The Parallel Between Clinical Trials and Diagnostic Tests

  • Christy Chuang-Stein
  • Simon Kirby
Part of the Springer Series in Pharmaceutical Statistics book series (SSPS)


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. 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.


Treatment Effect PPVPositive Predictive Value Success Probability Central Nervous System Drug Replication Probability 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Chuang-Stein, C. (2006). Sample size and the probability of a successful trial. Pharmaceutical Statistics, 5(4), 305–309.CrossRefGoogle Scholar
  2. Chuang-Stein, C., & Kirby, S. (2014). The shrinking or disappearing observed treatment effect. Pharmaceutical Statistics, 13(5), 277–280.CrossRefGoogle Scholar
  3. 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.MathSciNetCrossRefGoogle Scholar
  4. 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.CrossRefGoogle Scholar
  5. Lee, S. J., & Zelen, M. (2000). Clinical trials and sample size considerations: Another perspective. Statistical Science, 15(2), 95–110.Google Scholar
  6. 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.CrossRefGoogle Scholar
  7. 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.CrossRefGoogle Scholar
  8. 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.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Christy Chuang-Stein
    • 1
  • Simon Kirby
    • 2
  1. 1.KalamazooUSA
  2. 2.Pfizer Statistical Research & Consulting CenterCambridgeUK

Personalised recommendations