# trialr: Bayesian Clinical Trial Designs in R and Stan

## TL;DR

The paper introduces the R package 'trialr' which implements Bayesian clinical trial methods in Stan, enabling flexible inference and visualization for phase I and II trials, including dose-finding and efficacy assessment.

## Contribution

It provides the first comprehensive R package integrating Bayesian trial designs with Stan, facilitating advanced analysis and visualization in clinical research.

## Key findings

- Implementation of three Bayesian methods: CRM, EffTox, and augmented binary.
- Enhanced inference and visualization capabilities using posterior samples.
- Encourages wider adoption of Bayesian methods in clinical trials.

## Abstract

This manuscript introduces an \proglang{R} package called \pkg{trialr} that implements a collection of clinical trial methods in \proglang{Stan} and \proglang{R}. In this article, we explore three methods in detail. The first is the continual reassessment method for conducting phase I dose-finding trials that seek a maximum tolerable dose. The second is EffTox, a dose-finding design that scrutinises doses by joint efficacy and toxicity outcomes. The third is the augmented binary method for modelling the probability of treatment success in phase II oncology trials with reference to repeated measures of continuous tumour size and binary indicators of treatment failure. We emphasise in this article the benefits that stem from having access to posterior samples, including flexible inference and powerful visualisation. We hope that this package encourages the use of Bayesian methods in clinical trials.

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Source: https://tomesphere.com/paper/1907.00161