 100 people are needed in a full strength trial.
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Hochstrasser, L., D. Fröhlich, A. R. Schneeberger, S. Borgwardt, U. E. Lang, R. D. Stieglitz, and C. G. Huber. "Long-term reduction of seclusion and forced medication on a hospital-wide level: Implementation of an open-door policy over 6 years." European Psychiatry 48, no. 1 (2018): 51–57. http://dx.doi.org/10.1016/j.eurpsy.2017.09.008.
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AbstractBackgroundPsychiatric inpatient treatment is increasingly performed in settings with locked doors. However, locked wards have well-known disadvantages and are ethically problematic. In addition, recent data challenges the hypothesis that locked wards provide improved safety over open-door settings regarding suicide, absconding and aggression. Furthermore, there is evidence that the introduction of an open-door policy may lead to short-term reductions in involuntary measures. The aim of this study was to assess if the introduction of an open-door policy is associated with a long-term reduction of the frequency of seclusion and forced medication.MethodIn this 6-year, hospital-wide, longitudinal, observational study, we examined the frequency of seclusion and forced medication in 17,359 inpatient cases admitted to the Department of Adult Psychiatry, Universitäre Psychiatrische Kliniken (UPK) Basel, University of Basel, Switzerland. In an approach to enable a less restrictive policy, six previously closed psychiatric wards were permanently opened beginning from August 2011. During this process, a systematic change towards a more patient-centered and recovery-oriented care was applied. Statistical analysis consisted of generalized estimating equations (GEE) models.ResultsIn multivariate analyses controlling for potential confounders, the implementation of an open-door policy was associated with a continuous reduction of seclusion (from 8.2 to 3.5%; ηp2 = 0.82; odds ratio: 0.88) and forced medication (from 2.4 to 1.2%; ηp2 = 0.70; odds ratio: 0.90).ConclusionThis underlines the potential of the introduction of an open-door policy to attain a long-term reduction in involuntary measures.
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Carl, Ellen, Alina Shevorykin, Amylynn Liskiewicz, Ronald Alberico, Ahmed Belal, Martin Mahoney, Elizabeth Bouchard, Andrew Ray, and Christine E. Sheffer. "Increasing Physical Activity among Breast Cancer Survivors by Modulating Temporal Orientation with rTMS: Feasibility and Potential Efficacy." International Journal of Environmental Research and Public Health 18, no. 19 (September 24, 2021): 10052. http://dx.doi.org/10.3390/ijerph181910052.
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Maintaining adequate amounts of physical activity is a critical component of survivorship care for women with breast cancer. Increased physical activity is associated with increases in well-being, quality of life, and longevity, but women with cancer face unique, cancer-related factors that might affect physical activity. Consistent with the Competing Neurobehavioral Decision Systems model of decision making, we proposed to decrease delay discounting and increase physical activity by stimulating the executive function system via high-frequency repetitive transcranial magnetic stimulation (HF rTMS) of the left dorsolateral prefrontal cortex (LDLPFC). This randomized, sham-controlled, double-blinded trial examined the feasibility and potential efficacy of this approach to increase physical activity in breast cancer survivors. We hypothesized that active rTMS would significantly increase the mean number of steps per day and decrease delay discounting. Participants (n = 30) were primarily middle-aged (M = 53.7, SD = 7.9) and white with a mean BMI and body mass indices below 40. Indicators of feasibility and limited efficacy testing were positive. Although repeated-measures ANOVA revealed no significant changes in delay discounting, generalized estimating equations (GEE) found that participants in the active condition increased their mean daily steps by 400 steps per day, while those in the sham condition decreased this by nearly 600 steps per day. These findings indicate that the continued investigation of HF rTMS for increasing physical activity among women with breast cancer is justified.
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Wedig, M. M., M. H. Silverman, F. R. Frankenburg, D. Bradford Reich, G. Fitzmaurice, and M. C. Zanarini. "Predictors of suicide attempts in patients with borderline personality disorder over 16 years of prospective follow-up." Psychological Medicine 42, no. 11 (March 22, 2012): 2395–404. http://dx.doi.org/10.1017/s0033291712000517.
