Missing radiographic data handling in randomized clinical trials in rheumatoid arthritis

• Activity Number: 14
• Type: Topic Contributed
• Date/Time: Sunday, July 31, 2011 : 2:00 PM to 3:50 PM
• Sponsor: International Chinese Statistical Association
• Author(s): Xiaohong Huang*+
• Companies: sanofi-aventis
• Keywords: linear extrapolation ; mean rank imputation ; multiple imputation ; rank ANCOVA ; LOCF

Abstract

In recent years, there have been growing interests in compounds to slow down the structural joint damage in rheumatoid arthritis (RA) patients. Radiographic analyses results have become essential in establishing a ``delay in structural progression' claim in newly developed agents for the treatment of RA. It is well known that the radiographic progression data generally follow a non-normal distribution with excessive zeros. A special concern about the radiographic data analyses is the handling of the high rate of missing values which has considerable sensitivity to the imputation method chosen under the complexity of the non-normal data and the unique missing mechanism. In this research, we proposed both an innovative multiple imputation algorithm and a novel mean rank imputation method under the non-parametric framework for sensitivity analyses. A simulation study was designed using rank ANCOVA analysis to extensively assess and compare the finite performance of these two new methods along with four traditional missing data handling methods, i.e., linear extrapolation, LOCF, median quartile bin imputation and median imputation under various settings.