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Related Experiment Video

Updated: Jul 10, 2026

Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents

Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents

Published on: September 10, 2018

Parameter solicitation for planning cost effectiveness studies with dichotomous outcomes.

M McIntosh¹, S Ramsey, K Berry

¹Cancer Prevention Research Program, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA. mcintosh@biostat.washington.edu

Health Economics

|February 17, 2001

Summary

This study introduces a new method for planning cost-effectiveness randomized clinical trials (RCTs). It simplifies specifying the relationship between costs and effectiveness, improving study power calculations for the incremental cost-effectiveness ratio (ICER).

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Area of Science:

Health Economics
Biostatistics
Clinical Trial Design

Background:

Randomized clinical trials (RCTs) often include economic endpoints, summarized by the incremental cost-effectiveness ratio (ICER).
Planning RCTs with economic data requires specifying the correlation between costs and effectiveness, which can be unintuitive and inaccurate.
Mis-specifying this correlation significantly impacts study power, potentially leading to underpowered or overpowered trials.

Purpose of the Study:

To present a novel method for planning cost-effectiveness RCTs that avoids direct specification of the correlation between costs and effectiveness.
To simplify parameter specifications and enable realistic data generation for power simulations.
To improve the accuracy of sample size calculations for cost-effectiveness studies.

Main Methods:

Utilizing a mixture model to describe the association between costs and effectiveness when clinical outcomes are dichotomous.
Developing intuitive parameter specifications based on the mixture model.
Applying the Fieller's theorem method (FTM) for calculating confidence intervals and testing hypotheses about the ICER.
Demonstrating the method with data from a published clinical trial.

Main Results:

The proposed mixture model approach simplifies the specification of cost-effectiveness relationships in RCT planning.
This method allows for more accurate power calculations and sample size determination.
The approach facilitates the generation of realistic raw data for simulation-based power evaluations.

Conclusions:

The mixture model offers a more intuitive and accurate way to plan cost-effectiveness RCTs.
This method enhances the reliability of sample size calculations, crucial for efficient trial design.
The findings support improved methodologies for economic evaluations within clinical research.