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BackgroundIt is clinically important to understand the factors that increase the likelihood of the frequent and recurrent suicide attempts seen in those with borderline personality disorder (BPD). Although several studies have examined this subject in a cross-sectional manner, the aim of this study was to determine the most clinically relevant baseline and time-varying predictors of suicide attempts over 16 years of prospective follow-up among patients with BPD.MethodTwo-hundred and ninety in-patients meeting Revised Diagnostic Interview for Borderlines (DIB-R) and DSM-III-R criteria for BPD were assessed during their index admission using a series of semistructured interviews and self-report measures. These subjects were then reassessed using the same instruments every 2 years. The generalized estimating equations (GEE) approach was used to model the odds of suicide attempts in longitudinal analyses, controlling for assessment period, yielding an odds ratio (OR) and 95% confidence interval (CI) for each predictor.ResultsNineteen variables were found to be significant bivariate predictors of suicide attempts. Eight of these, seven of which were time-varying, remained significant in multivariate analyses: diagnosis of major depressive disorder (MDD), substance use disorder (SUD), post-traumatic stress disorder (PTSD), presence of self-harm, adult sexual assault, having a caretaker who has completed suicide, affective instability, and more severe dissociation.ConclusionsThe results of this study suggest that prediction of suicide attempts among borderline patients is complex, involving co-occurring disorders, co-occurring symptoms of BPD (self-harm, affective reactivity and dissociation), adult adversity, and a family history of completed suicide.
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Nisa, Khoirin, and Netti Herawati. "Robust Estimation of Generalized Estimating Equation when Data Contain Outliers." INSIST 2, no. 1 (March 22, 2017): 1. http://dx.doi.org/10.23960/ins.v2i1.23.
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Abstract—In this paper, a robust procedure for estimating parameters of regression model when generalized estimating equation (GEE) applied to longitudinal data that contains outliers is proposed. The method is called ‘iteratively reweighted least trimmed square’ (IRLTS) which is a combination of the iteratively reweighted least square (IRLS) and least trimmed square (LTS) methods. To assess the proposed method a simulation study was conducted and the result shows that the method is robust against outliers.Keywords—GEE, IRLS, LTS, longitudinal data, regression model.
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Tseng, Vincent WS, Jean Dos Reis Costa, Malte F. Jung, and Tanzeem Choudhury. "Using Smartphone Sensor Data to Assess Inhibitory Control in the Wild: Longitudinal Study." JMIR mHealth and uHealth 8, no. 12 (December 4, 2020): e21703. http://dx.doi.org/10.2196/21703.
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Background Inhibitory control, or inhibition, is one of the core executive functions of humans. It contributes to our attention, performance, and physical and mental well-being. Our inhibitory control is modulated by various factors and therefore fluctuates over time. Being able to continuously and unobtrusively assess our inhibitory control and understand the mediating factors may allow us to design intelligent systems that help manage our inhibitory control and ultimately our well-being. Objective The aim of this study is to investigate whether we can assess individuals’ inhibitory control using an unobtrusive and scalable approach to identify digital markers that are predictive of changes in inhibitory control. Methods We developed InhibiSense, an app that passively collects the following information: users’ behaviors based on their phone use and sensor data, the ground truths of their inhibition control measured with stop-signal tasks (SSTs) and ecological momentary assessments (EMAs), and heart rate information transmitted from a wearable heart rate monitor (Polar H10). We conducted a 4-week in-the-wild study, where participants were asked to install InhibiSense on their phone and wear a Polar H10. We used generalized estimating equation (GEE) and gradient boosting tree models fitted with features extracted from participants’ phone use and sensor data to predict their stop-signal reaction time (SSRT), an objective metric used to measure an individual’s inhibitory control, and identify the predictive digital markers. Results A total of 12 participants completed the study, and 2189 EMAs and SST responses were collected. The results from the GEE models suggest that the top digital markers positively associated with an individual’s SSRT include phone use burstiness (P=.005), the mean duration between 2 consecutive phone use sessions (P=.02), the change rate of battery level when the phone was not charged (P=.04), and the frequency of incoming calls (P=.03). The top digital markers negatively associated with SSRT include the standard deviation of acceleration (P<.001), the frequency of short phone use sessions (P<.001), the mean duration of incoming calls (P<.001), the mean decibel level of ambient noise (P=.007), and the percentage of time in which the phone was connected to the internet through a mobile network (P=.001). No significant correlation between the participants’ objective and subjective measurement of inhibitory control was found. Conclusions We identified phone-based digital markers that were predictive of changes in inhibitory control and how they were positively or negatively associated with a person’s inhibitory control. The results of this study corroborate the findings of previous studies, which suggest that inhibitory control can be assessed continuously and unobtrusively in the wild. We discussed some potential applications of the system and how technological interventions can be designed to help manage inhibitory control.
