Journal of biopharmaceutical statistics
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Handling missing data is an important consideration in the analysis of data from all kinds of medical device studies. Missing data in medical device studies can arise for all the reasons one might expect in pharmaceutical clinical trials. In addition, they occur by design, in nonrandomized device studies, and in evaluations of diagnostic tests. ⋯ Many types of missing data that can occur with diagnostic test evaluations are surveyed. Careful planning and conduct are recommended to minimize missing data. Although difficult, the prespecification of all missing data analysis strategies is encouraged before any data are collected.
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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. ⋯ 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.