# Assurance for clinical trial design with normally distributed outcomes: eliciting uncertainty about variances

## TL;DR

This paper introduces a straightforward method for eliciting prior distributions for variance parameters in normally distributed outcomes, enhancing assurance calculations in clinical trial design, including multi-stage trials, with practical software tools.

## Contribution

It provides a novel, simple elicitation approach for variance priors and demonstrates their integration into assurance calculations for single and multi-stage clinical trials.

## Key findings

- The method effectively incorporates variance uncertainty into assurance calculations.
- Application to multi-stage trials improves decision-making based on smaller trial outcomes.
- Software implementation facilitates practical adoption of the proposed approach.

## Abstract

The assurance method is growing in popularity in clinical trial planning. The method involves eliciting a prior distribution for the treatment effect, and then calculating the probability that a proposed trial will produce a `successful' outcome. For normally distributed observations, uncertainty about the variance of the normal distribution also needs to be accounted for, but there is little guidance in the literature on how to elicit a distribution for a variance parameter. We present a simple elicitation method, and illustrate how the elicited distribution is incorporated within an assurance calculation. We also consider multi-stage trials, where a decision to proceed with a larger trial will follow from the outcome of a smaller trial; we illustrate the role of the elicted distribution in assessing the information provided by a proposed smaller trial. Free software is available for implementing our methods.