Abstract
This chapter presents the motivations for, and consequences of, executing multiple statistical analyses in clinical trials. Logistical efficiency, the desire to build a causal argument, and the need to explore and develop unanticipated results, are appropriate and powerful factors that motivate the inclusion of multiple analyses in these randomized studies. However, their incorporation may produce results which can be difficult to integrate with the idea of controlling the type I error rate. The need to minimize the type I error rate in a clinical trial is developed, followed by an elementary introduction to one of the most useful tools for controlling type I error rates—the Bonferroni inequality. Alternative methods of controlling the type I error rate are then briefly discussed. The chapter ends by combining (1) the motivation for the prospective plan of an experiment and (2) the need to control the type I error rate into a framework to guide the design of a clinical trial. This final integration (Table 3.3) describes the three ways in which multiple analyses in clinical trials are carried out, and the implications for each of their interpretations. This result will be the basis for the development of the multiple analysis tools to which the rest of the book is devoted.
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(2003). The Lure and Complexity of Multiple Analyses. In: Multiple Analyses in Clinical Trials. Statistics for Biology and Health. Springer, New York, NY. https://doi.org/10.1007/0-387-21813-0_4
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DOI: https://doi.org/10.1007/0-387-21813-0_4
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