A Bayesian model for evaluating influenza antiviral efficacy in household studies with asymptomatic infections.

Publication Type:

Journal Article

Source:

Biostatistics (Oxford, England), Volume 10, Issue 2, p.390-403 (2009)

Keywords:

2009, Antiviral Agents, Bayes Theorem, Center-Authored Paper, Epidemiology Core Facility, Family Characteristics, Humans, Influenza, Human, Markov Chains, Models, Biological, Monte Carlo Method, Oseltamivir, Public Health Sciences Division, Randomized Controlled Trials as Topic, Research Trials Office Core Facility - Biostatistics Service, RNA, Viral, Shared Resources

Abstract:

Antiviral agents are an important component in mitigation/containment strategies for pandemic influenza. However, most research for mitigation/containment strategies relies on the antiviral efficacies evaluated from limited data of clinical trials. Which efficacy measures can be reliably estimated from these studies depends on the trial design, the size of the epidemics, and the statistical methods. We propose a Bayesian framework for modeling the influenza transmission dynamics within households. This Bayesian framework takes into account asymptomatic infections and is able to estimate efficacies with respect to protecting against viral infection, infection with clinical disease, and pathogenicity (the probability of disease given infection). We use the method to reanalyze 2 clinical studies of oseltamivir, an influenza antiviral agent, and compare the results with previous analyses. We found significant prophylactic efficacies in reducing the risk of viral infection and infection with disease but no prophylactic efficacy in reducing pathogenicity. We also found significant therapeutic efficacies in reducing pathogenicity and the risk of infection with disease but no therapeutic efficacy in reducing the risk of viral infection in the contacts.