Estimating Information Growth in Longitudinal Clinical Trials and Settings Leading to Nonmonotonicity

• Activity Number: 115
• Type: Contributed
• Date/Time: Monday, August 3, 2009 : 8:30 AM to 10:20 AM
• Sponsor: WNAR
• Author(s): Abigail Shoben*+ and Kyle Rudser and Scott Emerson
• Companies: University of Washington and The University of Minnesota and University of Washington
• Keywords: clinical trials ; information growth ; longitudinal data

Abstract

In group sequential clinical trials, it is necessary to estimate the amount of information at an interim analysis relative to the amount that would be present at the final analysis. If only one measurement is made per individual, this is often the ratio of sample sizes of the interim and final analyses. However, as discussed by Wu and Lan (1992), when the statistic of interest is a change over time with longitudinal data, such an approach overstates the information. In this talk, we discuss other problems that can result in overestimating the information, such as heteroscedasticity. We further explore situations in which the true information is nonmonotonic. For example, when using an inefficient estimator, imbalance during the conduct of the trial can lead to nonmonotonic information growth. We demonstrate the consequences of these scenarios and provide suggestions for future studies.