Related Experiment Video
Updated: Aug 6, 2026
08:58
Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
Published on: October 17, 2025
Industrialization of Bayesian decision-making for proof-of-commercial-concept study designs
Fan Wu1, Pascal Minini2, Gang Han1
1Evidence Generation and Decision Science, Sanofi, Morristown, NJ, USA.
Journal of Biopharmaceutical Statistics
|July 21, 2026
Summary
HERALD, a Bayesian decision-making framework, predicts Phase 3 success at the proof-of-commercial-concept stage. Industrializing HERALD streamlines decision-making and enhances cross-functional team communication for drug development.
Area of Science:
- Clinical trial design
- Pharmaceutical decision-making
- Bayesian statistics
Background:
- The Holistic Evolving ReAssessment-Leveraged Decision-making (HERALD) framework utilizes Bayesian principles.
- HERALD focuses on predicting Phase 3 clinical trial success.
- This framework is crucial for optimizing drug development strategies.
Purpose of the Study:
- To describe the industrialization of the HERALD framework at the proof-of-commercial-concept (POCC) study design stage.
- To demonstrate how HERALD integrates Phase 3 assumptions with POCC data for decision-making.
- To enhance the efficiency of communication within cross-functional teams during study design.
Main Methods:
- Development of specialized statistical software for HERALD implementation.
- Standardization of governance presentations for clear communication.
- Consolidation of diverse implementation examples for practical application.
- Implementation of role-tailored training programs for effective user engagement.
Main Results:
- HERALD provides decision boundaries ensuring a high probability of Phase 3 success.
- The framework has been widely adopted and endorsed across therapeutic areas at Sanofi.
- Industrialization has streamlined the discussion of decision-making criteria.
- Empowered statisticians and non-statisticians to communicate design options more effectively.
Conclusions:
- Industrialized HERALD enhances decision-making efficiency in early-stage drug development.
- Standardized processes and training facilitate better communication and collaboration.
- The framework supports robust decision-making by linking early-stage data to late-stage success probabilities.
Related Concept Videos
01:15Study Design in Statistics
A study design is a set of techniques that allow a researcher to collect and analyze data from different variables defined for a specific research problem. Statistics is commonly for effective study design and more robust experiments,
Does aspirin reduce the risk of heart attacks? Is one brand of fertilizer more effective at growing roses than another? Is fatigue as dangerous to a driver as the influence of alcohol? Questions like these are answered using randomized experiments with proper...
Does aspirin reduce the risk of heart attacks? Is one brand of fertilizer more effective at growing roses than another? Is fatigue as dangerous to a driver as the influence of alcohol? Questions like these are answered using randomized experiments with proper...
01:09Decision Making: P-value Method
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim is also stated. These statements can act as null and alternative hypotheses: a null hypothesis would be a neutral statement while the alternative hypothesis can have a...
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim is also stated. These statements can act as null and alternative hypotheses: a null hypothesis would be a neutral statement while the alternative hypothesis can have a...
01:14Decision Making: Traditional Method
The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
01:16Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance, comparing...
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance, comparing...
01:23Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...
01:16Experimental Designs
An experimental design is a systematic process that allows researchers to evaluate the relationship between dependent and independent variables. There are three widely used types of experimental design - pre-experimental design, true experimental design, and quasi-experimental design. In pre-experimental design, the researcher compares the data before and after some interventions or treatments. The true-experimental design has more than one purposefully created group, a commonly measured...