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There seems to be growing interest in the scientific and ethical merits of using an adaptive or platform design for clinical trials, particularly in the setting of large scale #COVID19 international collaboration. Are there any drawbacks?
It depends on the stopping rules, but halting poorly performing arms can bias the summary estimate away from the null. This bias can be magnified with publication bias in systematic reviews as described in @JAMA_current here… #statstwitter
The problem is some variation is random, so unusual benefits or harms could be due to chance alone. The idea of "regression to the mean" is where unusual results become less unusual over time as the effect of random outliers is diminished over time.…
During a pandemic, there are additional unique challenges. A vaccine may have a waning effect over time due to waning antibody, so lower efficacy after an interim analysis could be real or "regression to the mean," but how can we know one way or the other? #epitwitter #MedTwitter
Similarly, what about growing undetected immunity in the community (non-vaccine effects)? This is a limitation w/ platform trials where the same control group is used for multiple comparisons among different populations. See also, "network" meta-analysis…
Another limitation with adaptive trials in a pandemic is the lower certainty for sample size calculation inferences in a dynamic setting (while wasting p). For instance, what happens if the incidence suddenly changes in the population? #statsepi #covid_19 #research #methods
In summary, harmonizing trials can be helpful, but there are many considerations to keep in mind. Sometimes it is better to keep it simple when designing a trial. When interpreting results, we need to consider individual trials, meta-results, & Bayes, etc.…
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