Mean corrected generalized estimating equations for longitudinal binary outcomes with report bias

2021 ◽  
pp. 096228022110651
Author(s):  
Chao Li ◽  
Ye Shen ◽  
Qian Xiao ◽  
Stephen L Rathbun ◽  
Hui Huang ◽  
...  
Keyword(s):  
Generalized Estimating Equations ◽  
Behavioral Therapy ◽  
Estimating Equations ◽  
The Self ◽  
Binary Outcomes ◽  
Urine Test ◽  
Test Results ◽  
Cocaine Use ◽  
Report Bias ◽  
Generalized Estimating

Cocaine addiction is an important public health problem worldwide. Cognitive-behavioral therapy is a counseling intervention for supporting cocaine-dependent individuals through recovery and relapse prevention. It may reduce patients’ cocaine uses by improving their motivations and enabling them to recognize risky situations. To study the effect of cognitive behavioral therapy on cocaine dependence, the self-reported cocaine use with urine test data were collected at the Primary Care Center of Yale-New Haven Hospital. Its outcomes are binary, including both the daily self-reported drug uses and weekly urine test results. To date, the generalized estimating equations are widely used to analyze binary data with repeated measures. However, due to the existence of significant self-report bias in the self-reported cocaine use with urine test data, a direct application of the generalized estimating equations approach may not be valid. In this paper, we proposed a novel mean corrected generalized estimating equations approach for analyzing longitudinal binary outcomes subject to reporting bias. The mean corrected generalized estimating equations can provide consistently and asymptotically normally distributed estimators under true contamination probabilities. In the self-reported cocaine use with urine test study, accurate weekly urine test results are used to detect contamination. The superior performances of the proposed method are illustrated by both simulation studies and real data analysis.

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.

Analyzing Cross-Sectionally Clustered Data Using Generalized Estimating Equations

2021 ◽  
pp. 107699862110174
Author(s):  
Francis L. Huang
Keyword(s):  
Least Squares ◽  
Generalized Estimating Equations ◽  
Multilevel Models ◽  
Clustered Data ◽  
Estimating Equations ◽  
Worked Examples ◽  
Ordinary Least Squares ◽  
Binary Outcomes ◽  
Similarities And Differences ◽  
Generalized Estimating

The presence of clustered data is common in the sociobehavioral sciences. One approach that specifically deals with clustered data but has seen little use in education is the generalized estimating equations (GEEs) approach. We provide a background on GEEs, discuss why it is appropriate for the analysis of clustered data, and provide worked examples using both continuous and binary outcomes. Comparisons are made between GEEs, multilevel models, and ordinary least squares results to highlight similarities and differences between the approaches. Detailed walkthroughs are provided using both R and SPSS Version 26.

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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