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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).

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Notes

  1. 1.

    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.

References

  • Durham TA, Turner JR (2008) Introduction to statistics in pharmaceutical clinical trials. Pharmaceutical Press, London

    Google Scholar 

  • Fowler J, Jarvis P, Chevannes M (2002) Practical statistics for nursing and health care: a modern introduction. Wiley Hoboken, New Jersey, USA

    Google Scholar 

  • Gardner MF, Altman DG (1986) Estimation rather than hypothesis testing: confidence intervals rather than p-values. In: Gardner MF, Altman DG (eds) Statistics with confidence. British Medical Association, London

    Google Scholar 

  • ICH E8 (1997) General considerations for clinical trials. Available at: http://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Efficacy/E8/Step4/E8_Guideline.pdf. Accessed 23 Nov 2015

  • ICH E9 (1998) Statistical principles for clinical trials. Available at: http://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Efficacy/E9/Step4/E9_Guideline.pdf. Accessed 23 Nov 2015

  • Institute of Medicine of the National Academies (2007) The future of drug safety: promoting and protecting the health of the public. The National Academies Press, Washington, DC

    Google Scholar 

  • Machin D, Campbell MJ (2005) Design of studies for medical research. John Wiley & Sons, Chichester

    Book  Google Scholar 

  • MRC Streptomycin in Tuberculosis Trials Committee (1948) Streptomycin treatment of pulmonary tuberculosis. Br Med J 2:769–783

    Article  Google Scholar 

  • Turner JR (2010) New drug development: an introduction to clinical trials, 2nd edn. Springer, New York

    Book  Google Scholar 

  • Turner JR, Thayer JF (2001) Introduction to analysis of variance: design, analysis, and interpretation. Sage Publications, Thousand Oaks

    Book  Google Scholar 

  • Turner JR, Durham TA (2015) Must new drugs be superior to those already available? The role of noninferiority clinical trials. J Clin Hypertens (Greenwich) 17:319–321

    Article  CAS  Google Scholar 

  • Yoshioka A (1998) Use of randomisation in the Medical Research Council’s clinical trial of streptomycin in pulmonary tuberculosis in the 1940s. Br Med J 317:1220–1223

    Article  CAS  Google Scholar 

Further Reading

  • Aban IB, George B (2015) Statistical considerations for preclinical studies. Exp Neurol 270:82–87

    Article  PubMed  PubMed Central  Google Scholar 

  • Chavalarias D, Wallach JD, Li AH, Ioannidis JP (2016) Evolution of reporting p-values in the biomedical literature, 1990–2015. JAMA 315:1141–1148

    Article  CAS  PubMed  Google Scholar 

  • Kyriacou DN (2016) The enduring evolution of the p-value. JAMA 315:1113–1115

    Article  PubMed  Google Scholar 

  • LaVange LM (2014) The role of statistics in regulatory decision making. Ther Innov Regul Sci 48:10–19

    Article  Google Scholar 

  • LaVange LM, Permutt T (2015) A regulatory perspective on missing data in the aftermath of the NRC report. Stat Med [Epub ahead of print]

    Google Scholar 

  • Rowe P (2015) Essential statistics for the pharmaceutical sciences, 2nd edn. John Wiley & Sons, Chichester

    Book  Google Scholar 

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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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  • DOI: https://doi.org/10.1007/978-3-319-40347-2_4

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