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Author Notes:

Michael Haber, Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GE, USA. Email: mhaber@emory.edu

No potential conflicts of interest were disclosed.

Subjects:

Research Funding:

This work was supported by the Centers for Disease Controls and Prevention (CDC) via an IPA [19IPA1912112]. The findings and conclusions in this report are those of the author(s) and do not necessarily represent the official position of the Centers for Disease Control and Prevention.

Keywords:

  • Science & Technology
  • Life Sciences & Biomedicine
  • Biotechnology & Applied Microbiology
  • Immunology
  • waning vaccine efficacy
  • rotavirus vaccine
  • hazard of infection
  • Cox regression model
  • agent-based simulations

Comparing statistical methods for detecting and estimating waning efficacy of rotavirus vaccines in developing countries

Tools:

Journal Title:

HUMAN VACCINES & IMMUNOTHERAPEUTICS

Volume:

Volume 17, Number 11

Publisher:

, Pages 4632-4635

Type of Work:

Article | Final Publisher PDF

Abstract:

Introduction: Vaccination has significantly reduced morbidity and mortality resulting from rotavirus infection worldwide. However, rotavirus vaccine efficacy (VE) appears to wane over the first 2 years since vaccination, particularly in developing countries. Statistical methods for detecting VE waning and estimating its rate have been used in a few studies, but comparisons of methods for evaluating VE waning have not yet been performed. In this work we present and compare three methods–Durham’s method, Tian’s method, and time-dependent covariate (TDC) method–based on generalizations of the Cox proportional hazard model. Methods: We developed a new stochastic agent-based simulation model to generate data from a hypothetical rotavirus vaccine trial where the protective efficacy of the vaccine may vary over time. Input parameters to the simulation model were obtained from studies on rotavirus infections in four developing countries. We applied each of the methods to four simulated datasets and compared the type-1 error probabilities and the powers of the resulting statistical tests. We also compared estimated and true values of VE over time. Results: Durham’s method had the highest power of detecting true VE waning of the three methods. This method also provided quite accurate estimates of VE in each period and of the per-period drop in VE. Conclusions: Durham’s method is somewhat more powerful than the other two Cox proportional hazards model-based methods for detecting VE waning and provides more information about the temporal behavior of VE.

Copyright information:

© 2021 Taylor & Francis Group, LLC

This is an Open Access work distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).
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