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Hoffman-Censits, J. H., Y. Wong, T. Li, S. Boorjian, V. N. Giri, R. Uzzo, R. E. Greenberg, G. Hudes, and D. Y. Chen. "Dose intensity of cisplatin and gemcitabine (CG) for muscle invasive urothelial bladder cancer (MIUBC) in the neoadjuvant versus adjuvant settings: The Fox Chase Cancer Center (FCCC) experience." Journal of Clinical Oncology 27, no. 15_suppl (May 20, 2009): e16012-e16012. http://dx.doi.org/10.1200/jco.2009.27.15_suppl.e16012.
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e16012 Background: Despite radical cystectomy (RC), subjects with MIUBC remain at risk of recurrence and optimal timing of chemotherapy remains unclear. Historically, we offered adjuvant (ADJ) CG to patients (pts) with T≥3 or node positive tumors. Since Dec 2005, neoadjuvant (NEO) CG treatment has been our preferred approach for pts with ≥T2 tumors. We compare our results with NEO and ADJ CG, including pathologic response after NEO CG. Methods: We reviewed records of patients who underwent RC for MIUBC and received at least 2 cycles of perioperative CG between Jan 2002 and Dec 2008at FCCC. We used Fisher's exact and Wilcoxon tests, as well as Generalized Estimating Equations (GEE), to compare baseline patient characteristics, dose intensity, completion, and complication rates in the ADJ and NEO groups. Results: Results of 22 ADJ and 17 NEO patients are shown below. Characteristics of age, gender, race, preop T stage and ECOG performance status (PS) ≤1 were similar. There was no significant difference in preoperative T stage between groups (p=0.2). The delivery of full doses (C 70mg/m2,G 1000 mg/m2) of GC were similar in the NEO vs the ADJ group. The delay between RC and chemotherapy may be shorter in the patients treated preoperatively, and trended toward significance (p=0.06). Reasons for dose modifications and delays are also shown in the Table . Of pts treated with NEO CG, 10/17 (55.7%) were ≤pT1 with 9 of those 10 pts also pN0 (52.9% of 17 pts) . 5/17 (29.4%) had positive lymph nodes at RC. Conclusions: CG for MIUBC is well tolerated in the NEO setting, with dose intensity comparable to that delivered adjuvantly. NEO pathologic downstaging with CG appears comparable to historical results with MVAC, and should be confirmed in larger, prospective studies. [Table: see text] No significant financial relationships to disclose.
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Cheza, Alexander, Boikhutso Tlou, and Danai Tavonga Zhou. "Incidence of non-communicable diseases (NCDs) in HIV patients on ART in a developing country: Case of Zimbabwe’s Chitungwiza Central Hospital—A retrospective cohort study (2010–2019)." PLOS ONE 16, no. 5 (May 27, 2021): e0252180. http://dx.doi.org/10.1371/journal.pone.0252180.
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Introduction The incidence of non-communicable diseases (NCDs) has been reported to be rising over the years leading up to 2010. In Zimbabwe, there are few studies done to examine the incidence of NCDs in people living with HIV (PLHIV) on anti-retroviral treatment (ART). Objective To determine the incidence of NCDs in HIV patients on ART at the Chitungwiza Central Hospital over ten years and the associated risk factors. Methods This was a retrospective cohort study using data from 203 patients enrolled on ART at the Chitungwiza Central Hospital between 2010 and 2019. All 500 records were considered and the selection was based on participants’ consenting to the study and their strict adherence to ART without absconding. The incidence of NCDs was determined and generalized estimating equations (GEE) were used to estimate the association between NCDs and the selected risk factors. Findings Data collected at the study’s baseline (2010) showed that the most prevalent NCD was hypertension, found in (18/203) 8.9% of the study participants, followed by diabetes (6.9%), then followed by cardiovascular diseases (CVD) (3.9%), and the least common NCD was cancer (1.9%). Incidences of all of these NCDs showed an increasing trend as the time of follow-up progressed. The factors found to be significantly associated with the development of NCDs were gender (p = 0.002) and follow-up time (p<0.001). Geographical location was a significant risk factor as urban patients were more likely to develop hypertension as compared to the peri-urban patients (p = 0.001). Conclusions NCDs and HIV comorbidity is common with women more likely than males to develop NCDs as they advance in age. There is need to devise targeted intervention approach to the respective NCDs and risk factors since they affect differently in relation to the demographic details of the participants. Recommendations This paper recommends a multi-stakeholder approach to the management of NCDs, with researchers, clinicians and the government and its various arms taking a leading role.
