Studies of equivalence in clinical vaccine research

Robert M. Jacobson, Gregory A. Poland

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

The development of combination vaccines as well as the improved manufacture of vaccines have resulted in the need for clinical studies seeking equivalence or non-inferiority as investigators seek to demonstrate that combination vaccines achieve the same levels of efficacy, immunogenicity, and safety as their individual counterparts. Given the nature of the statistical analysis, studies of equivalence require particular attention to sample size. The current double-significance method of Neyman-Pearson attempts to address problems with equivalence testing, but it leads to very large sample sizes and illogical results. With such large samples, one can find a clinically trivial difference that is still statistically significant. The late Professor Alvan R. Feinstein proposed a more logical approach that would call for smaller, more workable sample sizes. Understanding the issues involved in sample size calculations for such studies is important to those who design clinical vaccine studies. The implications of the calculations will have far-reaching effects on the feasibility of the study such as availability of subjects, the success with recruitment, and the overall expenses. In fact, the feasibility issues may prevent the study being undertaken at all.

Original languageEnglish (US)
Pages (from-to)2315-2317
Number of pages3
JournalVaccine
Volume23
Issue number17-18
DOIs
StatePublished - Mar 18 2005

Fingerprint

Sample Size
clinical trials
Vaccines
vaccines
Combined Vaccines
Research
sampling
Feasibility Studies
Research Personnel
manufacturing
Safety
statistical analysis
immune response
testing
Clinical Studies
methodology

Keywords

  • Statistics
  • Therapeutic equivalence
  • Vaccines

ASJC Scopus subject areas

  • Immunology
  • Microbiology
  • Virology
  • Infectious Diseases
  • Public Health, Environmental and Occupational Health
  • veterinary(all)

Cite this

Studies of equivalence in clinical vaccine research. / Jacobson, Robert M.; Poland, Gregory A.

In: Vaccine, Vol. 23, No. 17-18, 18.03.2005, p. 2315-2317.

Research output: Contribution to journalArticle

Jacobson, RM & Poland, GA 2005, 'Studies of equivalence in clinical vaccine research', Vaccine, vol. 23, no. 17-18, pp. 2315-2317. https://doi.org/10.1016/j.vaccine.2005.01.025
Jacobson, Robert M. ; Poland, Gregory A. / Studies of equivalence in clinical vaccine research. In: Vaccine. 2005 ; Vol. 23, No. 17-18. pp. 2315-2317.
@article{65e0094fd6724e47b7293d49c477f6ca,
title = "Studies of equivalence in clinical vaccine research",
abstract = "The development of combination vaccines as well as the improved manufacture of vaccines have resulted in the need for clinical studies seeking equivalence or non-inferiority as investigators seek to demonstrate that combination vaccines achieve the same levels of efficacy, immunogenicity, and safety as their individual counterparts. Given the nature of the statistical analysis, studies of equivalence require particular attention to sample size. The current double-significance method of Neyman-Pearson attempts to address problems with equivalence testing, but it leads to very large sample sizes and illogical results. With such large samples, one can find a clinically trivial difference that is still statistically significant. The late Professor Alvan R. Feinstein proposed a more logical approach that would call for smaller, more workable sample sizes. Understanding the issues involved in sample size calculations for such studies is important to those who design clinical vaccine studies. The implications of the calculations will have far-reaching effects on the feasibility of the study such as availability of subjects, the success with recruitment, and the overall expenses. In fact, the feasibility issues may prevent the study being undertaken at all.",
keywords = "Statistics, Therapeutic equivalence, Vaccines",
author = "Jacobson, {Robert M.} and Poland, {Gregory A.}",
year = "2005",
month = "3",
day = "18",
doi = "10.1016/j.vaccine.2005.01.025",
language = "English (US)",
volume = "23",
pages = "2315--2317",
journal = "Vaccine",
issn = "0264-410X",
publisher = "Elsevier BV",
number = "17-18",

}

TY - JOUR

T1 - Studies of equivalence in clinical vaccine research

AU - Jacobson, Robert M.

AU - Poland, Gregory A.

PY - 2005/3/18

Y1 - 2005/3/18

N2 - The development of combination vaccines as well as the improved manufacture of vaccines have resulted in the need for clinical studies seeking equivalence or non-inferiority as investigators seek to demonstrate that combination vaccines achieve the same levels of efficacy, immunogenicity, and safety as their individual counterparts. Given the nature of the statistical analysis, studies of equivalence require particular attention to sample size. The current double-significance method of Neyman-Pearson attempts to address problems with equivalence testing, but it leads to very large sample sizes and illogical results. With such large samples, one can find a clinically trivial difference that is still statistically significant. The late Professor Alvan R. Feinstein proposed a more logical approach that would call for smaller, more workable sample sizes. Understanding the issues involved in sample size calculations for such studies is important to those who design clinical vaccine studies. The implications of the calculations will have far-reaching effects on the feasibility of the study such as availability of subjects, the success with recruitment, and the overall expenses. In fact, the feasibility issues may prevent the study being undertaken at all.

AB - The development of combination vaccines as well as the improved manufacture of vaccines have resulted in the need for clinical studies seeking equivalence or non-inferiority as investigators seek to demonstrate that combination vaccines achieve the same levels of efficacy, immunogenicity, and safety as their individual counterparts. Given the nature of the statistical analysis, studies of equivalence require particular attention to sample size. The current double-significance method of Neyman-Pearson attempts to address problems with equivalence testing, but it leads to very large sample sizes and illogical results. With such large samples, one can find a clinically trivial difference that is still statistically significant. The late Professor Alvan R. Feinstein proposed a more logical approach that would call for smaller, more workable sample sizes. Understanding the issues involved in sample size calculations for such studies is important to those who design clinical vaccine studies. The implications of the calculations will have far-reaching effects on the feasibility of the study such as availability of subjects, the success with recruitment, and the overall expenses. In fact, the feasibility issues may prevent the study being undertaken at all.

KW - Statistics

KW - Therapeutic equivalence

KW - Vaccines

UR - http://www.scopus.com/inward/record.url?scp=14844334883&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=14844334883&partnerID=8YFLogxK

U2 - 10.1016/j.vaccine.2005.01.025

DO - 10.1016/j.vaccine.2005.01.025

M3 - Article

VL - 23

SP - 2315

EP - 2317

JO - Vaccine

JF - Vaccine

SN - 0264-410X

IS - 17-18

ER -