Statistics in medicine
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Statistics in medicine · Aug 2005
Diagnostics for assumptions in moderate to large simple clinical trials: do they really help?
In this article, primarily we look at a case study, where prior to conducting the major efficacy analysis, one performs a diagnostic test for assumptions, and acts upon the result if the diagnostic test rejects the assumptions. Specifically, we show by an example that a hybrid approach of using a diagnostic test for equality of variance in a two-sample t-test situation can adversely affect, rather than protect, the operating characteristics of the study. ⋯ Secondarily, we present rationale as to why the classical tests (or slightly modified versions) can be viewed as asymptotically non-parametric, and can actually be more robust against failure of assumptions than rank tests. Readers are cautioned that this illustration is limited to efficacy analysis, and is not meant as a criticism of other analyses, such as modelling or exploratory ones.