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Estimating the power to detect a change caused by a vaccine from time series data

Authors
  • Weinberger, Daniel M.1, 2
  • Warren, Joshua L.2, 3
  • 1 Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, 06520, USA
  • 2 Public Health Modeling Unit, Yale School of Public Health, New Haven, CT, 06520, USA
  • 3 Department of Biostatistics, Yale School of Public Health, New Haven, CT, 06520, USA
Type
Published Article
Journal
Gates Open Research
Publisher
F1000 Research Limited
Publication Date
Oct 19, 2020
Volume
4
Identifiers
DOI: 10.12688/gatesopenres.13116.2
PMID: 33117962
PMCID: PMC7578561
Source
PubMed Central
Keywords
License
Unknown

Abstract

When evaluating the effects of vaccination programs, it is common to estimate changes in rates of disease before and after vaccine introduction. There are a number of related approaches that attempt to adjust for trends unrelated to the vaccine and to detect changes that coincide with introduction. However, characteristics of the data can influence the ability to estimate such a change. These include, but are not limited to, the number of years of available data prior to vaccine introduction, the expected strength of the effect of the intervention, the strength of underlying secular trends, and the amount of unexplained variability in the data. Sources of unexplained variability include model misspecification, epidemics due to unidentified pathogens, and changes in ascertainment or coding practice among others. In this study, we present a simple simulation framework for estimating the power to detect a decline and the precision of these estimates. We use real-world data from a pre-vaccine period to generate simulated time series where the vaccine effect is specified a priori . We present an interactive web-based tool to implement this approach. We also demonstrate the use of this approach using observed data on pneumonia hospitalization from the states in Brazil from a period prior to introduction of pneumococcal vaccines to generate the simulated time series. We relate the power of the hypothesis tests to the number of cases per year and the amount of unexplained variability in the data and demonstrate how fewer years of data influence the results.

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