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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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Tirta, Made, and Dian Anggraeni. "The Development of Web-based Graphical User Interface for Learning and Fitting Generalized Estimating Equation with Spline Smoothers." Jurnal ILMU DASAR 19, no. 1 (February 23, 2018): 63. http://dx.doi.org/10.19184/jid.v19i1.6997.
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Statistical modeling (regression analyses) have been growing rapidly into various directions to accommodate various data conditions. For longitudinal or repeated measures data, one of the suitable models is GEE (Generalized Estimating Equation). In practice, to do complex modeling such as GEE, the use of statistical software is necessary and it is available on free open source software R. However, GEE modeling on R can only be access through command line interface (CLI), and most practical researchers very much rely on Graphical User Interface (GUI) based statistics software. To make access to GEE (both order 1 and 2) much easier, we developed, using Shiny toolkit, two types of web-based GUI, standard pull down menu type and e-module type (with narrative theories) that can be utilized for learning and fitting GEE. This paper discusses the features of the interfaces and illustrates the use of them.
Keywords: longitudinal data, Generalized Estimating Equation (GEE), exponential families, statistical modeling, correlated response, nonparametric, natural splines, shiny toolkit
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Owusu-Darko, Isaac, Isaac Kwasi Adu, and Nana Kena Frempong. "Application of generalized estimating equation (GEE) model on students' academic performance." Applied Mathematical Sciences 8 (2014): 3359–74. http://dx.doi.org/10.12988/ams.2014.44277.
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Otilia da Costa, Jocilene, Alice Prudêncio Jacques Maria, Paulo António Alves Pereira, Elisabete Fraga Freitas, and Francisco Emanuel Cunha Soares. "Portuguese two-lane highways: modelling crash frequencies for different temporal and spatial aggregation of crash data." Transport 33, no. 1 (August 18, 2015): 92–103. http://dx.doi.org/10.3846/16484142.2015.1073619.
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The identification of contributory factors to crash frequencies observed in different highway facilities can aid transportation and traffic management agencies to improve road traffic safety. In spite of the strategic importance of the national Portuguese road network, there are no recent studies concerned with either the identification of contributory factors to road crashes or Crash Prediction Models (CPMs) for this type of roadway. This study presents an initial contribution to this problem by focusing on the national roads NR-14, NR-101 and NR-206, which are located in Northern region of Portugal. They are two-lane single carriageway rural roads. This study analysed the crash frequencies, Average Annual Daily Traffic (AADT) and geometric characteristics of 88 two-lane road segments. The selected segments were 200-m-long and did not cross through urbanized areas. The fixed length of 200 meters corresponds to the road length used in Portugal to define a critical point. Data regarding the annual crash frequency and the AADT were available from 1999 to 2010. Due to the high number of zero-crash records in the initial database, the data were explored to identify the best statistical modelling approach to be adopted. The Generalized Estimating Equations (GEE) procedure was applied to 10 distinctive databases formed by grouping the original data in time and space. The results show that the different observations within each road segment present an exchangeable correlation structure type. This paper also analyses the impact of the sample size on the model’s capability of identifying the contributing factors to crash frequencies. The major contributing factors identified for the two-lane highways studied were the traffic volume (expressed in AADT), lane width, vertical sinuosity, and Density of Access Points (DAP). Acceptable CPM was identified for the highways considered, which estimated the total number of crashes for 400-m-long segments for a cumulative period of two years.
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Angela, Vitrano, Rita Barone, Gaetano Restivo Pantalone, Paolo Rigano, Marcello Capra, Liana Cuccia, Francesco Gagiardotto, et al. "Repeated Echocardiographic Left Ventricular Ejection Fraction Measurements: A Strong and Accessible Tool for Detecting At High Risk of Heart Failure Thalassemia Major Population,." Blood 118, no. 21 (November 18, 2011): 3197. http://dx.doi.org/10.1182/blood.v118.21.3197.3197.
