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Causal inference for vaccine effects on infectiousness.
M Elizabeth Halloran1, Michael G Hudgens
1Center for Quantitative Infectious Diseases, Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Department of Biostatistics, University of Washington, Washington, USA.
The International Journal of Biostatistics
|April 14, 2012
Summary
Vaccine efficacy for infectiousness estimates can be biased. This study develops causal methods to accurately measure how vaccines reduce infectiousness, even when they don't prevent infection entirely.
Area of Science:
- Epidemiology
- Biostatistics
- Infectious Disease Modeling
Background:
- Vaccines may reduce infectiousness even if they don't fully prevent infection.
- Current methods for estimating vaccine efficacy against infectiousness are prone to selection bias and lack causal interpretation.
Purpose of the Study:
- To develop causal estimands for vaccine efficacy against infectiousness.
- To address limitations of existing estimation methods in the context of transmission dynamics.
Main Methods:
- Development of causal estimands for vaccine efficacy for infectiousness.
- Incorporation of principal stratification and interference within transmission units (dyads).
- Analysis of four distinct population scenarios, including a general case and special cases.
Main Results:
- Causal estimands are well-defined within specific principal strata.
- Identifiability of estimands generally requires strong, unverifiable assumptions.
- Large sample bounds on causal vaccine efficacy for infectiousness were derived.
Conclusions:
- Existing methods for estimating vaccine efficacy for infectiousness are problematic.
- Causal inference provides a framework for more accurate estimation.
- The developed framework offers insights into vaccine impact on transmission, even with imperfect protection.
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