Simultaneous Confidence Intervals Among k Mean Vectors in Repeated Measures with Missing Data

SYNOPTIC ABSTRACT

In this article, we consider a test for the equality of k mean vectors in the intraclass correlation model with monotone missing data. We derive simultaneous confidence intervals for all pairwise comparisons and for comparisons with a control by using the idea in Koizumi and Seo (2009). Finding distributions of T²_max type statistics exactly is extremely difficult even if the complete data are obtained. Hence we discuss the approximation to the upper percentage point of T²_max type statistic by using Bonferroni's inequality. Finally, the accuracy and conservativeness for procedures proposed in this article are evaluated by Monte Carlo simulation. Two examples are also given.

Key words and phrases:

• Intraclass correlation model
• Monotone missing data
• Simultaneous confidence intervals
• Pairwise comparisons
• Comparisons with a control
• Hotelling's T²-statistic
• Bonferroni inequality