Multiple Imputation Techniques in Small Sample Clinical Trials

• Activity Number: 121
• Type: Contributed
• Date/Time: Monday, August 9, 2004 : 12:00 PM to 1:50 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Sunni A. Barnes*+ and Stacy R. Lindborg and John W. Seaman
• Companies: Mayo Clinic and Eli Lilly and Company and Baylor University
• Keywords: multiple imputation ; LOCF ; missing data ; MAR ; Bayesian least squares

Abstract

Clinical trials allow researchers to draw conclusions about the effectiveness of a treatment. However, the statistical analysis used to draw these conclusions will inevitably be complicated by the common problem of attrition. Resorting to ad hoc methods such as case-deletion or mean imputation can lead to biased results, especially if the amount of missing data is high. Multiple imputation, on the other hand, provides the researcher with an approximate solution that can be generalized to a number of different datasets and statistical problems. Multiple imputation is known to be statistically valid when n is large. However, questions still remain about the validity of multiple imputation for small samples in clinical trials. We investigate the small-sample performance of several multiple imputation methods, as well as the last observation carried forward method. We also introduce a nonparametric multiple imputation procedure that incorporates more information than those currently in use and does so for less computational expense.