A Bayesian approach to sequential analysis in post-licensure vaccine safety surveillance

Navigating challenges in pediatric trial conduct: integrating bayesian sequential design with semiparametric elicitation for handling primary and secondary endpoints

BACKGROUND

This study presents a Bayesian Adaptive Semiparametric approach designed to address the challenges of pediatric randomized controlled trials (RCTs). The study focuses on efficiently handling primary and secondary endpoints, a critical aspect often overlooked in pediatric trials. This methodology is particularly pertinent in scenarios where sparse or conflicting prior data are present, a common occurrence in pediatric research, particularly for rare diseases or conditions.

METHOD

Our approach considers Bayesian adaptive design, enhanced with B-Spline Semiparametric priors, allowing for the dynamic updating of priors with ongoing data. This improves the efficiency and accuracy of the treatment effect estimation. The Semiparametric prior inherent flexibility makes it suitable for pediatric populations, where responses to treatment can be highly variable. The design operative characteristics were assessed through a simulation study, motivated by the real-world case of the REnal SCarring Urinary infEction Trial (RESCUE).

RESULT

We demonstrate that Semiparametric prior parametrization exhibits an improved tendency to correctly declare the treatment effect at the study conclusion, even if recruitment challenges, uncertainty, and prior-data conflict arise. Moreover, the Semiparametric prior design demonstrates an improved ability in truly stopping for futility, with this tendency varying with the sample size and discontinuation rates. Approaches based on Parametric priors are more effective in detecting treatment efficacy during interim assessments, particularly with larger sample sizes.

CONCLUSION

Our findings indicate that these methods are especially effective in managing the complexities of pediatric trials, where prior data may be limited or contradictory. The flexibility of Semiparametric prior design in incorporating new evidence proves advantageous in addressing recruitment challenges and making informed decisions with restricted data.

Bayesian safety surveillance with adaptive bias correction

Postmarket safety surveillance is an integral part of mass vaccination programs. Typically relying on sequential analysis of real-world health data as they accrue, safety surveillance is challenged by sequential multiple testing and by biases induced by residual confounding in observational data. The current standard approach based on the maximized sequential probability ratio test (MaxSPRT) fails to satisfactorily address these practical challenges and it remains a rigid framework that requires prespecification of the surveillance schedule. We develop an alternative Bayesian surveillance procedure that addresses both aforementioned challenges using a more flexible framework. To mitigate bias, we jointly analyze a large set of negative control outcomes that are adverse events with no known association with the vaccines in order to inform an empirical bias distribution, which we then incorporate into estimating the effect of vaccine exposure on the adverse event of interest through a Bayesian hierarchical model. To address multiple testing and improve on flexibility, at each analysis timepoint, we update a posterior probability in favor of the alternative hypothesis that vaccination induces higher risks of adverse events, and then use it for sequential detection of safety signals. Through an empirical evaluation using six US observational healthcare databases covering more than 360 million patients, we benchmark the proposed procedure against MaxSPRT on testing errors and estimation accuracy, under two epidemiological designs, the historical comparator and the self-controlled case series. We demonstrate that our procedure substantially reduces Type 1 error rates, maintains high statistical power and fast signal detection, and provides considerably more accurate estimation than MaxSPRT. Given the extensiveness of the empirical study which yields more than 7 million sets of results, we present all results in a public R ShinyApp. As an effort to promote open science, we provide full implementation of our method in the open-source R package EvidenceSynthesis.

COVID-19 Vaccine in Pregnant and Lactating Women: A Review of Existing Evidence and Practice Guidelines

Coronavirus 2019 (COVID-19) has created a global pandemic that is devastating human lives, public healthcare systems, and global economies. Multiple effective and safe COVID-19 vaccines have been developed at an unprecedented speed due to the efforts of the scientific community, and collaboration between the federal government and pharmaceutical companies. However, the continued exclusion of pregnant and lactating women from the COVID anti-viral and vaccine trials has created the paradox of a lack of empirical evidence in a high-risk population. Based on the experience of similar prior vaccines, animal developmental and reproductive toxicology studies, and preliminary findings from human studies, various healthcare professional advisory committees (Advisory Committee on Immunization Practices, American College of Obstetricians and Gynecologists, Society for Maternal-Fetal Medicine, and Academy of Breastfeeding Medicine) have issued guidance supporting COVID-19 vaccination in pregnant and lactating women. In this article, we summarize the available data on the efficacy and safety profile of COVID-19 vaccination in pregnant and lactating women, review the challenges of vaccine hesitancy, and include recommendations for healthcare providers.

The coronavirus disease 2019 vaccine in pregnancy: risks, benefits, and recommendations

The coronavirus disease 2019 has caused over 2 million deaths worldwide, with over 412,000 deaths reported in Unites States. To date, at least 57,786 pregnant women in the United States have been infected, and 71 pregnant women have died. Although pregnant women are at higher risk of severe coronavirus disease 2019-related illness, clinical trials for the available vaccines excluded pregnant and lactating women. The safety and efficacy of the vaccines for pregnant women, the fetus, and the newborn remain unknown. A review of maternal and neonatal coronavirus disease 2019 morbidity and mortality data along with perinatal vaccine safety considerations are presented to assist providers with shared decision-making regarding vaccine administration for this group, including the healthcare worker who is pregnant, lactating, or considering pregnancy. The coronavirus disease 2019 vaccine should be offered to pregnant women after discussing the lack of safety data, with preferential administration for those at highest risk of severe infection, until safety and efficacy of these novel vaccines are validated.

Innovative trial designs and analyses for vaccine clinical development

In the past decades, the world has experienced several major virus outbreaks, e.g. West African Ebola outbreak, Zika virus in South America and most recently global coronavirus (COVID-19) pandemic. Many vaccines have been developed to prevent a variety of infectious diseases successfully. However, several infections have not been preventable so far, like COVID-19, which induces an immediate urgent need for effective vaccines. These emerging infectious diseases often pose unprecedent challenges for the global heath community as well as the conventional vaccine development paradigm. With a long and costly traditional vaccine development process, there are extensive needs in innovative vaccine trial designs and analyses, which aim to design more efficient vaccines trials. Featured with reduced development timeline, less resource consuming or improved estimate for the endpoints of interests, these more efficient trials bring effective medicine to target population in a faster and less costly way. In this paper, we will review a few vaccine trials equipped with adaptive design features, Bayesian designs that accommodate historical data borrowing, the master protocol strategy emerging during COVID-19 vaccine development, Real-World-Data (RWD) embedded trials and the correlate of protection framework and relevant research works. We will also discuss some statistical methodologies that improve the vaccine efficacy, safety and immunogenicity analyses. Innovative clinical trial designs and analyses, together with advanced research technologies and deeper understanding of the human immune system, are paving the way for the efficient development of new vaccines in the future.