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Jointly assessing multiple endpoints in pilot and feasibility studies.

Robert N Montgomery¹, Amy E Bodde², Eric D Vidoni³

¹Department of Biostatistics and Data Science, University of Kansas Medical Center, Kansas City, USA.

Statistical Methods in Medical Research

|February 12, 2025

Summary

This summary is machine-generated.

This study introduces a novel Joint Feasibility Space (JFS) method for pilot and feasibility studies. This approach improves the assessment of future trial feasibility by analyzing multiple endpoints more efficiently.

Keywords:

Bayesian analysis Pilot studies feasibility sample size

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Area of Science:

Biostatistics
Clinical Trial Design
Health Services Research

Background:

Pilot and feasibility studies are crucial for determining the viability of definitive trials.
Current methods for assessing feasibility endpoints are often simplistic and do not fully consider future trial requirements.

Purpose of the Study:

To propose and evaluate a new statistical method for analyzing feasibility outcomes in pilot studies.
To enhance the assessment of future trial feasibility by incorporating multiple endpoints and future study designs.

Main Methods:

Development of a Joint Feasibility Space (JFS) to define the combination of outcomes indicating future trial feasibility.
Estimation of the probability of achieving joint feasibility using Bayesian methods.
Simulation studies to create a decision rule based on frequentist operating characteristics and comparison with existing methods.

Main Results:

The proposed method effectively incorporates relationships between multiple feasibility endpoints.
The new approach demonstrates comparable performance to existing methods for single endpoints.
The method shows increased efficiency when analyzing multiple feasibility endpoints compared to general-purpose methods.

Conclusions:

Feasibility endpoints are central to pilot and feasibility studies and warrant more rigorous analysis.
The developed Joint Feasibility Space (JFS) method provides a more effective and efficient approach for assessing the feasibility of future clinical trials.