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The importance of randomization
Conclusive evidence from clinical studies about treatment efficacy requires an appropriate control group, usually requiring randomization to balance out factors. During fatal disease outbreaks, randomization may be difficult to conduct due to ethical concerns and challenging field conditions. Dodd et al. have now performed a meta-analysis of six clinical studies conducted during the West Africa Ebola virus disease outbreak. These authors examined whether statistical modeling of multiple cohorts from these studies would facilitate a reasonable comparator for experimental treatments in future clinical studies lacking a randomized control group. The meta-analysis revealed considerable heterogeneity in the different control groups, which was not removed after statistical modeling. This suggests that nonrandomized control group data cannot be used as a comparator.
Abstract
Recent Ebola virus disease outbreaks affirm the dire need for treatments with proven efficacy. Randomized controlled clinical trials remain the gold standard but, during disease outbreaks, may be difficult to conduct due to ethical concerns and challenging field conditions. In the absence of a randomized control group, statistical modeling to create a control group could be a possibility. Such a model-based reference control would only be credible if it had the same mortality risk as that of the experimental group in the absence of treatment. One way to test this counterfactual assumption is to evaluate whether reasonable similarity exists across nonrandomized control groups from different clinical studies, which might suggest that a future control group would be similarly homogeneous. We evaluated similarity across six clinical studies conducted during the 2013–2016 West Africa outbreak of Ebola virus disease. These studies evaluated favipiravir, the biologic ZMapp, the antimalarial drug amodiaquine, or administration of convalescent plasma or convalescent whole blood. We compared the nonrandomized control groups of these six studies comprising 1147 individuals infected with Ebola virus. We found considerable heterogeneity, which did not disappear after statistical modeling to adjust for prognostic variables. Mortality risk varied widely (31 to 66%) across the nonrandomized control arms of these six studies. Models adjusting for baseline covariates (age, sex, and cycle threshold, a proxy for viral load) failed to sufficiently recalibrate these studies and showed that heterogeneity remained. Our findings highlight concerns about making invalid conclusions when comparing nonrandomized control groups to cohorts receiving experimental treatments.
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