Creative and innovative statistics in clinical research and development.
OBJECTIVE
The aim of this paper is to show that even in a highly regulated area such as clinical research and development in pharmaceutical industry, there are needs and ample opportunities for statisticians and other medical informatics professionals to further creatively develop and implement methods in order to support the collection, analysis and interpretation of clinical data.


METHODS
The recently published "Critical Path" initiative of the US Food and Drug Administration discusses the decline in new drug submissions in the last decade and illustrates potential causes in the present clinical development process. Areas where statisticians can and have begun to look for new innovative ways to overcome these shortcomings are presented and examples of such novel approaches that have been developed by statistical methodologists in the pharmaceutical industry together with statisticians in academia are given.


RESULTS
In Early Development, i.e., in the first studies in man with a new compound, a combination of Bayesian methods and modeling approaches is particularly promising to increase the efficiency of decision making whereas in later phases (IIb and III) a marriage of modeling and classical frequentist approaches together with novel adaptive designs is expected help to chose the right dose regimen and to perform the trials more efficiently in reduced time.


CONCLUSIONS
The combination of known statistical methods and thinking and the development of new approaches are in line with the present paradigm of "learning and confirming" in regulated clinical development while increasing the efficiency of both.
Maurer
W
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Biostatistics and Statistical Reporting, Novartis Pharma AG, Basel, Switzerland. willi.maurer@pharma.novartis.com
D001499:Bayes Theorem; D001699:Biometry; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data*; D003657:Decision Making; D017277:Drug Approval / Q000706:statistics & numerical data*; D004341:Drug Evaluation / Q000706:statistics & numerical data*; D004345:Drug Industry; D006801:Humans; D015233:Models, Statistical; D014481:United States; D014486:United States Food and Drug Administration