Statistical Models for Predicting the Duration of Vaccine-Induced Protection

Drug information journal : DIJ / Drug Information Association volume 33, pages811–819(1999)Cite this article

Abstract

When new vaccine products are marketed, they are generally supported by clinical studies lasting only a few years. The duration of protection from disease, however, is often expected to be considerably longer than the time spans of the clinical studies. Thus, there are both conceptual and methodological problems in trying to estimate long-term protection based on data from short-term clinical studies. We discuss the utility of predicting the duration of protection based on extrapolating observed antibody data. Several modeling strategies that have appeared in the clinical and statistical literature are reviewed. Two alternative mixed model strategies are considered and three methods of constructing confidence intervals for the mean duration of protection are proposed. Finally, a sample data set is used to illustrate and compare the various methods.

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References

  1. 1.

    Werzberger A, Mensch B, Kuter B, Brown L, Lewis J, Sitrin R, Miller W, Shouval D, Wiens B, Calandra G, Ryan J, Provost P, Nalin D. A controlled trial of a Formalin-inactivated hepatitis A vaccine in healthy children. New Engl J Med. 1992;327:453–457.

    CAS  Article  Google Scholar 

  2. 2.

    Van Damme P, Thoelen S, Cramm M, De Groote K, Safary A, Meheus A. Inactivated hepatitis A vaccine: Reactogenicity, immunogenicity, and long-term antibody persistence. J Med Virol. 1994;44:446–451.

    Article  Google Scholar 

  3. 3.

    Wiens B, Bohidar N, Pigeon J, Egan J, Hurni W, Brown L, Kuter B, Nalin D. Duration of protection from clinical hepatitis A disease after vaccination with VAQTA. J Med Viol. 1996;49:235–241.

    CAS  Article  Google Scholar 

  4. 4.

    Wiedemann G, Kundt M, Ambrosch F, Safary A, D’ Hondt E, Delem A. Inactivated hepatitis A vaccine: Long-term antibody persistence. Vaccine. 1997;10(6/7):612–615.

    Article  Google Scholar 

  5. 5.

    Totos G, Gizaris V, Papaevangelou G. Hepatitis A vaccine: Persistence of antibodies 5 years after the first injection. Vaccine. 1997;15(11):1252–1253.

    CAS  Article  Google Scholar 

  6. 6.

    Bovier P, Farinelli T, Loutan L, Herzog C, Glueck R. Long-term immunogenicity following immunization with a virosome hepatitis A vaccine. Presented at the Fifth International Conference on Travel Medicine, Geneva, Switzerland, 1997; Abstract 129, 136.

    Google Scholar 

  7. 7.

    Coursaget P, Yvonnet B, Gilks W, Wang C, Day N, Chiron J, Diop-Mar I. Scheduling of revaccination against hepatitis B virus. Lancet. 1991;337:1180–1183.

    CAS  Article  Google Scholar 

  8. 8.

    Gesemann M, Scheiermann N. Quantification of hepatitis B vaccine-induced antibodies as a predictor of anti-HBs persistence. Vaccine. 1995;13(6):443–447.

    CAS  Article  Google Scholar 

  9. 9.

    Schofield T, Lei C. Modelling antibody persistence. Presented at the Eleventh Annual Midwest Biophar-maceutical Workshop, Muncie, IN, May 26, 1988.

    Google Scholar 

  10. 10.

    Miller WJ, Clark W, Hurni W, Kuter B, Schofield T, Nalin D. Sensitive assays for hepatitis A antibodies. J Med Virol 1993;41:201–204.

    CAS  Article  Google Scholar 

  11. 11.

    Harville DA. Maximum likelihood approaches to variance component estimation and to related problems. J Am Stat Assoc. 1977;72:320–338.

    Article  Google Scholar 

  12. 12.

    Fieller WC. The biological standardisation of insulin. J Roy Stat Society, Suppl. 1940;7:1–64.

    Article  Google Scholar 

  13. 13.

    Fieller WC. Some problems in interval estimation. J Roy Stat Society, B. 1954;16:175–185.

    Google Scholar 

  14. 14.

    Efron B. Bootstrap methods: Another look at the jackknife. Ann Stat. 1979;7:1–26.

    Article  Google Scholar 

Download references

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Affiliations

  1. Professor, Department of Mathematical Sciences, Villanova University, 800 Lancaster Avenue, Villanova, PA, 19085, USA

    Dr. Joseph G. Pigeon PhD

  2. Senior Biometrician, Senior Biometrician, Biostatistics and Research Data Systems, Merck Research Laboratories, West Point, Pennsylvania, USA

    Norman R. Bohidar PhD & Zhongxin Zhang PhD

  3. Scientist, Biometrics, Department of Biometrics and Data Management, Quintiles, Inc., Kansas City, Missouri, USA

    Brian L. Wiens PhD

Authors
  1. Dr. Joseph G. Pigeon PhD
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  2. Norman R. Bohidar PhD
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  3. Zhongxin Zhang PhD
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  4. Brian L. Wiens PhD
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Correspondence to Dr. Joseph G. Pigeon PhD.

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Pigeon, J.G., Bohidar, N.R., Zhang, Z. et al. Statistical Models for Predicting the Duration of Vaccine-Induced Protection. Ther Innov Regul Sci 33, 811–819 (1999). https://doi.org/10.1177/009286159903300322

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Key Words

  • Extrapolation
  • Bootstrap
  • Mixed models
  • Piecewise linear models
  • Fieller’s theorem