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An R-Based Landscape Validation of a Competing Risk Model

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Published on: September 16, 2022

A C++ program to calculate sample sizes for cost-effectiveness trials in a Bayesian framework.

Shah-Jalal Sarker¹, Anne Whitehead, Iftekhar Khan

¹Centre for Experimental Cancer Medicine, Barts Cancer Institute, Queen Mary, University of London, UK. s.sarker@qmul.ac.uk

Computer Methods and Programs in Biomedicine

|February 13, 2013

Summary

This summary is machine-generated.

This study introduces a C++ program for Bayesian sample size calculations in cost-effectiveness (CEA) and efficacy clinical trials. The software addresses a gap in readily available tools for these complex analyses.

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Area of Science:

Biostatistics
Health Economics
Clinical Trial Design

Background:

Cost-Effectiveness Analysis (CEA) is crucial in clinical trials.
Formal sample size calculations for CEA trials are uncommon.
Lack of accessible software, especially for Bayesian methods, hinders sample size determination.

Purpose of the Study:

To present a C++ program for Bayesian sample size estimation in cost-effectiveness clinical trials.
To provide a tool for calculating sample sizes for both cost-effectiveness and efficacy trials.
To facilitate complex sample size calculations in a Bayesian framework.

Main Methods:

Development of a C++ program utilizing NAG library functions.
Implementation of a Bayesian approach for sample size calculation, based on O'Hagan and Stevens.
Inclusion of capabilities for various willingness-to-pay thresholds and cost-effect correlations.

Main Results:

The program enables sample size estimation for cost-effectiveness and efficacy trials.
It supports calculations under diverse assumptions regarding cost and effect correlations.
The software can also yield frequentist sample sizes under specific prior conditions.

Conclusions:

The developed C++ program provides a valuable tool for sample size calculations in clinical trials.
It specifically addresses the need for Bayesian sample size estimation in cost-effectiveness studies.
The program is user-friendly, runs on Windows, and offers short computation times.