Bayesian adaptive N-of-1 trials for estimating population and individual treatment effects

TL;DR

This paper introduces a new adaptive design algorithm for N-of-1 trials that efficiently estimates treatment effects at both individual and population levels using Laplace approximations, improving treatment selection.

Contribution

The paper presents a novel Laplace-based adaptive design algorithm for N-of-1 trials, enhancing efficiency in treatment effect estimation and treatment selection.

Findings

The method outperforms multi-armed bandit and randomized designs in identifying optimal treatments.

It provides accurate estimates of individual and population treatment effects.

The approach is computationally efficient and effective in real and simulated trials.

Abstract

This article proposes a novel adaptive design algorithm that can be used to find optimal treatment allocations in N-of-1 clinical trials. This new methodology uses two Laplace approximations to provide a computationally efficient estimate of population and individual random effects within a repeated measures, adaptive design framework. Given the efficiency of this approach, it is also adopted for treatment selection to target the collection of data for the precise estimation of treatment effects. To evaluate this approach, we consider both a simulated and motivating N-of-1 clinical trial from the literature. For each trial, our methods were compared to the multi-armed bandit approach and a randomised N-of-1 trial design in terms of identifying the best treatment for each patient and the information gained about the model parameters. The results show that our new approach selects designs that are highly efficient in achieving each of these objectives. As such, we propose our Laplace-based algorithm as an efficient approach for designing adaptive N-of-1 trials.