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Randomization and Baseline Transmission

  • M. Elizabeth Halloran
  • Ira M. LonginiJr.
  • Claudio J. Struchiner
Chapter
Part of the Statistics for Biology and Health book series (SBH)

Abstract

Vaccination could interact with population characteristics such as pre-existing immunity, genetic composition, intensity of transmission, or nutritional status, so that estimates of efficacy of a given vaccine in different populations could differ considerably. The biologic efficacy could be the same in the different populations, but the composition of the population would result in the differing efficacy estimates based on the epidemiological outcome of interest. Considerations along these lines demonstrate the role and limitations of randomization. Randomization is one assignment mechanism under which treatment is assigned independent of the potential outcome of interest. The treatment assignment is also independent of other covariates under randomization. Randomization also allows, on average, for a balanced distribution of any covariates, observed or not, in the vaccine and placebo groups. Thus, the treatment groups are seen as comparable. Baseline transmission, pre-existing immunity, and individual responsiveness are examples of possibly relevant factors. For these reasons, randomization, in addition to double-masking, are usually proposed as good research practices for valid clinical trials (Efron 1971).

Randomization, however, does not guarantee that the estimated effect is an unbiased estimate of the biologic effect of interest. Statistical validity does not necessarily guarantee epidemiological validity. That is, there is a distinction between statistical bias and epidemiological confounding. The ability of randomization to control for confounding has been challenged from at least two perspectives. Greenland and Robins (1986), Greenland (1987), and Greenland et al (1999) state the problem from the perspective of potential outcomes and show that effect measures can be confounded even if the treatment assignment mechanism is random. Gail (1986, 1988) and Gail et al. (1984, 1988) examine the effects of omitting a covariate that has the same distribution among exposed and unexposed subjects from regression analyses of cohort data. They describe the conditions under which a balanced covariate can be omitted without biasing the estimates.

Keywords

Cumulative Incidence Transmission Probability Trial Site Vaccine Trial Malaria Vaccine 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag New York 2010

Authors and Affiliations

  • M. Elizabeth Halloran
    • 1
  • Ira M. LonginiJr.
    • 1
  • Claudio J. Struchiner
    • 2
  1. 1.Center for Statistics and Quantitative Infectious DiseasesUniversity of Washington, and Fred Hutchinson Cancer Research CenterSeattleUSA
  2. 2.Escola Nacional de Saúde Pública Fundação Oswaldo CruzRio de JaneiroBrazil

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