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Abstract:
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In some clinical trial scenarios for some estimands dropout does not necessarily result in missing data because the dropout is considered to be the endpoint. For example, an NRI analysis of a categorical outcome can be seen as not actually imputing missing values, but rather defining dropouts as treatment failures. For continuous endpoints BOCF can be seen in a similar light as those who dropout will not in the long run have any improvement from the drug. Although taking into account intercurrent events in their definitions, these composite estimands entail the rigid and often invalid assumption that there is no benefit from non-pharmacologic factors. The trimmed mean was developed and proposed by a prominent FDA statistician as another composite estimand and an alternative analytic approach for these scenarios. However, the research supporting the approach was not extensive. Therefore, we conducted a simulation study and an analysis of actual clinical trial data to better understand the operational characteristics of the trimmed mean method. In the presentation we will review relevant background and present the results of the simulation study and real data analysis.
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