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Preparation and Analysis of In Vitro Three Dimensional Breast Carcinoma Surrogates

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Published on: May 9, 2016

Predictive probability of success using surrogate endpoints.

Gaelle Saint-Hilary¹, Valentine Barboux², Matthieu Pannaux²

¹Dipartimento di Scienze Matematiche (DISMA) Giuseppe Luigi Lagrange, Politecnico di Torino, Turin, Italy.

Statistics in Medicine

|December 15, 2018

Summary

This summary is machine-generated.

Predicting clinical trial success is crucial for drug development. This study introduces a new method using surrogate endpoints to forecast trial outcomes, improving early-stage decision-making.

Keywords:

Bayesian analysis decision-making predictions probability of success surrogate endpoints

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Area of Science:

Drug development and clinical trial methodology
Biostatistics and predictive modeling

Background:

Predictive probability of success is vital for drug development decisions.
Clinical trials often rely on surrogate endpoints in early stages, with limited data on clinical endpoints.

Purpose of the Study:

To propose a general, reliable methodology for predicting future clinical trial success using surrogate endpoints.
To effectively utilize all available evidence, including surrogate and clinical endpoint data.

Main Methods:

Development of a "surrogate prior" from past surrogate endpoint trial results.
Bayesian framework to combine surrogate prior with clinical endpoint data, if available.
Two methods proposed to handle discordance between surrogate prior and clinical endpoint data.

Main Results:

A comprehensive simulation study investigated the predictive methodology's behavior.
The methodology was applied to a drug development case in Multiple Sclerosis.

Conclusions:

The proposed methodology offers a robust approach to predict clinical trial success from surrogate endpoints.
This approach can accelerate drug development and improve early selection of promising candidates.