A Tutorial on Net Benefit Regression for Real-World Cost-Effectiveness Analysis Using Censored Data from Randomized or Observational Studies

HIGHLIGHTS

We illustrate the steps involved in carrying out cost-effectiveness analysis using net benefit regressions with possibly censored demo data by providing step-by-step guidance and code applied to a data set.We demonstrate the importance of these new methods by illustrating how naïve methods for handling censoring can lead to biased cost-effectiveness results.

An open-label, non-randomized study investigating the safety and efficacy of smallpox vaccine, LC16, as post-exposure prophylaxis for mpox

Mpox is an acute exanthematous disease caused by the monkeypox virus. Since May 2022, it has spread as a community-acquired infection, mainly in Europe and the United States, and urgent measures to prevent this infection were also required in Japan. In this study, we investigated the post-exposure prophylaxis of mpox and safety after inoculating the smallpox vaccine. Participants in close contact with patients with mpox were inoculated with "Freeze-dried cell culture Smallpox Vaccine LC16," within 14 days after close contact. Six cases were registered, and all the participants were inoculated. No mpox symptoms or related complications were observed in the participants for 21 days after the close contact. Adverse events due to inoculation, such as rash, fever, lymphadenopathy, and local reaction at the inoculation site (comprising erythema, swelling, induration, and pain) were observed in the participants; however, all inoculation-related events were non-severe and non-serious, and the participants recovered during the 28-day observation period. The findings of this study suggest that inoculation with LC16 is an effective post-exposure prophylaxis in individuals who had close contact with patients with mpox. Further large-scale studies are warranted to validate these findings.

The design and analysis of non-randomized studies: a case study of off-label use of hydroxychloroquine in the COVID-19 pandemic

Introduction: The COVID-19 pandemic has prompted researchers to conduct non-randomized studies in an effort to find an off-label drug that can effectively combat the virus and its effects. While these studies can expedite the drug approval process, researchers must carefully design and analyze such studies in order to perform rigorous science that is reproducible and credible. This article focuses on several key design and analysis considerations that can improve the scientific rigor of non-randomized studies of off-label drugs. Areas covered: The aim of this article is to provide an overview of best approaches that should be considered for non-randomized studies on off-label drugs. We discuss these approaches in detail and use a non-randomized study by Rivera et al. in Cancer Discovery as an example of methods that have been undertaken for COVID-19. Expert opinion: While non-randomized studies are inherently biased, they may be unavoidable in situations such as the COVID-19 pandemic, where researchers need to find an effective treatment quickly. We believe that a well-formed experimental design, high-quality data collection, and a well-thought-out statistical and data analysis plan are sufficient to produce rigorous and credible results for making an optimal decision.