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Causal Inference for Vaccine Effects on Infectiousness

Listed author(s):
  • Halloran M. Elizabeth

    (Fred Hutchinson Cancer Research Center and University of Washington)

  • Hudgens Michael G.

    (University of North Carolina at Chapel Hill)

Registered author(s):

    If a vaccine does not protect individuals completely against infection, it could still reduce infectiousness of infected vaccinated individuals to others. Typically, vaccine efficacy for infectiousness is estimated based on contrasts between the transmission risk to susceptible individuals from infected vaccinated individuals compared with that from infected unvaccinated individuals. Such estimates are problematic, however, because they are subject to selection bias and do not have a causal interpretation. Here, we develop causal estimands for vaccine efficacy for infectiousness for four different scenarios of populations of transmission units of size two. These causal estimands incorporate both principal stratification, based on the joint potential infection outcomes under vaccine and control, and interference between individuals within transmission units. In the most general scenario, both individuals can be exposed to infection outside the transmission unit and both can be assigned either vaccine or control. The three other scenarios are special cases of the general scenario where only one individual is exposed outside the transmission unit or can be assigned vaccine. The causal estimands for vaccine efficacy for infectiousness are well defined only within certain principal strata and, in general, are identifiable only with strong unverifiable assumptions. Nonetheless, the observed data do provide some information, and we derive large sample bounds on the causal vaccine efficacy for infectiousness estimands. An example of the type of data observed in a study to estimate vaccine efficacy for infectiousness is analyzed in the causal inference framework we developed.

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    File URL: https://www.degruyter.com/view/j/ijb.2012.8.issue-2/1557-4679.1354/1557-4679.1354.xml?format=INT
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    Article provided by De Gruyter in its journal The International Journal of Biostatistics.

    Volume (Year): 8 (2012)
    Issue (Month): 2 (January)
    Pages: 1-40

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    Handle: RePEc:bpj:ijbist:v:8:y:2012:i:2:n:6
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    1. Sobel, Michael E., 2006. "What Do Randomized Studies of Housing Mobility Demonstrate?: Causal Inference in the Face of Interference," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1398-1407, December.
    2. Susmita Datta & M. Elizabeth Halloran & Ira M. Longini Jr, 1999. "Efficiency of Estimating Vaccine Efficacy for Susceptibility and Infectiousness: Randomization by Individual Versus Household," Biometrics, The International Biometric Society, vol. 55(3), pages 792-798, 09.
    3. Imai, Kosuke, 2008. "Sharp bounds on the causal effects in randomized experiments with "truncation-by-death"," Statistics & Probability Letters, Elsevier, vol. 78(2), pages 144-149, February.
    4. Bryan E. Shepherd & Peter B. Gilbert & Yannis Jemiai & Andrea Rotnitzky, 2006. "Sensitivity Analyses Comparing Outcomes Only Existing in a Subset Selected Post-Randomization, Conditional on Covariates, with Application to HIV Vaccine Trials," Biometrics, The International Biometric Society, vol. 62(2), pages 332-342, 06.
    5. Yannis Jemiai & Andrea Rotnitzky & Bryan E. Shepherd & Peter B. Gilbert, 2007. "Semiparametric estimation of treatment effects given base-line covariates on an outcome measured after a post-randomization event occurs," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(5), pages 879-901.
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