Missing Data Handling in Chronic Pain Trials

Abstract

In chronic pain trials, proper handling of missing data due to dropout is an important issue because the dropout rate is high and the study conclusion may depend on the method chosen. The intent-to-treat (ITT) principle usually requires imputations for missing data to include the dropouts as well as completers in the statistical analysis. However, a statistical analysis with imputation might lead to a misinterpretation of clinical data. In chronic pain trials, treatment-related dropouts are clinical outcomes themselves. For example, an early dropout due to toxicity usually indicates a treatment failure, as does a dropout due to lack of efficacy. Problems with traditional methods such as last observation carried forward (LOCF) or baseline observation carried forward (BOCF) are identified especially in the chronic pain setting. Alternative methods, such as continuous responder analysis and two-part model analysis, treating dropouts as clinical events, are introduced with an example of osteoarthritis clinical trial data.