Assessing response profiles from incomplete longitudinal clinical trial data with subject dropout under regulatory conditions

Abstract

Treatment effects are often evaluated by comparing change over time in outcome measures. However, valid analyses of longitudinal data can be problematic, particularly when some data are missing for reasons related to the outcome. In choosing the primary analysis for confirmatory clinical trials, regulatory agencies have for decades favored the last observation carried forward (LOCF) approach for imputing missing values. Many advances in statistical methodology, and also in our ability to implement those methods, have been made in recent years. The characteristics of data from acute phase clinical trials can be exploited to develop an appropriate analysis for assessing response profiles in a regulatory setting. These data characteristics and regulatory considerations will be reviewed. Approaches for handling missing data are compared along with options for modeling time effects and correlations between repeated measurements. Theory and empirical evidence are utilized to support the proposal that likelihood-based mixed-effects model repeated measures (MMRM) approaches, based on the missing at random assumption, provide superior control of Type I and Type II errors when compared with the traditional LOCF approach, which is based on the more restrictive missing completely at random assumption. It is further reasoned that in acute phase clinical trials, unstructured modeling of time trends and within-subject error correlations may be preferred