If you have an interest in being involved in this SIG, or have an interesting case study or experience to share please contact Fabian Tibaldi (email: fabian.tibaldi@gsk.com)
June 17th - 19th Amsterdam
Chair: Fabian Tibaldi
In this session, three talks will cover two critical statistical aspects of vaccine clinical development, namely correlates of protection and safety in these clinical trials. Two talks will showcase the latest progress in statistical methods that contribute to the identification and validation of correlates of protection, such as the modeling of immune response data for predicting vaccine efficacy. The third one will propose recommendations to improve and harmonize the collection of actively solicited adverse events (AEs) in prophylactic vaccine clinical trials. These are important topics that contribute to the advancement of the fight against infectious diseases.
Joint modeling of sparse immune response data and time-to-disease for prediction of vaccine efficacy. Greg Papageorgiou, Toufik Zahaf, Andrea Callegaro |
Application of causal inference methodology in evaluation correlates of risk
| Sanne Roels and Joris Menten |
Harmonizing the collection of solicited adverse events in prophylactic vaccine clinical trials
| Brigitte Cheuvart, Bart Spiessens, Roy van Heesbeen, Derchieh Hung, Coralie Andrade, Joanna Korejwo-Peyramond and Fernanda Tavares-Da-Silva. |
FDA Workshop 2022 in September https://ww2.amstat.org/meetings/biop/2022/
Instructors : TBC
Organizer : DJ Tang, SVP, Data Services dejun.tang@firmaclinical.com
COVID-19 pandemic has ignited a world-wide broad interest in development of vaccines. Because of biological nature, there are many unique statistical issues and challenges in vaccine clinical trials. Some examples include using immunogenicity to evaluate vaccine effects and consistency in manufacture; the rigorous large studies needed to demonstrate efficacy due to low incidence rate of disease; identifying and using biomarkers based on correlates of protection; stringent safety requirement because of broad administration to millions of healthy individuals; and application of innovative designs to speed up the development.
This half-day short course will provide an overview of issues and statistical methods for vaccine clinical trials. Following some general introduction of vaccine development, the course will cover topics for statistical methods in analysis of immunogenicity, efficacy, and safety. Unique features for vaccine trials such as non-inferiority design, lot consistency, correlate of protection, super superiority study, and handling of low incidence events, etc. will be discussed. Case examples from various vaccine programs will be presented.
Course outlines: 1) Introduction of vaccine development; 2) Statistical methods for immunogenicity including non-inferiority comparison, handling of missing data, issues for multi-valent vaccines, and lot consistency; 3) Statistical method for efficacy including conditional exact methods, adaptive dose range and seamless designs, and Bayesian methods; 4) Correlate of protection, modeling efficacy from immunogenicity markers; 5) Evaluation of safety.
The COVID-19 pandemic has had effects on multiple operational aspects of clinical trials. Three notable areas of impact are an increase in pandemic-related missing data, disruption to patient recruitment, and acceleration of decentralized trials with alternative methods to conduct patient follow up. In the area of missing data, operational metrics that are leading indicators of potential issues with the primary efficacy endpoints have been helpful. Work will be presented on sophisticated metrics that have been developed that can flag problems even for complex endpoints such as progression-free survival (PFS). These measures can be used as QTLs and/or in centralized study monitoring at a study level; and can be summarized to quantify risk at a portfolio level. In the area of patient recruitment, recruitment in complex trials is often facilitated by the use of stochastic models. The COVID-19 pandemic brought an additional layer of complexity to recruitment modelling: as countries went into shutdown, sponsors faced the dilemma of re-planning study delivery, re-balancing their portfolios “on the fly”. That translated into the need to adapt the recruitment model to account for effect of COVID-19. Work will be presented on an approach that blends Poisson-Gamma models, real-time recruitment data updates and epidemiological modelling of COVID-19 spread. In the area of decentralized trials, the use of alternative mechanisms to conduct patient follow-up, such as telemedicine and remote assessments, in addition to core clinic visits has been accelerated by the COVID-19 pandemic. Clinical trial sponsors had to react quickly to incorporate different methods for managing the conduct of ongoing clinical trials assuring the robust collection of safety and efficacy data. Lessons learned will be shared on how the impact of changes in study conduct were managed and consequences to the planned analyses.
The session will highlight real life experiences implementing Bayesian designs in proof of concept and pivotal COVID-19 vaccine trials. The speakers and discussant will reflect on the key methodological considerations, regulatory interactions, and operational challenges. Our first speaker will present a Bayesian sequential proof-of-concept (POC) trial to investigate the efficacy of Bacillus Calmette-Guérin (BCG) in providing protection against COVID-19 infections through ""trained immunity"". The main design consideration is to provide a framework to rapidly establish a POC on the vaccine efficacy of BCG under a constantly evolving incidence rate and in absence of prior efficacy data. The trial design is based on taking several interim looks and calculating the predictive power with the current cohort at each look. Decisions to stop the trial for futility or stopping enrollment for efficacy are made based on the last two predictive power computations.
Our second speaker will discuss the statistical aspects of the Pfizer Phase III Bayesian adaptive vaccine trial. This trial incorporates multiple interim analyses, each based on achieving a sufficiently high Bayesian posterior probability of vaccine efficacy. The trial also incorporates early stopping for futility based on Bayesian predictive probabilities. The talk will include key statistical aspects and regulatory challenges of the design.
Following the outbreak of COVID-19 around the world, several COVID-19 vaccine candidates are in development across various clinical trial stages. While historically it has taken years or even decades to develop safe and effective vaccines from discovery to licensure, the urgency of developing a vaccine for COVID-19 presents enormous challenges and opportunities. Pharmaceutical companies across the world have joined efforts to race against the clock in order to develop effective vaccines and treatments to control the pandemic and to stop the spread of the disease that is causing a high number of deaths worldwide. Statisticians have played an important leadership role in dealing with these challenges. In this session, key opinion leaders from industry, regulatory, and academia will share their stories on handling challenges and innovative approaches for COVID-19 vaccine development.
Confirmed speakers and discussants:
- Geert Molenberghs (Universiteit Hasselt & KU Leuven), “COVID-19: modeling, epidemiological, clinical and vaccine aspects”
- Scott Patterson (Sanofi-Pasteur) “Statistical Observations on SARS-CoV-2 Clinical Vaccine Development”
- Ye Yang (CBER. FDA) “Statistical Challenges in COVID-19 Vaccine Trials”
- Tsai-Lien Lin (CBER, FDA), discussant
More meetings, conferences and presentations in our Newsletter.
