Abstract
In this chapter, we compare successive trials designed and conducted to assess the efficacy of a new drug to a series of diagnostic tests. The condition to diagnose is whether the new drug has a clinically meaningful efficacious effect. This comparison offers us the opportunity to apply properties pertaining to diagnostic tests discussed in Chap. 3 to clinical trials. Building on the results in Chap. 3, we discuss why replication is such a critically important concept in drug development and show why replication is not as easy as some might have hoped. The difference between replicability and reproducibility is briefly discussed. We end the chapter by highlighting the difference between statistical power and the probability of a positive trial. This last point becomes more important as a new drug moves through the various development stages as will be illustrated in Chap. 9.
Using preliminary research to approve new treatments has high costs in morbidity and healthcare dollars.
—British Medical Journal, Nov 23 2015
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References
Benjamini, Y. (2020). Replicability problems in science: It’s not the p-values’ fault, presented at a webinar hosted by the National Institute of Statistical Sciences, May 6. Available at https://www.niss.org/news/niss-webinar-hosts-third-webinar-use-p-values-making-decisions. Accessed 12 February 2021.
Chuang-Stein, C. (2006). Sample size and the probability of a successful trial. Pharmaceutical Statistics, 5(4), 305–309.
Chuang-Stein, C., & Kirby, S. (2014). The shrinking or disappearing observed treatment effect. Pharmaceutical Statistics, 13(5), 277–280.
Gibson, E. W. (2020). The role of p-values in judging the strength of evidence and realistic replication expectations. Statistics in Biopharmaceutical Research. https://doi.org/10.1080/19466315.2020.1724560
Hung, H. M. J., & O’Neill, R. T. (2003). Utilities of the P-value distribution associated with effect size in clinical trials. Biometrical Journal, 45(6), 659–669.
Kesselheim, A. S., Hwang, T. J., & Franklin, J. M. (2015). Two decades of new drug development for central nervous system disorders. Nature Reviews Drug Discovery, 14(12), 815–816.
Lee, S. J., & Zelen, M. (2000). Clinical trials and sample size considerations: Another perspective. Statistical Science, 15(2), 95–110.
O’Neill, R. T. (1997). Secondary endpoints cannot be validly analyzed if the primary endpoint does not demonstrate clear statistical significance. Controlled Clinical Trials, 18(6), 550–556.
Pereira, T. V., Horwitz, R. I., & Ioannidis, J. P. A. (2012). Empirical evaluation of very large treatment effects of medical intervention. Journal of the American Medical Association, 308(16), 1676–1684.
Zuckerman, D. M., Jury, N. J., & Sicox, C. E. (2015). 21st century cures act and similar policy efforts: at what cost? British Medical Journal, 351, h6122.
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Chuang-Stein, C., Kirby, S. (2021). The Parallel Between Clinical Trials and Diagnostic Tests. In: Quantitative Decisions in Drug Development. Springer Series in Pharmaceutical Statistics. Springer, Cham. https://doi.org/10.1007/978-3-030-79731-7_4
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DOI: https://doi.org/10.1007/978-3-030-79731-7_4
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