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Abstract Abstract 3197 Background: The prognosis for thalassemia major (TM) has dramatically improved in the last two decades. However, many transfusion-dependent patients continue to develop secondary iron overloading, and eventually death, particularly from cardiac disease. The possibility of detecting easily and earliest the patients at risk of cardiac death is so far the main challenge of clinical management of these patients. Therefore, the mean reduction of Left Ventricular Ejection Fraction (LVEF), determined by echocardiography, was evaluated over the time. Methods: Among the 413 observed patients only 188 had complete records for LVEF measurements during, at least, five considered consecutive years. Included patients were divided into two cohorts: the not alive and the alive-group with 22 and 166 patients, respectively. Generalized Estimating Equations (GEE) model was used to show the reduction of the mean of LVEF (Hedeker & Gibbons, 2006). This approach was implemented in the 'xtgee' procedure of Stata 11 software (StataCorp, College Station, TX, USA). The logistic regression model was used to evaluate the risk of death (Collet D. 2003). In this analysis, the mean reduction of LVEF was categorized into three levels: the baseline category including all patients with an increase greater than 0%, the category 1 including all patients with a reduction greater than 0% but less than 7% and the category 2 including all patients with a reduction higher or equal to 7%. All of the statistical analyses were performed under code at the Department for Mathematical and Statistical Sciences 'S. Vianelli', University of Palermo (Italy) by A.V. Results: Baseline findings are shown on Table I. Figure 1 shows the proÞles of the GEE model for the mean LVEF between the two groups. The regression coefficient of Status×Time shows a statistically significant linear decrease over the time of 1,51 per year of the mean LVEF between not alive versus alive patients (Coeff. −1.51, CI (−2,31;−0,71), p-value<0,0001,Fig. 1). Patients with a mean reduction of LVEF greater or equal to 7% over the time had a statistical significant higher risk of death from heart failure (OR= 4,93,95% CI 1,61;15,11, p-value = 0,005). Discussion: Recently, Kirk et al. 2009 suggested as cardiac T2* magnetic resonance is able to detect patients at high risk of heart failure and arrhythmia from myocardial siderosis. However, other studies showed the presence of patients with abnormal heart function and normal heart T2* and did not suggest lower heart T2* for patients suffering from arrhythmia (Pepe et al., 2006; Marsella et al.,2011). Moreover, although the use of T2* is spreading, its availability is so far limited. Instead, availability of echocardiography is surely greater. Moreover, interobserver and intraobserver reliability for the visual assessment of the global LVEF measurements have been extensively shown even in comparison with Magnetic Resonace Imaging (Hoffmann et al. 2005; Gimelli et al. 2008; Blondheim et al., 2008; Sjzli et al. 2011). Therefore, repeated measurements of LVEF may be a strong and more accessible tool for detecting at risk of heart failure TM population. Disclosures: No relevant conflicts of interest to declare.
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Zhao, Dongdong, Caiyun Hu, Jian Chen, Bao Dong, Qiongqiong Ren, Dandan Yu, Yuanyuan Zhao, Jie Li, Yujun Huang, and Yehuan Sun. "Risk factors of geriatric depression in rural China based on a generalized estimating equation." International Psychogeriatrics 30, no. 10 (January 30, 2018): 1489–97. http://dx.doi.org/10.1017/s1041610218000030.
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ABSTRACTBackground:The number of elderly individuals living in China is increasing rapidly. The aim of this study was to examine the potential risk factors of geriatric depression in rural areas.Methods:A repeated cross-sectional study was conducted between January 2015 and October 2016 in rural China. Nine hundred forty-five elderly individuals were included in both investigations. A generalized estimating equation (GEE) was used to examine the relationships between geriatric depression and socio-demographics, the number of chronic diseases, ADL (Activity of Daily Living) disability, cognitive impairment, and anxiety.Results:Among the participants, the majority was female (61.4%) and illiterate (81.5%) and had a general economic status (63.0%) and more than two kinds of chronic diseases (62.9%). The bivariate analysis indicated that geriatric depression was associated with social support, education level, economic status, ADL disability, anxiety disorders, and cognitive impairment at both survey time points. The GEE results showed that poor economic status (OR = 8.294, p < 0.001), the presence of more than two chronic diseases (OR = 1.681, p = 0.048), ADL disability (OR = 2.184, p < 0.001), cognitive impairment (OR = 1.921, p < 0.001), and anxiety (OR = 5.434, p < 0.001) were risk factors for geriatric depression in rural China; better social support (OR = 0.924, 95% CI = 0.899–0.949, p < 0.001) was found to be a protective factor.Conclusions:Geriatric depression in rural China was associated with several socio-demographic, physical, and mental factors. Targeted interventions are essential to improve the psychological health of aged individuals in rural China.
