JSM 2014 Home
Online Program Home
My Program

Abstract Details

Activity Number: 70
Type: Contributed
Date/Time: Sunday, August 3, 2014 : 4:00 PM to 5:50 PM
Sponsor: Biometrics Section
Abstract #311982 View Presentation
Title: Statistical Power of Approximate Tests in Two-Stage Nonlinear Mixed Models
Author(s): Jeff Burton*+ and Robbie Beyl and William Johnson
Companies: Pennington Biomedical Research Center and Pennington Biomedical Research Center and Pennington Biomedical Research Center
Keywords: Nonlinear ; Mixed model ; Two-stage ; Power ; Testing ; Fisher information matrix
Abstract:

Here, we investigate power of tests about fixed effects parameters in nonlinear mixed models. For obtaining estimates of unknown model parameters needed for construction of test statistics, a two-stage approach is implemented. The tests of interest that shall be compared are the large-sample Wald and likelihood ratio tests as well as several approximate F-tests. For the Wald and approximate F-tests, we modify the test statistics by developing an approximation to the large-sample covariance matrix of estimates of fixed population parameters based on the Fisher information matrix. In contrast to previous research in which null properties of tests were investigated (Burton & Volaufova, 2014), our focus here is on comparing power of tests using various configurations of fixed model parameters under the alternative hypothesis. A simulation study using a one-compartment pharmacokinetic model is used for illustration.


Authors who are presenting talks have a * after their name.

Back to the full JSM 2014 program




2014 JSM Online Program Home

For information, contact jsm@amstat.org or phone (888) 231-3473.

If you have questions about the Professional Development program, please contact the Education Department.

The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.

ASA Meetings Department  •  732 North Washington Street, Alexandria, VA 22314  •  (703) 684-1221  •  meetings@amstat.org
Copyright © American Statistical Association.