Abstract
When faced with the myriad challenges in drug development, the discipline of Statistics (recognized by the use of an upper case “S”) is “the knight in shining armor” that rides to our assistance and facilitates the collection, analysis, and interpretation of optimal-quality data as the basis for rational decision-making at all stages of the process (Durham and Turner 2008). In this and the following two chapters, we have resisted the temptation to provide exhaustive discussion of subtle nuances of statistical analysis: our interest in the discipline of Statistics is a pragmatic one since it provides the best way currently available to conduct clinical development programs.
Our interest in the discipline of Statistics is a pragmatic one since it provides the best way currently available to conduct clinical development programs (Turner 2010).
Notes
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It should be noted that ANOVA can also be used when there are only two treatment groups. If one analyzed the same data set with an independent-group t-test and then with an independent-group ANOVA, the t statistic and the F statistic would be different numbers, but the p-value associated with each of them, i.e., the key value of interest in determining the attainment of statistical significance, would be identical.
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Further Reading
Aban IB, George B (2015) Statistical considerations for preclinical studies. Exp Neurol 270:82–87
Chavalarias D, Wallach JD, Li AH, Ioannidis JP (2016) Evolution of reporting p-values in the biomedical literature, 1990–2015. JAMA 315:1141–1148
Kyriacou DN (2016) The enduring evolution of the p-value. JAMA 315:1113–1115
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Rowe P (2015) Essential statistics for the pharmaceutical sciences, 2nd edn. John Wiley & Sons, Chichester
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Turner, J.R., Karnad, D.R., Kothari, S. (2017). Analyzing and Reporting Efficacy Data. In: Cardiovascular Safety in Drug Development and Therapeutic Use. Adis, Cham. https://doi.org/10.1007/978-3-319-40347-2_4
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