Independence and statistical inference in clinical trial designs: a tutorial review

The requirements for statistical approaches to the design, analysis, and interpretation of experimental data are now accepted by the scientific community. This is of particular importance in medical studies where public health consequences are of concern. Investigators in the clinical sciences should be cognizant of statistical principles in general, but should always be wary of the pursuing their own analyses and engage statisticians for data analysis whenever possible. Examples of circumstances that require statistical evaluation not found in textbooks and not always obvious to the lay person are pervasive. Incorrect statistical evaluation and analyses in such situations will result in erroneous and potentially serious misleading interpretation of clinical data. Although a statistician may not be responsible for any misinterpretations in such unfortunate circumstances, the quote often cited about statisticians and "damned liars" may appear to be more truth than fable. This article is a tutorial review and describes a common misuse of clinical data resulting in an apparently large sample size derived from a small number of patients. This mistake is a consequence of ignoring the dependency of results, treating multiple observations from a single patient as independent observations.