Work

A Note on Type II Error Under Random Effects Misspecification in Generalized Linear Mixed Models

Year: 2010

Type: article

Abstract: Summary In this rejoinder, we discuss the impact of misspecifying the random effects distribution on inferences obtained from generalized linear mixed models (GLMMs). Special attention is paid to the power of the tests for the fixed-effect parameters. To study this misspecification, researchers often use simulation designs in which several choices for the true underlying random-effects distribution are considered, while the assumed distribution is kept fixed. Neuhaus, McCulloch, and Boylan (2010, Biometrics 00, 000–000) argue that a logically correct approach should consist of varying the assumed, fitted distribution, while holding the true fixed. We argue that both simulation designs can bring valuable insights into the impact of the misspecification. Furthermore, using both designs, we illustrate that the power associated with the tests for the fixed-effect parameters in GLMM may be affected by misspecifying the random-effects distribution.

Source: Biometrics

DOI: 10.1111/j.1541-0420.2010.01474.x

Authors: John M. Neuhaus, Charles E. McCulloch, Ross Boylan

Institution: University of California, San Francisco

Language: English

Topic: Statistical Methods and Bayesian Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funder: National Cancer Institute

Award: Assessing / Developing Methods for Complex Dependent Data