We propose platform trials with outcome-adaptive randomization to efficiently select the foremost effective coronavirus disease 2019 (COVID-19) treatments. The worldwide spread of severe acute respiratory syndrome coronavirus 2 infections is alarming in its geographic scope and within the number of associated deaths. Further innovation within the design of the clinical trials would be a broader use of outcome-adaptive randomization, a selected adaptive design feature that potentially reduces the number of deaths or other adverse outcomes incurred during an attempt.
To favor groups with more advantageous outcomes, outcome-adaptive randomization updates the allocation proportions on the idea of observed outcomes from cumulatively enrolled persons so far. A simulation study wiped out 2016 showed that, relative to platform trials allowing early dropping of ineffective groups, a platform trial additionally incorporating outcome-adaptive randomization can cause an 18% decrease within the number of poor outcomes.
the benefits of updating randomization allocation are thus also measured in adverse outcomes averted. Should care providers’ equipment falter before the top of the study, they’ll be strongly tempted to ignore subsequent treatment assignments.
Approaches that mitigate this concern include masking investigators, when possible, and separating the roles of clinicians who are providing treatment from those assessing outcomes.
A collaborative effort will help us to widely implement the most effective treatments as quickly as possible, and with potentially more persons receiving the most effective treatments.