Concordance correlation coefficients estimated by modified variance components and generalized estimating equations for longitudinal overdispersed Poisson data

2021 ◽  
pp. 096228022110651
Author(s):  
Miao-Yu Tsai ◽  
Chia-Ni Sun ◽  
Chao-Chun Lin
Keyword(s):  
Correlation Coefficient ◽  
Generalized Estimating Equations ◽  
Variance Component ◽  
Estimating Equations ◽  
Estimating Equation ◽  
Repeated Measurements ◽  
Concordance Correlation Coefficient ◽  
Concordance Correlation ◽  
Poisson Data ◽  
Generalized Estimating

For longitudinal overdispersed Poisson data sets, estimators of the intra-, inter-, and total concordance correlation coefficient through variance components have been proposed. However, biased estimators of quadratic forms are used in concordance correlation coefficient estimation. In addition, the generalized estimating equations approach has been used in estimating agreement for longitudinal normal data and not for longitudinal overdispersed Poisson data. Therefore, this paper proposes a modified variance component approach to develop the unbiased estimators of the concordance correlation coefficient for longitudinal overdispersed Poisson data. Further, the indices of intra-, inter-, and total agreement through generalized estimating equations are also developed considering the correlation structure of longitudinal count repeated measurements. Simulation studies are conducted to compare the performance of the modified variance component and generalized estimating equation approaches for longitudinal Poisson and overdispersed Poisson data sets. An application of corticospinal diffusion tensor tractography study is used for illustration. In conclusion, the modified variance component approach performs outstandingly well with small mean square errors and nominal 95% coverage rates. The generalized estimating equation approach provides in model assumption flexibility of correlation structures for repeated measurements to produce satisfactory concordance correlation coefficient estimation results.

Progression of Diabetic Complications in Subgroups of People with Long Term Diabetes Type 1 According to Clinical Course

2020 ◽  
Author(s):  
Christian Gerdes ◽  
Christoph Werner ◽  
Christof Kloos ◽  
Thomas Lehmann ◽  
Gunter Wolf ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Generalized Estimating Equations ◽  
Microvascular Complications ◽  
Microvascular Disease ◽  
Estimating Equations ◽  
Estimating Equation ◽  
Individual Risk ◽  
Binary Outcomes ◽  
Generalized Estimating

Abstract Aims Prevention and prediction of microvascular complications are important aims of medical care in people with type 1 diabetes. Since the course of the disease is heterogenous, we tried to identify subgroups with specific risk profiles for microvascular complications. Methods Retrospective analysis of a cohort of 285 people (22637 consultations) with >10 years of type 1 diabetes. Persons were grouped into slow (<15 years), fast (>15 years) and non progressors according to the average onset of microvascular complications. Generalized estimating equations for binary outcomes were applied and pseudo coefficients of determination were calculated. Results Progression to microvascular disease was associated with age (OR: 1.034 [1.001–1.068]; p=0.04), diabetes duration (OR: 1.057 [1.021–1.094]; p=0.002), HbA1c (OR: 1.035 [1.011–1.060]; p=0.005), BMI (OR: 0.928 [0.866–0.994]; p=0.034) and the social strata index (OR: 0.910 [0.830–0.998]; p=0.046). Generalized estimating equations predicted 31.02% and exclusion of HbA1c marginally reduced the value to 28.88%. The proportion of patients with LADA was higher in fast than slow progressors [13 (26.5%) vs. 14 (11.9%); p=0.019]. A generalized estimating equation comparing slow to fast progressors revealed no significant markers. Conclusion In our analysis, we were able to confirm known risk factors for microvascular disease in people with type 1 diabetes. Overall, prediction of individual risk was difficult, the effect of individual markers minor and we could not find differences regarding slow or fast progression. We therefore emphasis the need for additional markers to predict individual risk for microvascular disease.

scholarly journals Weighted generalized estimating equations for response-adaptive treatment regimes in two-stage longitudinal studies

2017 ◽  
Vol 51 (1) ◽  
pp. 79-100
Author(s):  
JESSE YENCHIH HSU ◽  
ABDUS S. WAHED
Keyword(s):  
Longitudinal Studies ◽  
Generalized Estimating Equations ◽  
Rating Scale ◽  
Estimating Equations ◽  
Repeated Measurements ◽  
Two Stage ◽  
Data Set ◽  
Treatment Regimes ◽  
Second Stage ◽  
Generalized Estimating

Two-stage longitudinal studies are common in the treatment of mental diseases, such as chronic forms of major depressive disorders. Outcomes in such studies often consist of repeated measurements of scores, such as the 24-item Hamilton Rating Scale for Depression, throughout the duration of therapy. Two issues that make the analysis of data from such two-stage studies different from standard longitudinal data are: (1) the randomization in the second stage for patients who fail to respond in the first stage; and (2) the drop-out of patients which sometimes occurs before the second stage. In this article, we show how the weighted generalized estimating equations can be used to draw inference for treatment regimes from two-stage studies. Specifically, we show how to construct weights and use them in the generalized estimating equations to derive consistent estimators of regime effects, and compare them. Large-sample properties of the proposed estimators are derived analytically, and examined through simulations. We demonstrate our methods by applying them to a depression data set.

scholarly journals Assessment of a Modified Sandwich Estimator for Generalized Estimating Equations with Application to Opioid Poisoning in MIMIC-IV ICU Patients

Stats ◽  
2021 ◽  
Vol 4 (3) ◽  
pp. 650-664
Author(s):  
Paul Rogers ◽  
Julie Stoner
Keyword(s):  
Repeated Measures ◽  
Generalized Estimating Equations ◽  
Rare Event ◽  
Estimating Equations ◽  
Small Sample ◽  
Repeated Measurements ◽  
Regression Coefficients ◽  
Binary Outcomes ◽  
Sandwich Estimator ◽  
Generalized Estimating

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

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