A Two-Step Multiple Imputation for Analysis of Repeated Measures with Left-Censored and Missing Data

• Activity Number: 170
• Type: Topic Contributed
• Date/Time: Monday, August 5, 2013 : 10:30 AM to 12:20 PM
• Sponsor: Biopharmaceutical Section
• Author(s): G. Frank Liu*+ and Peter Hu and Devan Mehrotra
• Companies: Merck Res Labs and Bristol-Myers Squibb and Merck
• Keywords: Multiple imputation ; Missing data ; Censored data ; Repeated measures

Abstract

Left censored data can occur in many clinical trials in which laboratory measures are obtained with a quantitative analysis assay. Some common examples are longitudinal viral load measures in HIV drug trials, and antibody measures in vaccine studies. Conventionally, an analysis of covariance model or constraint longitudinal data analysis (cLDA) model may be applied in the analysis of log-transformed responses, with the left-censored values replaced with the quantification limit or half of the limit. When the proportion of left censoring is moderate to large, this single ad-hoc imputation method may lead to bias in parameter estimates and inappropriate alpha levels for statistical tests. In this paper, we propose a two-step multiple imputation (MI) approach to deal with the censored and missing data. The proposed two-step MI can be implemented using common software such as SAS procedures. Simulations are conducted under various scenarios to evaluate the performance of this method compared with the conventional methods. Applications to real clinical trials are also given to illustrate the potential impact and benefit of the proposed method.