A Simplified Bayesian Analysis Method for Vaccine Efficacy

Mapping Intimacies ◽

10.1101/2020.12.07.20244954 ◽

2020 ◽

Author(s):

Carlo Graziani

Keyword(s):

Bayesian Analysis ◽

Vaccine Efficacy ◽

Vaccine Trial ◽

Nuisance Parameter ◽

Phase Iii ◽

Analysis Method ◽

Final Phase ◽

Posterior Density ◽

One Dimensional ◽

Probability Densities

AbstractWe describe a simplified Bayesian analysis of vaccine trial data, in which a reparametrization of the Poisson likelihood leads to a factorization in which the protective vaccine efficacy VES and the nuisance parameter appear in different factors. As a consequence the posterior density acquires a factorized form, and marginalization over the nuisance parameter is trivial. Estimates of VES accordingly become a matter of simple manipulations of one-dimensional posterior probability densities. We demonstrate the method using the publically-released data on the efficacy of three vaccines agains SARS-CoV-2: the final Phase III data from the Pfizer/BioNTech and Moderna mRNA vaccines and the interim data released for the Sputnik V adenovirus-based vaccine.

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OC 8491 PREPVACC: A PHASE III, MAMS ADAPTIVE PROPHYLACTIC HIV VACCINE TRIAL WITH A SECOND RANDOMISATION TO COMPARE F/TAF WITH TDF/FTC PREP

BMJ Global Health ◽

10.1136/bmjgh-2019-edc.23 ◽

2019 ◽

Vol 4 (Suppl 3) ◽

pp. A10.1-A10

Author(s):

Sarah Joseph ◽

Pontiano Kaleebu ◽

Eugene Ruzagira ◽

Christian Holm Hansen ◽

Janet Seeley ◽

...

Keyword(s):

Vaccine Efficacy ◽

Vaccine Trial ◽

Final Analysis ◽

Standard Of Care ◽

Hiv Vaccine ◽

Phase Iii ◽

Hiv Incidence ◽

Multi Stage ◽

Adaptive Trial Design ◽

Env Protein

BackgroundThere remains an urgent need for a prophylactic HIV vaccine to control generalised epidemics. PrEP has demonstrated effectiveness of 86% and is recommended by WHO; uptake is generally high, but retention is disappointing in some settings. The EDCTP2 project PrEPVacc will assess the efficacy of two combination prophylactic vaccine regimens (DNA, MVA and Env protein/adjuvant) each compared to placebo and the proportion of infections averted by F/TAF in comparison to TDF/FTC. A Registration Cohort, recruiting HIV negative volunteers at risk of HIV will precede the trial.MethodsThe PrEPVacc partnership agreed that 70% vaccine efficacy had public health relevance. The trial uses nstage software for multi-arm, multi-stage designs (MAMS) and the averted infections ratio (AIR) methodology with participants randomised (i) 1:1:1 to active product or placebo (ii) 1:1 to TDF/FTC : F/TAF until week 26 (presumed peak immunogenicity). Access to PrEP in the Registration Cohort and after week 26 will be standard of care. HIV seroconversions occurring between weeks 0–26 will inform the PrEP analysis, incorporating HIV incidence amongst those who do not take up PrEP locally in the Registration Cohort. Seroconversions after week 26 will inform vaccine analyses.ResultsUp to 556 participants per group affords 92% power to detect vaccine efficacy of 70% at the final analysis, assuming incidence of 4/100-person years and 10% loss with 81% and 97% power to conclude that F/TAF can avert half or more of the infections prevented by TDF/FTC if effectiveness of TDF/FTC is 70% and 80%, respectively.ConclusionPrEPVacc adopts a pragmatic approach to uncertainties around HIV incidence in settings where PrEP is increasingly available. This innovative adaptive trial design uses validated software to determine vaccine efficacy and a novel methodology to evaluate a new PrEP agent, overcoming the challenge of demonstrating non-inferiority when adherence to TDF/FTC is high and the number of outcome events very low.

