Re-thinking non-inferiority: a practical trial design for optimising treatment duration

TL;DR

This paper introduces a flexible, multi-arm trial design that models the entire treatment duration-response curve to optimize treatment length, overcoming limitations of traditional non-inferiority trials.

Contribution

It proposes a novel trial design using fractional polynomials and splines to estimate the duration-response curve, reducing sample size needs and arbitrariness of margins.

Findings

Approximately 500 patients with 5-7 arms suffice for accurate curve estimation.

Fractional polynomials perform as well or better than spline methods.

The approach provides a comprehensive view of treatment duration effects.

Abstract

Background: trials to identify the minimal effective treatment duration are needed in different therapeutic areas, including bacterial infections, TB and Hepatitis--C. However, standard non-inferiority designs have several limitations, including arbitrariness of non-inferiority margins, choice of research arms and very large sample sizes. Methods: we recast the problem of finding an appropriate non-inferior treatment duration in terms of modelling the entire duration-response curve within a pre-specified range. We propose a multi-arm randomised trial design, allocating patients to different treatment durations. We use fractional polynomials and spline-based methods to flexibly model the duration-response curve. We compare different methods in terms of a scaled version of the area between true and estimated prediction curves. We evaluate sensitivity to key design parameters, including sample size, number and position of arms. Results: a total sample size of $\sim 500$ patients divided into a moderate number of equidistant arms (5-7) is sufficient to estimate the duration-response curve within a $5\%$ error margin in $95\%$ of the simulations. Fractional polynomials provide similar or better results than spline-based methods in most scenarios. Conclusions: our proposed practical randomised trial design is an alternative to standard non-inferiority designs, avoiding many of their limitations, and yet being fairly robust to different possible duration-response curves. The trial outcome is the whole duration-response curve, which could be used by clinicians and policy makers to make informed decisions, facilitating a move away from a forced binary hypothesis testing paradigm.