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Maggio, Aurelio, Marcello Capra, Liana Cuccia, Francesco Gagliardotto, Paolo Rigano, Giusi Calvaruso, Lorella Pitrolo, et al. "Long-Term Use of Deferiprone Enhances Significantly the Left Ventricular Ejection Function in Thalassemia Major." Blood 118, no. 21 (November 18, 2011): 5302. http://dx.doi.org/10.1182/blood.v118.21.5302.5302.
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Abstract Abstract 5302 Background: A multicentre randomized controlled trial (RCT) was designed to assess the effectiveness of long-term sequential deferiprone-deferoxamine (DFO-DFP) versus DFP alone to treat thalassaemia major (TM) (Maggio et al.,2009). Effectviness, survival, adverse events and costs were comparable between the groups. These findings were confirmed in a further 21-month follow-up (Pantalone et al., 2011). Moreover, deferiprone-alone has been reported to be superior to deferoxamine for the removal of cardiac iron and improvement in left ventricular ejection function (LVEF). However, little is known of its relative effect on LVEF after long-term treatment. Therefore, data from this prospective RCT were retrospectively analyzed to assess the LVEF responses to these treatments. Methods: In this retrospective survey of RCT, 99 patients with TM received, from September 30, 2000 to December 31, 2007, LVEF study consecutively (Table I). Generalized Estimating Equations (GEE) model was used to show the possible change of the mean of LVEF over the time between the Sequential DFP-DFO versus the DFP (Hedeker & Gibbons, 2006). This approach was implemented in the 'xtgee' procedure of Stata 11 software (StataCorp, College Station, TX, USA). All of the statistical analyses were performed under code at the Department for Mathematical and Statistical Sciences 'S. Vianelli', University of Palermo (Italy) by A.V. Results: Baseline findings are shown on Table I. Figure 1 shows the proÞles of the GEE model for the change in the mean LVEF between the two groups. The regression coefficient of treatment suggests as, the DFP-group shows statistically significant increase of mean ejection fraction over time (Coeff. 0,97, 95% CI (0,51; 1,44), p-value<0,0001, Fig. I). Actually, while the Sequential DFP-DFO treatment shows a slight advantage in terms of difference in the mean of LVEF (Coeff. 2,36, 95% CI (0,02; 4,71), p-value=0,047,Fig. I), it does not increase significantly LVEF over the time (Coeff. −0,34, 95% CI (−1,09; 0,39), p-value=0,359). Discussion: Previous retrospective studies suggested as deferiprone –treated patients had higher LVEF in comparison with Deferoxamine (DFO) or Deferasirox (DFX) treated groups (Anderson et al., 2002; Pepe et al., 2011). Moreover, survival analysis suggested a substantial decline in cardiac deaths in recent years, related to switching high-risk patients from subcutaneous desferrioxamine to chelation regimes which include the oral chelator deferiprone (Borgna-Pignatti et al., 2006; Telfer et al., 2009). Finally, Pennell et al. 2006 suggested, during a RCT over 1 year comparing DFO versus DFP, as LVEF increased significantly more in the deferiprone-treated group (3.1% vs 0.3% absolute units; P =.003). This retrospective survey of long-term prospective RCT, shows as the effect of deferiprone-alone treatment increases significantly during the years (Coeff. 0,97, 95% CI (0,51; 1,44), p-value<0,0001, Fig. 1). Cardioprotective effect of deferiprone on mitochondrial function in cultured, iron-loaded heart cells has been suggested (Link et al., 1999;Glickstein et al., 2006;Xu et al.,2006), even at concentrations below the iron-mobilizing effect (Link et al.,1999). These findings clincally support as long-term treatment with DFP-alone enhances LVEF over the years and may prevent death due to heart failure in patients with thalassemia major. Disclosures: Off Label Use: Deferiprone on sequencial way with Deferoxamine.
