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Related Experiment Video

Updated: May 10, 2026

A Clinical Trial Assessing the Safety, Efficacy, and Delivery of Olive-Oil-Based Three-Chamber Bags for Parenteral Nutrition

A Clinical Trial Assessing the Safety, Efficacy, and Delivery of Olive-Oil-Based Three-Chamber Bags for Parenteral Nutrition

Published on: September 20, 2019

Bayesian sample size determination for a clinical trial with correlated continuous and binary outcomes.

James D Stamey¹, Fanni Natanegara, John W Seaman

¹Department of Statistical Science, Baylor University, Waco, TX 76798, USA. james_stamey@baylor.edu

Journal of Biopharmaceutical Statistics

|June 22, 2013

Summary

This study introduces a Bayesian method for calculating sample sizes in clinical trials, considering both efficacy and safety outcomes. High correlation between these outcomes can significantly reduce the required total sample size.

Related Experiment Videos

Last Updated: May 10, 2026

A Clinical Trial Assessing the Safety, Efficacy, and Delivery of Olive-Oil-Based Three-Chamber Bags for Parenteral Nutrition

A Clinical Trial Assessing the Safety, Efficacy, and Delivery of Olive-Oil-Based Three-Chamber Bags for Parenteral Nutrition

Published on: September 20, 2019

Area of Science:

Clinical Trials Methodology
Biostatistics
Statistical Modeling

Background:

Clinical trials frequently collect multiple outcomes to evaluate both treatment effectiveness and patient safety.
Simultaneous assessment of efficacy and safety is crucial for comprehensive trial evaluation.
Determining optimal sample size is essential for trial efficiency and statistical power.

Purpose of the Study:

To develop a Bayesian sample size determination procedure for regression models with continuous efficacy and binary safety variables.
To account for the correlation between efficacy and safety variables in sample size calculations.
To demonstrate potential sample size savings through simulation.

Main Methods:

A simulation-based Bayesian procedure was developed for sample size determination.
The methodology incorporates a regression model analyzing a continuous efficacy variable and a binary safety variable.
The model explicitly accounts for the correlation between these two outcome variables.

Main Results:

The developed Bayesian procedure enables sample size calculation for dual-outcome trials.
The procedure effectively models the relationship between continuous efficacy and binary safety measures.
Simulation results indicate substantial sample size reductions are achievable when efficacy and safety variables are highly correlated.

Conclusions:

The proposed Bayesian approach provides an efficient method for sample size determination in clinical trials with multiple outcomes.
Accounting for the correlation between efficacy and safety variables can lead to significant resource savings.
This methodology supports optimized clinical trial design by balancing the need for robust statistical evidence with efficient resource allocation.