The International Journal of Biostatistics
Ed. by Chambaz, Antoine / Hubbard, Alan E. / van der Laan, Mark J.
2 Issues per year
IMPACT FACTOR 2014: 0.741
5-year IMPACT FACTOR: 1.475
SCImago Journal Rank (SJR) 2014: 1.247
Source Normalized Impact per Paper (SNIP) 2014: 1.078
Impact per Publication (IPP) 2014: 1.206
Mathematical Citation Quotient (MCQ) 2014: 0.07
Volume 11 (2015)
Volume 7 (2011)
Volume 5 (2009)
Volume 4 (2008)
Volume 3 (2007)
Volume 2 (2006)
Volume 1 (2005)
Most Downloaded Articles
- An Introduction to Causal Inference by Pearl, Judea
- Sample Size Estimation for Repeated Measures Analysis in Randomized Clinical Trials with Missing Data by Lu, Kaifeng/ Luo, Xiaohui and Chen, Pei-Yun
- Survival Models in Health Economic Evaluations: Balancing Fit and Parsimony to Improve Prediction by Jackson, Christopher H/ Sharples, Linda D and Thompson, Simon G
- Evaluating treatment effectiveness in patient subgroups: a comparison of propensity score methods with an automated matching approach by Radice, Rosalba/ Ramsahai, Roland/ Grieve, Richard/ Kreif, Noemi/ Sadique, Zia and Sekhon, Jasjeet S.
- Survival Curve Estimation with Dependent Left Truncated Data Using Cox's Model by Mackenzie, Todd
Causal Inference for Vaccine Effects on Infectiousness
1Fred Hutchinson Cancer Research Center and University of Washington
2University of North Carolina at Chapel Hill
Citation Information: The International Journal of Biostatistics. Volume 8, Issue 2, Pages 1–40, ISSN (Online) 1557-4679, DOI: 10.2202/1557-4679.1354, January 2012
- Published Online:
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.
Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.