Translational clinical trials: an entropy-based approach to sample size

Abstract

Translational clinical trials are small studies of therapies emerging from the laboratory. These trials are essential for generating early evidence regarding the effects of treatment on specific targets in the disease pathway and for guiding the next studies to be done. The statistical properties of such studies have been neglected, in part, because they do not fit the well-known clinical trials developmental paradigm. This paper discusses the translational trial setting, and presents an information (entropy) based approach to understanding the properties and use of these trials. The combination of biological knowledge with a designed experiment (albeit a small one) is a powerful device for resolving much of the considerable uncertainty surrounding an emerging therapeutic concept. An approach to motivating the sample size for translational trials is presented.

Get full access to this article

View all access options for this article.

References

1. Eggermont A , Newell H . Translational research in clinical trials: the only way forward. Eur J Cancer 2001; 37: 1965-1965.

2. Minna JD , Gazdar AF . Translational research comes of age. Nat Med 1996; 2: 974–975.

3. Crowley WF, Jr , Thier , SO . The continuing dilemma in clinical investigation and the future of American health care: a system-wide problem requiring collaborative solutions. Acad Med 1996; 71: 1154–1163.

4. Rustgi AK . Translational research: what is it? Gastroent gology 1999; 116: 1285-1285.

5. Saijo N . Translational study in cancer research. Intern Med 2002; 41: 770–773.

6. Sugarman J , McKenna WG . Ethical hurdles for translational research. Radiat Res 2003; 160:1–4.

7. Marincola FM . Translational medicine: a two-way road. J Translational Med 2003; 1:1-1.

8. Fisher RA . Theory of statistical estimation. Proc Cambridge Philos Soc 1925; 22: 700–725.

9. Shannon CE . A mathematical theory of communication. Bell System Technical J 1948; 27: 379–3423; 623–656.

10.

10. Applebaum D . Probability and information. Cambridge: Cambridge University Press, 1996.

11.

11. Kullback S . Information theory and statistics. New York: John Wiley & Sons, 1959.

12.

12. O'Hagan A . Kendall's advanced theory of statistics. Vol. 2B, Bayesian inference. London: Edward Arnold, 1994.

13.

13. Kullback S , Liebler RA . On information and sufficiency. Annals Math Stat 1951; 22: 79–86.

14.

14. Bolland TW . The use of the expected-net-gain chart to illustrate various aspects of sampling and sample design. Am Statistician 1985; 39: 47–52.

15.

15. Lindley DV . On a measure of the information provided by an experiment. Annals Math Stat 1956; 27: 986–1005.

16.

16. Sebastiani P , Wynn HP . Maximum entropy sampling and optimal Bayesian design. JRSS B 2000; 62: 145–157.

17.

17. Wolfram S . The mathematica book, 4th edition. Champaign, IL: Wolfram Media/Cambridge University Press, 1999.

18.

18. Evans CH Jr , Ilstad ST , eds. Small Clinical Trials: Issues and Challenges. Washington, DC: National Academy Press, 2001.