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Assessing the feasibility of Nipah vaccine efficacy trials based on previous outbreaks in Bangladesh

10.1101/2020.12.06.20244871 ◽

2020 ◽

Author(s):

Birgit Nikolay ◽

Marc Lipsitch ◽

Mahmudur Rahman ◽

Stephen P. Luby ◽

Henrik Salje ◽

...

Keyword(s):

Vaccine Efficacy ◽

Time Window ◽

Nipah Virus ◽

Vaccine Trial ◽

Cluster Randomized Trial ◽

Case Control ◽

Phase Iii ◽

Phase Iii Clinical Trials ◽

Ring Vaccination ◽

Cluster Randomized

AbstractBackgroundNipah virus (NiV) is an emerging, bat-borne pathogen that can be transmitted from person-to-person. Vaccines are currently being developed for NiV, and studies funded to evaluate their safety and immunogenicity, so that they could possibly be used to contain outbreaks. An important unanswered question is whether it will be possible to evaluate the efficacy of vaccine candidates in phase III clinical trials in a context where spillovers from the zoonotic reservoir are infrequent and associated with small outbreaks. The objective of this study was to investigate the feasibility of conducting a phase III vaccine trial in Bangladesh, the only country reporting regularly NiV cases.MethodsWe used simulations based on previously observed NiV cases from Bangladesh, an assumed vaccine efficacy of 90% and other NiV vaccine target characteristics, to compare three vaccination study designs: (i) cluster randomized ring vaccination, (ii) cluster randomized mass vaccination, and (iii) an observational case-control study design.ResultsThe simulations showed that, assuming a ramp-up period of 10 days and a mean hospitalization delay of 4 days, it would take 516 years and over 163,000 vaccine doses to run a ring vaccination trial under current epidemic conditions. A cluster-randomized trial in the two most affected districts would take 81 years and 2.3 million vaccine doses. An observational case-control design in these two districts would require seven years and 2.5 million vaccine doses.DiscussionWithout a change in the epidemiology of NiV, ring vaccination or cluster-randomized trials are unlikely to be completed within a reasonable time window. In this light, the remaining options are: (i) not conducting a phase III trial until the epidemiology of NiV changes, (ii) identifying alternative ways to licensure such as observational studies or controlled studies in animals such as in the US Food and Drug Administration’s (FDA) Animal Rule.

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Overwhelming Evidence for Vaccine Efficacy in the Pfizer Trial: An Interim Bayesian Analysis

10.31234/osf.io/fs562 ◽

2020 ◽

Author(s):

Eric-Jan Wagenmakers ◽

Quentin Frederik Gronau

Keyword(s):

Logistic Regression ◽

Initial Data ◽

Bayesian Analysis ◽

Vaccine Efficacy ◽

Group Membership ◽

Vaccine Trial ◽

Evidence Based ◽

Phase 3 ◽

Overwhelming Evidence ◽

Bayesian Logistic Regression

On Monday November 9th 2020, Pfizer and BioNTech issued a press release in which Pfizer CEO Dr. Albert Bourla stated: “Today is a great day for science and humanity. The first set of results from our Phase 3 COVID-19 vaccine trial provides the initial evidence of our vaccine’s ability to prevent COVID-19”. But what exactly are the initial data, and how strong is the initial evidence? Based on the available data, we conduct a Bayesian logistic regression with group membership (placebo vs. vaccine) as a predictor. Our results confirm that the Pfizer interim Phase 3 data are indeed highly promising. Even though the case numbers are relatively small, their distribution across the placebo and vaccinated group is so lopsided that (1) the evidence in favor of effectiveness is overwhelming; (2) the effect is almost certainly very large, although how large exactly is difficult to tell.

