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Evaluating and utilizing probability of study success in clinical development.
Yanping Wang1, Haoda Fu, Pandurang Kulkarni
1Eli Lilly and Company, Indianapolis, IN 46285, USA. wang_yanping@lilly.com
Clinical Trials (London, England)
|March 9, 2013
Summary
Evaluating the probability of study success (PrSS) using available data aids decision-making in drug development. This analytical approach can improve the accuracy of critical Go/No-Go decisions, especially after early-stage trials.
Area of Science:
- Drug Development
- Clinical Trials
- Oncology Research
Background:
- Drug development is expensive, time-consuming, and high-risk.
- Informed decision-making is crucial for R&D.
- Analytical tools leverage data to assess risks and benefits.
Purpose of the Study:
- Demonstrate using data analytics to evaluate the probability of study success (PrSS).
- Showcase how PrSS can improve clinical development decisions in oncology.
- Utilize real oncology examples to illustrate the methodology.
Main Methods:
- Quantify PrSS using predictive power (average conditional power).
- Employ a two-step process: Bayesian modeling to derive treatment effect distribution and analytical/simulation-based PrSS evaluation.
- Synthesize relevant data using Bayesian modeling and stated assumptions.
Main Results:
- Applied the PrSS evaluation procedure to multiple oncology pipeline compounds.
- The analysis provided crucial insights for decision-making.
- PrSS was a key consideration in the evaluated cases.
Conclusions:
- PrSS, derived from available data, supports drug development decisions, especially Go/No-Go decisions post-proof-of-concept.
- Assumptions in treatment effect modeling (e.g., drug interactions, exchangeability) are context-specific.
- Recommend regular PrSS evaluation alongside routine clinical trial data analysis.
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