Generalized Linear Mixed Models for Binary Data: Are Matching Results from Penalized Quasi-Likelihood and Numerical Integration Less Biased?

Open Access

Peer-reviewed

Research Article

Andrea Benedetti ,

* E-mail: andrea.benedetti@mcgill.ca

Affiliations Department of Medicine, McGill University, Montreal, Canada, Department of Epidemiology, Biostatistics & Occupational Health, McGill University, Montreal, Canada, Respiratory Epidemiology and Clinical Research Unit, Montreal Chest Institute, Montreal, Canada
⨯
Robert Platt,

Affiliations Department of Medicine, McGill University, Montreal, Canada, Department of Pediatrics, McGill University, Montreal, Canada
⨯
Juli Atherton

Affiliation Département de Mathématiques, Université de Québec à Montréal, Montreal, Canada
⨯

Reader Comments (0)

Forecasting
Is the Subject Area "Forecasting" applicable to this article?

Thanks for your feedback.
Linear regression analysis
Is the Subject Area "Linear regression analysis" applicable to this article?

Thanks for your feedback.
Statistical data
Is the Subject Area "Statistical data" applicable to this article?

Thanks for your feedback.
Normal distribution
Is the Subject Area "Normal distribution" applicable to this article?

Thanks for your feedback.
Numerical integration
Is the Subject Area "Numerical integration" applicable to this article?

Thanks for your feedback.
Probability distribution
Is the Subject Area "Probability distribution" applicable to this article?

Thanks for your feedback.
Simulation and modeling
Is the Subject Area "Simulation and modeling" applicable to this article?

Thanks for your feedback.
Approximation methods
Is the Subject Area "Approximation methods" applicable to this article?

Thanks for your feedback.