Survival Models: Analysis of Binary Outcomes in Longitudinal Studies Using Weighted Estimating Equations and Discrete-Time Survival Methods: Prevalence and Incidence of Smoking in An Adolescent Cohort

2005 ◽  
pp. 161-185 ◽  
Author(s):  
John B. Carlin ◽  
Rory Wolfe ◽  
Carolyn Coffey ◽  
George C. Patton
Keyword(s):  
Longitudinal Studies ◽  
Discrete Time ◽  
Estimating Equations ◽  
Survival Models ◽  
Binary Outcomes

Robust designs accounting for model uncertainty in longitudinal studies with binary outcomes

2019 ◽  
Vol 29 (3) ◽  
pp. 934-952
Author(s):  
Jérémy Seurat ◽  
Thu Thuy Nguyen ◽  
France Mentré
Keyword(s):  
Monte Carlo ◽  
Longitudinal Studies ◽  
Robust Design ◽  
Fisher Information ◽  
Model Uncertainty ◽  
Fisher Information Matrix ◽  
Information Matrix ◽  
Average Power ◽  
Binary Outcomes ◽  
Mixed Effect

To optimize designs for longitudinal studies analyzed by mixed-effect models with binary outcomes, the Fisher information matrix can be used. Optimal design approaches, however, require a priori knowledge of the model. We aim to propose, for the first time, a robust design approach accounting for model uncertainty in longitudinal trials with two treatment groups, assuming mixed-effect logistic models. To optimize designs given one model, we compute several optimality criteria based on Fisher information matrix evaluated by the new approach based on Monte-Carlo/Hamiltonian Monte-Carlo. We propose to use the DDS-optimality criterion, as it ensures a compromise between the precision of estimation of the parameters, and hence the Wald test power, and the overall precision of parameter estimation. To account for model uncertainty, we assume candidate models with their respective weights. We compute robust design across these models using compound DDS-optimality. Using the Fisher information matrix, we propose to predict the average power over these models. Evaluating this approach by clinical trial simulations, we show that the robust design is efficient across all models, allowing one to achieve good power of test. The proposed design strategy is a new and relevant approach to design longitudinal studies with binary outcomes, accounting for model uncertainty.

Analysing child mortality in Nigeria with geoadditive discrete-time survival models

2005 ◽  
Vol 24 (5) ◽  
pp. 709-728 ◽  
Author(s):  
Samson B. Adebayo ◽  
Ludwig Fahrmeir
Keyword(s):  
Child Mortality ◽  
Discrete Time ◽  
Survival Models

scholarly journals P.2.29 Does a change in working conditions influence labour force participation among workers with a chronic disease?

2019 ◽  
Vol 76 (Suppl 1) ◽  
pp. A94.3-A95
Author(s):  
Jolinda Schram ◽  
Suzan Robroek ◽  
Patricia Ots ◽  
Sander van Zon ◽  
Sandra Brouwer ◽  
...  
Keyword(s):  
Social Support ◽  
Chronic Disease ◽  
Working Conditions ◽  
Discrete Time ◽  
Older Workers ◽  
Labour Force ◽  
Job Demands ◽  
Physical Workload ◽  
Survival Models ◽  
Paid Employment

ObjectiveThis study investigated the association between changing working conditions and exit from paid employment during the following year among older workers with a chronic disease in the Netherlands.MethodFour annual waves from the Study on Transitions in Employment, Ability and Motivation (STREAM; 2010–2013) provided information on working conditions and demographics for 2838 older workers with a chronic disease, aged 45–64 years. The analytical sample consisted of 5491 responses from 2838 workers. Five types of working conditions were investigated; physical workload, psychological job demands, job autonomy, emotional job demands and social support. Discrete-time survival models were used to estimate the associations of change in working conditions in a particular year on the probability of exiting paid work for persons with a chronic disease in the following year.ResultsOf the 2838 workers, a small majority was male (52%), most workers had an intermediate level of education (39.7%), and the mean age was 53.7 years (SD 5.50). Results showed that working conditions substantially changed (i.e. difference of one standard deviation) between two waves. Social support and emotional job demands had the highest amounts of substantial changes (17% and 19%), while physical demands remained relatively stable (6% substantial change). After the first two waves, about 12% of workers with a chronic illness left paid employment. Results of discrete-time survival models are expected to be available in 2019 (by the time of the EPICOH conference).ConclusionEnsuring that working conditions can be adapted to the needs of older workers who have a chronic disease may help to extend working life.

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.

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