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Vaccine Efficacy at a Point in Time

10.1101/2021.02.04.21251133 ◽

2021 ◽

Author(s):

Dean Follmann ◽

Michael Fay

Keyword(s):

Vaccine Efficacy ◽

Proportional Hazards ◽

Proportional Hazards Model ◽

Vaccine Trial ◽

Phase Iii ◽

Cross Sectional ◽

Transmission Potential ◽

Viral Loads ◽

Hazards Model ◽

Vaccine Effect

AbstractVaccine trials are generally designed to assess efficacy on clinical disease. The vaccine effect on infection, while important both as a proxy for transmission and to describe a vaccine’s total effects, requires frequent longitudinal sampling to capture all infections. Such sampling may not always be feasible. A logistically easy approach is to collect a sample to test for infection at a regularly scheduled visit. Such point or cross-sectional sampling does not permit estimation of classic vaccine effiacy on infection, as long duration infections are sampled with higher probability. Building on work by Rinta-Kokko and others (2009) we evaluate proxies of the vaccine effect on transmission at a point in time; the vaccine efficacy on prevalent infection and on prevalent viral load, VEPI and VEPV L, respectively. Longer infections with higher viral loads should have more transmission potential and prevalent vaccine efficacy naturally captures this aspect. We apply a proportional hazards model for infection risk and show how these metrics can be estimated using longitudinal or cross-sectional sampling. We also introduce regression models for designs with multiple cross-sectional sampling. The methods are evaluated by simulation and a phase III vaccine trial with PCR cross-sectional sampling for subclinical infection is analyzed.

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High-order compact LOD methods for solving high-dimensional advection equations

Computational and Applied Mathematics ◽

10.1007/s40314-021-01483-w ◽

2021 ◽

Vol 40 (3) ◽

Author(s):

Bo Hou ◽

Yongbin Ge

Keyword(s):

Truncation Error ◽

Three Dimensional ◽

Taylor Series Expansion ◽

High Order ◽

High Dimensional ◽

Analysis Method ◽

Order Accuracy ◽

Von Neumann ◽

One Dimensional ◽

Von Neumann Analysis

AbstractIn this paper, by using the local one-dimensional (LOD) method, Taylor series expansion and correction for the third derivatives in the truncation error remainder, two high-order compact LOD schemes are established for solving the two- and three- dimensional advection equations, respectively. They have the fourth-order accuracy in both time and space. By the von Neumann analysis method, it shows that the two schemes are unconditionally stable. Besides, the consistency and convergence of them are also proved. Finally, numerical experiments are given to confirm the accuracy and efficiency of the present schemes.

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Is Bayesian Analysis Ready for Use in Phase III Randomized Clinical Trials?

Stroke ◽

10.1161/01.str.0000170638.55491.bb ◽

2005 ◽

Vol 36 (7) ◽

pp. 1622-1623 ◽

Cited By ~ 11

Author(s):

George Howard ◽

Christopher S. Coffey ◽

Gary R. Cutter

Keyword(s):

Clinical Trials ◽

Bayesian Analysis ◽

Randomized Clinical Trials ◽

Phase Iii

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Thinking in Bayesian inferences: A brief reflection on statistical approach for psychological research

10.31219/osf.io/gp2qr ◽

2021 ◽

Author(s):

Tri Tam Le

Keyword(s):

Bayesian Analysis ◽

Personal Experience ◽

Statistical Approach ◽

Psychological Research ◽

Analysis Method ◽

Bayesian Inferences

I give some benefits of the Bayesian analysis method from my personal experience in psychological research.

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Design of vaccine efficacy trials during public health emergencies

Science Translational Medicine ◽

10.1126/scitranslmed.aat0360 ◽

2019 ◽

Vol 11 (499) ◽

pp. eaat0360 ◽

Cited By ~ 6

Author(s):

Natalie E. Dean ◽

Pierre-Stéphane Gsell ◽

Ron Brookmeyer ◽

Victor De Gruttola ◽

Christl A. Donnelly ◽

...

Keyword(s):

Public Health ◽

Vaccine Efficacy ◽

Vaccine Trial ◽

Disease Outbreak ◽

Public Health Emergencies ◽

Efficacy Trials ◽

Challenging Environment

Public health emergencies, such as an Ebola disease outbreak, provide a complex and challenging environment for the evaluation of candidate vaccines. Here, we outline the need for flexible and responsive vaccine trial designs to be used in public health emergencies, and we summarize recommendations for their use in this setting.

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