JSM 2005 - Toronto

Abstract #304476

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 323
Type: Contributed
Date/Time: Tuesday, August 9, 2005 : 2:00 PM to 3:50 PM
Sponsor: Biopharmaceutical Section
Abstract - #304476
Title: Missing Data in Longitudinal Controlled Clinical Trial: A Power Comparison for Intent-to-treat Analysis
Author(s): Hrishikesh Chakraborty*+ and Hong Gu
Companies: RTI International and Dalhousie University
Address: 3040 Cornwallis RD, RTP, NC, 27709, United States
Keywords: longitudinal study ; controlled clinical trials ; missing value ; intent-to-treat analysis ; mixed model ; power
Abstract:

Missing values and dropouts are common issues in all areas of medicine and public health research, and intent-to-treat (ITT) analysis has become a widely accepted method for analysis of clinical trials in the pharmaceutical industry. In most clinical trials, some patients do not complete their intended followup according to the protocol, generating missing values. Missing values lead to concern and confusion in identifying the ITT population and makes data analysis more complex and challenging. There is no adequate strategy for ITT analyses of longitudinal clinical trial data with missing values. Use of ad hoc strategies dealing with missing values for an ITT analysis is common practice of clinical trials. A detailed investigation based on simulation studies was performed to develop recommendations for ITT analysis of longitudinal controlled clinical trial data with missing values. We compared sizes and powers between popular ad hoc approaches and linear mixed model under different missing scenarios. Simulation results suggest that for a high percentage of missing values, mixed model approach without any ad hoc imputation provide more powerful comparison.

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Revised March 2005