Safety Study of Salmeterol in Asthma in Adults

Severe Adult Asthmas: Integrating Clinical Features, Biology, and Therapeutics to Improve Outcomes

Evaluation and effective management of asthma, and in particular severe asthma, remains at the core of pulmonary practice. Over the last 20-30 years, there has been increasing appreciation that "severe asthma" encompasses multiple different subgroups or phenotypes, each with differing presentations. Using clinical phenotyping, in combination with rapidly advancing molecular tools and targeted monoclonal antibodies (human knockouts), the understanding of these phenotypes, and our ability to treat them, have greatly advanced. Type-2 (T2)-high and -low severe asthmas are now easily identified. Fractional exhaled nitric oxide and blood eosinophil counts can be routinely employed in clinical settings to identify these phenotypes and predict responses to specific therapies, meeting the initial goals of precision medicine. Integration of molecular signals, biomarkers, and clinical responses to targeted therapies has enabled identification of critical molecular pathways and, in certain phenotypes, advanced them to near-endotype status. Despite these advances, little guidance is available to determine which class of biologic is appropriate for a given patient, and current "breakthrough" therapies remain expensive and even inaccessible to many patients. Many of the most severe asthmas, with and without T2-biomarker elevations, remain poorly understood and treated. Nevertheless, conceptual understanding of "the severe asthmas" has evolved dramatically in a mere 25 years, leading to dramatic improvements in the lives of many.

Are restricted mean survival time methods especially useful for noninferiority trials?

BACKGROUND

Restricted mean survival time methods compare the areas under the Kaplan-Meier curves up to a time τ for the control and experimental treatments. Extraordinary claims have been made about the benefits (in terms of dramatically smaller required sample sizes) when using restricted mean survival time methods as compared to proportional hazards methods for analyzing noninferiority trials, even when the true survival distributions satisfy proportional hazardss.

METHODS

Through some limited simulations and asymptotic power calculations, the authors compare the operating characteristics of restricted mean survival time and proportional hazards methods for analyzing both noninferiority and superiority trials under proportional hazardss to understand what relative power benefits there are when using restricted mean survival time methods for noninferiority testing.

RESULTS

In the setting of low-event rates, very large targeted noninferiority margins, and limited follow-up past τ, restricted mean survival time methods have more power than proportional hazards methods. For superiority testing, proportional hazards methods have more power. This is not a small-sample phenomenon but requires a low-event rate and a large noninferiority margin.

CONCLUSION

Although there are special settings where restricted mean survival time methods have a power advantage over proportional hazards methods for testing noninferiority, the larger issue in these settings is defining appropriate noninferiority margins. We find the restricted mean survival time methods lacking in these regards.

Assessing Clinical Equivalence in Oncology Biosimilar Trials With Time-to-Event Outcomes

A typical biosimilar study in oncology uses the overall response evaluated at a specific time point as the primary endpoint, which is generally acceptable regulatorily, to assess clinical equivalence between a biosimilar and its reference product. The standard primary endpoint for evaluating an anticancer therapy, progression-free or overall survival would be a secondary endpoint in a biosimilar trial. With a conventional analytic procedure via, for example, hazard ratio to quantify the group difference, it is difficult and challenging to assess clinical equivalence with respect to progression-free or overall survival because the study generally has a limited number of clinical events observed in the study. In this article, we show that an alternative procedure based on the restricted mean survival time, which has been discussed extensively for design and analysis of a general equivalence study, is readily applicable to a biosimilar trial. Unlike the hazard ratio, this procedure provides a clinically interpretable estimate for assessing equivalence. Using the restricted mean survival time as a summary measure of the survival curve will enhance better treatment decision making in adopting a biosimilar product over the reference product.