Online Program Home
  My Program

Abstract Details

Activity Number: 329 - Advances of Statistical Methodologies in Mental Health and Related Field: Some Recent Issues and Solutions
Type: Topic Contributed
Date/Time: Tuesday, August 1, 2017 : 10:30 AM to 12:20 PM
Sponsor: Mental Health Statistics Section
Abstract #322776 View Presentation
Title: Multiple Testing with Close to Equally Correlated Correlation Structure
Author(s): Boris Zaslavsky*
Companies: FDA/CBER
Keywords: Compound symmetry; ; Covariance matrices ; Monte Carlo simulation ; Quantile ; Sensitivity analysis ; Taylor expansion
Abstract:

In clinical trials with multiple primary endpoints or with multiple observations on the same sampling unit, the maximum of all observations is a convenient statistic that controls the familywise error rate. The quantile of this statistic depends on the correlation among multiple observations. To simplify modeling, the compound symmetry (CS) covariance structure is frequently used. The assumption of exact compound symmetry cannot usually be justified, and further sensitivity studies under more varied correlations are recommended. The need for multiple simulations may impose an increased demand on computer and time resources. To evaluate the sensitivity of simulation results restricted to CS structure, we calculated the linear part of the Taylor expansion of the CDF for the maximum statistic. Furthermore, we derived the Taylor expansion for quantiles of the maximum statistic. Our simulation studies on the linear approximation of quantiles confirmed good performance of the linearization formula.


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

Back to the full JSM 2017 program

 
 
Copyright © American Statistical Association