期刊
EPIDEMIOLOGY
卷 31, 期 2, 页码 259-262出版社
LIPPINCOTT WILLIAMS & WILKINS
DOI: 10.1097/EDE.0000000000001135
关键词
Bayesian analysis; Hierarchical modeling; Pneumococcal vaccines; Vaccine efficacy
资金
- National Institute of Allergy and Infectious Diseases/National Institutes of Health [R01-AI123208, R01-AI137093]
- Bill and Melinda Gates Foundation [OPP1176267]
Pneumococcal conjugate vaccines target 10 or 13 specific serotypes. To evaluate the overall efficacy of these products, the vaccine-targeted serotypes are typically aggregated into a single group. However, it is often desirable to evaluate variations in effects for different serotypes. These serotype-specific estimates are often based on small counts, resulting in a high degree of uncertainty (i.e., large standard errors and wide confidence intervals). An alternative is to use a hierarchical Bayesian statistical model, which estimates overall effectiveness while simultaneously providing estimates of serotype-specific vaccine effects. These shrunken serotype-specific estimators often have smaller mean squared errors (MSEs) than unbiased versions due to a large decrease in posterior uncertainty. We reanalyzed published data from a randomized controlled trial on the efficacy of 13-valent pneumococcal conjugate vaccine (PCV13) against community-acquired pneumonia caused by vaccine-targeted serotype using a hierarchical model. This model provides a potential framework for obtaining estimates of serotype-specific vaccine effects with reduced MSEs.
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