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Madrid García, A., J. Font, D. Freites Nuñez, L. León, C. Lajas, E. Pato, J. A. Jover, B. Fernandez, L. Abasolo, and L. Rodriguez Rodriguez. "SAT0598 PREDICTORS OF HEALTH-RELATED QUALITY OF LIFE IN PATIENTS WITH MUSCULOSKELETAL DISEASES: A LONGITUDINAL ANALYSIS FROM AN ELECTRONIC HEALTH RECORD DATABASE." Annals of the Rheumatic Diseases 79, Suppl 1 (June 2020): 1257–58. http://dx.doi.org/10.1136/annrheumdis-2020-eular.5835.
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Background:Musculoskeletal diseases (MSKs) are the major cause of disability, affecting the patient’s daily activities as well as their Quality of Life (QoL).Objectives:To analyze in a cohort of patients with MSKs the influence of demographic and clinical predictors in repeated measures of Health-Related QoL (HRQoL) obtained from a departmental electronic health record (EHR)Methods:Patients attending the Hospital Clínico San Carlos rheumatology outpatient clinic (HCSC-MSK cohort) from April 1st, 2007 to November 30th, 2017 who were seen at least twice were included in this study. Visits separated more than one year from previous or next visit were excluded. Our primary outcome was HRQoL collected in each patient’s visit using the Rosser Classification Index (it ranges between 0 and 1, the higher value, the higher HRQoL. It is calculated as the combination of two subscales: disability and distress). Demographic and clinical variables, such as diagnoses, treatments, and comorbidities were included as predictors. Variable selection was carried out in 4 steps. First, we developed a bivariate Generalized Estimating Equations model, selecting those variables with a p-value < 0.15. Second, those with a pair-wise absolute correlation < 0.95 were carried to the next step. Third, multivariate GEE analyses were performed including clinical related variables, and those independently associated with our primary outcome were selected with a p-value < 0.15. At least one predictor of each multivariate analyses was included even if none of them had p-value < 0.15. In this case the one, with the lowest p-value was chosen. Fourth, a grid search approach was followed to determine the penalization value and a penalized GEE model was implemented including all selected variables and using an independence correlation structure. Those with an estimate greater than 10^-3 and a z-robust > 1.96 were considered to be independently associated with the HRQoL.Results:A total of 18,187 patients with 95,960 outpatient visits were included in this study. From the initial 410 predictors, 289, 241, 153, and 34 were selected in the first, second, third, fourth and fifth steps. The median age was 56.4 years. The major part of the patients were women (69.4%), and the medianRosservalue was 0.986. More common diagnoses, treatments and comorbidities were back pain, tendinitis, pain in joint; analgesia, gastric protector and NSAIDs; hypertension, dyslipidemia and diabetes respectively. The predictors with a greater negative impact in HRQoL were the use of 3rd level analgesics and azathioprine, a presence of kidney failure, fibromyalgia, and ischemic heart disease. Conversely, use of symptomatic slow action drugs for osteoarthritis, statins, lowering uric acid drugs, a diagnose of mixed connective tissue disease, and better HRQoL in the past six months were independently associated with a positive impact in the HRQoL.Conclusion:We have identified several diagnoses, treatments and comorbidites independently associated with HRQoL in a cohort of patients followed up in a rheumatology outpatient clinic. This represent a first step in the implementation of value-based care for MSK patients, as we can now review the procedures associated with a worse HRQoL in an attempt to improve them.References:Predictor selection. Dx: Diagnoses. DxE: Diagnoses Episode. DxTFT: Diagnoses Total Follow-up Time.DxTET: Diagnoses Total Episode Follow-up Time T: Treatment. DxE: Treatment Episode. DxTFT:Treatment Total Follow-up Time. DxTET: Treatment Total Episode Follow-up Time. XM: Comorbiditiesand Concomitant Treatments. DM: Demographics. HS: Health Status.Disclosure of Interests:None declared
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Zeger, Scott L., Kung-Yee Liang, and Paul S. Albert. "Models for Longitudinal Data: A Generalized Estimating Equation Approach." Biometrics 44, no. 4 (December 1988): 1049. http://dx.doi.org/10.2307/2531734.
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