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.

Simple generalized estimating equations (GEEs) and weighted generalized estimating equations (WGEEs) in longitudinal studies with dropouts: guidelines and implementation in R

2016 ◽  
Vol 35 (19) ◽  
pp. 3424-3448 ◽  
Author(s):  
Alejandro Salazar ◽  
Begoña Ojeda ◽  
María Dueñas ◽  
Fernando Fernández ◽  
Inmaculada Failde
Keyword(s):  
Longitudinal Studies ◽  
Generalized Estimating Equations ◽  
Estimating Equations ◽  
Generalized Estimating

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.

scholarly journals Penalized generalized estimating equations for relative risk regression with applications to brain lesion data

2021 ◽  
Author(s):  
Petya Kindalova ◽  
Michele Veldsman ◽  
Thomas E Nichols ◽  
Ioannis Kosmidis
Keyword(s):  
Relative Risk ◽  
Generalized Estimating Equations ◽  
Binary Data ◽  
Large Scale ◽  
Brain Lesion ◽  
Estimating Equations ◽  
Variance Function ◽  
Parameter Estimates ◽  
Data Set ◽  
Generalized Estimating

Motivated by a brain lesion application, we introduce penalized generalized estimating equations for relative risk regression for modelling correlated binary data. Brain lesions can have varying incidence across the brain and result in both rare and high incidence outcomes. As a result, odds ratios estimated from generalized estimating equations with logistic regression structures are not necessarily directly interpretable as relative risks. On the other hand, use of log-link regression structures with the binomial variance function may lead to estimation instabilities when event probabilities are close to 1. To circumvent such issues, we use generalized estimating equations with log-link regression structures with identity variance function and unknown dispersion parameter. Even in this setting, parameter estimates can be infinite, which we address by penalizing the generalized estimating functions with the gradient of the Jeffreys prior. Our findings from extensive simulation studies show significant improvement over the standard log-link generalized estimating equations by providing finite estimates and achieving convergence when boundary estimates occur. The real data application on UK Biobank brain lesion maps further reveals the instabilities of the standard log-link generalized estimating equations for a large-scale data set and demonstrates the clear interpretation of relative risk in clinical applications.

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.

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