scholarly journals Laplace approximation, penalized quasi-likelihood, and adaptive Gauss–Hermite quadrature for generalized linear mixed models: towards meta-analysis of binary outcome with sparse data

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Ke Ju ◽  
Lifeng Lin ◽  
Haitao Chu ◽  
Liang-Liang Cheng ◽  
Chang Xu
Keyword(s):  
Mixed Models ◽  
Generalized Linear Mixed Models ◽  
Meta Analysis ◽  
Linear Mixed Models ◽  
Sparse Data ◽  
Laplace Approximation ◽  
Binary Outcome ◽  
Gauss Hermite Quadrature

Generalized linear mixed models for meta-analysis

1999 ◽  
Vol 18 (6) ◽  
pp. 643-654 ◽  
Author(s):  
Robert W. Platt ◽  
Brian G. Leroux ◽  
Norman Breslow
Keyword(s):  
Mixed Models ◽  
Generalized Linear Mixed Models ◽  
Meta Analysis ◽  
Linear Mixed Models

l2-Penalized temporal logit-mixed models for the estimation of regional obesity prevalence over time

2021 ◽  
pp. 096228022110175
Author(s):  
Jan P Burgard ◽  
Joscha Krause ◽  
Ralf Münnich ◽  
Domingo Morales
Keyword(s):  
Parameter Estimation ◽  
Medical Treatment ◽  
Mixed Models ◽  
Generalized Linear Mixed Models ◽  
Linear Mixed Models ◽  
Obesity Prevalence ◽  
Model Parameter ◽  
Model Parameter Estimation ◽  
Public Health Reporting ◽  
Over Time

Obesity is considered to be one of the primary health risks in modern industrialized societies. Estimating the evolution of its prevalence over time is an essential element of public health reporting. This requires the application of suitable statistical methods on epidemiologic data with substantial local detail. Generalized linear-mixed models with medical treatment records as covariates mark a powerful combination for this purpose. However, the task is methodologically challenging. Disease frequencies are subject to both regional and temporal heterogeneity. Medical treatment records often show strong internal correlation due to diagnosis-related grouping. This frequently causes excessive variance in model parameter estimation due to rank-deficiency problems. Further, generalized linear-mixed models are often estimated via approximate inference methods as their likelihood functions do not have closed forms. These problems combined lead to unacceptable uncertainty in prevalence estimates over time. We propose an l2-penalized temporal logit-mixed model to solve these issues. We derive empirical best predictors and present a parametric bootstrap to estimate their mean-squared errors. A novel penalized maximum approximate likelihood algorithm for model parameter estimation is stated. With this new methodology, the regional obesity prevalence in Germany from 2009 to 2012 is estimated. We find that the national prevalence ranges between 15 and 16%, with significant regional clustering in eastern Germany.

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