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Adapting clinical trial design to maintain meaningful outcomes during a multicenter asthma trial in the precision medicine era
- Sorkness, Christine A;
- King, Tonya S;
- Dyer, Anne-Marie;
- Chinchilli, Vernon M;
- Mauger, David T;
- Krishnan, Jerry A;
- Blake, Kathryn;
- Castro, Mario;
- Covar, Ronina;
- Israel, Elliot;
- Kraft, Monica;
- Lang, Jason E;
- Lugogo, Njira;
- Peters, Stephen P;
- Wechsler, Michael E;
- Wenzel, Sally E;
- Lazarus, Stephen C;
- “AsthmaNet”, On behalf of the National Heart Lung and Blood Institute's
- et al.
Published Web Location
https://doi.org/10.1016/j.cct.2018.12.012Abstract
Precision medicine is expected to impact the care of people with asthma, given its high disease prevalence, heterogeneity of pathophysiologic mechanisms, and consequent clinical phenotypes. A novel phenotype-stratified clinical trial conducted by the NHLBI AsthmaNet Consortium, titled Steroids in Eosinophil Negative Asthma (SIENA), was a randomized, multicenter, clinical trial that prospectively stratified individuals according to their baseline level of sputum inflammation during a screening period. Two phenotypic strata were assigned based on an a priori defined extent of sputum eosinophilia (Eos Low versus Eos High). This article describes: the scientific premise for the trial design, including assumptions used for power calculations; modifications to the analysis plan implemented after the trial started due to a higher than expected prevalence of one phenotypic stratum which impacted the ability to accrue sufficient subjects within the planned budget and study period; investigator alternatives to address the strata imbalance weighing scientific impact and study feasibility; and the final modified SIENA study design and analysis plan. SIENA was successfully completed in a manner that maintained meaningful outcomes. We conclude with recommendations for incorporation of pre-specified contingency plans into phenotype-directed protocols, to address the potential for differences in observed compared to estimated prevalence of different phenotypes in a study population. These approaches can be applied to precision medicine trials for the future.
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