Quartagno, M;
Walker, AS;
Carpenter, J;
Phillips, P;
Parmar, M;
(2018)
Rethinking non-inferiority: a practical trial design for optimising treatment duration.
Clinical Trials
, 15
(5)
pp. 477-488.
10.1177/1740774518778027.
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Abstract
BACKGROUND: Trials to identify the minimal effective treatment duration are needed in different therapeutic areas, including bacterial infections, tuberculosis 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 call this a ‘Durations design’. 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 ~ 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. CONCLUSION: Our proposed practical randomised trial ‘Durations design’ shows promising performance in the estimation of the duration–response curve; subject to a pending careful investigation of its inferential properties, it provides a potential 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 may be used by clinicians and policymakers to make informed decisions, facilitating a move away from a forced binary hypothesis testing paradigm.
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