Algorithmic benchmark modulation: A novel method to develop success rates for clinical studies.

Area of Science:

• Pharmaceutical industry
• Clinical trial management
• Decision analysis

Background:

• Accurate Probability of Technical Success (PTS) is crucial for pharmaceutical decision-making.
• Current methods for estimating PTS include power calculations, assurance calculations, and industry benchmarks, often with subjective adjustments.
• AstraZeneca utilized both assurance calculations and industry benchmarks, incorporating subjective modulations.

Purpose of the Study:

• To introduce and validate a novel, structured algorithm for modulating Probability of Technical Success (PTS) values.
• To enhance the consistency and efficiency of PTS assessments in pivotal pharmaceutical studies.
• To provide a tool that assists the pharmaceutical industry in addressing challenges in PTS estimation.

Main Methods:

• Development of a simple algorithm based on a comprehensive set of multiple-choice questions for PTS modulation.
• The algorithm structures issues historically considered during subjective modulations.
• Validation involved a set of 57 phase 3 PTS assessments.

Main Results:

• AstraZeneca's historical PTS estimations were found to be reasonably accurate based on 57 phase 3 assessments.
• A strong correlation was observed between subjective modulations and the novel modulation algorithm.
• The findings provide confidence in the validity of the new PTS modulation method.

Conclusions:

• The PTS modulation algorithm addresses limitations of traditional assurance calculations and unmodulated benchmarks, such as selection biases and difficulty in modeling various factors.
• It allows for the accommodation of project-specific considerations, unlike generic industry benchmarks.
• This methodology enables more consistent PTS assessments with reduced effort for pivotal studies.