This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 396
Type: Topic Contributed
Date/Time: Tuesday, August 3, 2010 : 2:00 PM to 3:50 PM
Sponsor: Section on Survey Research Methods
Abstract - #306516
Title: Estimation Bias in Complete-Case Analysis in Crossover Studies with Missing Data
Author(s): Fang Liu*+
Companies: Merck Research Laboratories
Address: 126 E. Lincoln Ave, Rahway, NJ, 07065,
Keywords: All Available Data ; Missing Data Mechanism ; Right Truncation ; Type-I Error ; Efficiency
Abstract:

Crossover designs are used often in clinical trials. It is not uncommon that subjects discontinue before completing all treatment periods. Despite availability of statistical methods utilizing all available data, naive approaches, such as the complete case (CC) analysis, which is only valid under missing completely at random, are still widely used in practice. We obtain the analytical form of the estimation bias of treatment effects with CC for 2- and 3-period crossover studies. We use simulation to examine the inflation of type-I error and efficiency loss in the inferences with CC. Invalidity and inefficiency of two other practical approaches for defining analyzed data in the presence of missing data -- data from at least two periods (2P) in multi-period crossover and available cases for a specific comparison of interest -- are also demonstrated through simulation studies.


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