How Much Missing Data Are Too Much in Clinical Trials of Neuropharmacological Drug Products

• Activity Number: 3
• Type: Invited
• Date/Time: Sunday, July 29, 2012 : 2:00 PM to 3:50 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Ohidul I Siddiqui*+
• Companies: FDA
• Keywords: Repeated Measure Analysis ; , Longitudinal data analysis ; , missing data mechanism ; ignorable missing data

Abstract

Missing data due to dropout of patients in randomized clinical trials are potential sources of bias in the efficacy evaluation of investigational drugs. The biases might be severe when the presence of missing data is substantial. Over the last two decades, extensive research works on dealing with missing data in clinical trials were conducted by both the academicians and pharmaceutical industry statisticians. Majority of the research findings supported the use of longitudinal data analysis for efficacy evaluation of study drugs instead of using the conventional last observation carried forward (LOCF) approach for dealing with missing at random (MAR) data in drug development research. However, there is a little evidence of exploring the impact of increases in the prevalence of missing data on biases in the statistical inferences. An attempt has been made in this research to explore such impacts based on an extensive simulation study and real clinical trial study data sets.