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A framework for considering the risk‐benefit trade‐off in designing noninferiority trials using composite outcome approaches
Author(s) -
Montepiedra Grace,
Ramchandani Ritesh,
Miyahara Sachiko,
Kim Soyeon
Publication year - 2020
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.8777
Subject(s) - tolerability , outcome (game theory) , regimen , margin (machine learning) , clinical trial , medicine , statistics , computer science , mathematics , adverse effect , machine learning , mathematical economics
When a new treatment regimen is expected to have comparable or slightly worse efficacy to that of the control regimen but has benefits in other domains such as safety and tolerability, a noninferiority (NI) trial may be appropriate but is fraught with difficulty in justifying an acceptable NI margin that is based on both clinical and statistical input. To overcome this, we propose to utilize composite risk‐benefit outcomes that combine elements from domains of importance (eg, efficacy, safety, and tolerability). The composite outcome itself may be analyzed using a superiority framework, or it can be used as a tool at the design stage of a NI trial for selecting an NI margin for efficacy that balances changes in risks and benefits. In the latter case, the choice of NI margin may be based on a novel quantity called the maximum allowable decrease in efficacy (MADE), defined as the marginal difference in efficacy between arms that would yield a null treatment effect for the composite outcome given an assumed distribution for the composite outcome. We observe that MADE: (1) is larger when the safety improvement for the experimental arm is larger, (2) depends on the association between the efficacy and safety outcomes, and (3) depends on the control arm efficacy rate. We use a numerical example for power comparisons between a superiority test for the composite outcome vs a noninferiority test for efficacy using the MADE as the NI margin, and apply the methods to a TB treatment trial.

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