Date of Award

Spring 3-6-2019

Degree Type


Degree Name

PhD Health Sciences


Health and Medical Sciences


Deborah A. DeLuca, JD

Committee Member

Terrence F. Cahill, Ed.D.

Committee Member

Glenn Beamer, Ph.D.


meta-analysis, meta-regression, orphan drugs, clinical trials, regulatory success, regulatory approval


Background and Purpose of the Study: Developed an algorithm (AODI) for predicting probability of regulatory success (PRS) for new orphan drugs after phase II testing has been conductedwith the objective of providing a tool to improve drug portfolio decision-making.Methods: Examined 132 studies from recent publications (2005 onwards). Data on safety, efficacy, operational, market, and company characteristics were obtained from public sources. Meta-analysis and meta-regressions were used to provide an unbiased approach to assess overall predictability and to identify the most important individual predictors.Results: Found that a simple three-factor model (disease prevalence, clinical trial duration and clinical trial participation) had high specificity for predicting regulatory approval (success).Conclusion:smaller clinical trial participation, shorter clinical trials duration and lower rare disease prevalence were found to be highly associated with the Probability of Regulatory Success (PRS) of orphan drugs.