To add or not to add a new treatment arm to a multiarm study: A decision‐theoretic framework

Abstract

Multiarm clinical trials, which compare several experimental treatments against control, are frequently recommended due to their efficiency gain. In practise, all potential treatments may not be ready to be tested in a phase II/III trial at the same time. It has become appealing to allow new treatment arms to be added into on‐going clinical trials using a “platform” trial approach. To the best of our knowledge, many aspects of when to add arms to an existing trial have not been explored in the literature. Most works on adding arm(s) assume that a new arm is opened whenever a new treatment becomes available. This strategy may prolong the overall duration of a study or cause reduction in marginal power for each hypothesis if the adaptation is not well accommodated. Within a two‐stage trial setting, we propose a decision‐theoretic framework to investigate when to add or not to add a new treatment arm based on the observed stage one treatment responses. To account for different prospect of multiarm studies, we define utility in two different ways; one for a trial that aims to maximise the number of rejected hypotheses; the other for a trial that would declare a success when at least one hypothesis is rejected from the study. Our framework shows that it is not always optimal to add a new treatment arm to an existing trial. We illustrate a case study by considering a completed trial on knee osteoarthritis.

Including non-concurrent control patients in the analysis of platform trials: is it worth it?

“…In order to minimise the impact caused by the presence of a trend, most researchers advocate the use of concurrent control data when making inference about the newly added treatment(s) [23][24][25]. Others stipulate that non-concurrent control data may be used when making inference about the newly added arm by adjusting for possible trends [26,27].…”

“…Several analysis approaches are considered. Within a two-stage setting where a new treatment is added at the end of stage one [25,28], we explore the impact of i) the timing of adding a new arm ii) the sample size of the new arm and iii) the magnitude of a linear or a step trend [15] on the inference about the newly added experimental treatment. We provide recommendations about under which conditions non-concurrent control data should be used.…”

Evaluating Bayesian adaptive randomization procedures with adaptive clip methods for multi-arm trials

“…For example include monetary values to reflect cost-effectiveness of the approach. A future work could be investigating such a selection strategy for implementing BAR in trials where there is no control arm and in platform trials, such as in the setting considered by Lee et al 37 One may also extend the strategy to compare different classes of adaptive randomization, where the goal of the randomization approach includes maintaining covariate balance, see Rosenberger and Lachin, 38 for example.…”

The Evolution of Master Protocol Clinical Trial Designs: A Systematic Literature Review

“…An earlier study on the same subject with slightly different assumptions but similar findings has been performed by Wason and Trippa. 277 In a recently published article, Lee et al 278 propose a decision-theoretic framework based on 2 utility functions: one that tries to maximize the number of rejected hypotheses and one that counts at least one rejected hypothesis as a trial success, to decide whether a new treatment arm should be added to a MAMS design. For a comparison of several MAMS designs with traditionally used trial designs in the context of an epidemic, such as Ebola, please refer to Brueckner et al 279…”