Semiparametric Relative-Risk Regression for Infectious Disease Transmission Data

Eben Kenah

Journal of the American Statistical Association, 2015, vol. 110, issue 509, 313-325

Abstract: This article introduces semiparametric relative-risk regression models for infectious disease data. The units of analysis in these models are pairs of individuals at risk of transmission. The hazard of infectious contact from i to j consists of a baseline hazard multiplied by a relative risk function that can be a function of infectiousness covariates for i , susceptibliity covariates for j , and pairwise covariates. When who-infects-whom is observed, we derive a profile likelihood maximized over all possible baseline hazard functions that is similar to the Cox partial likelihood. When who-infects-whom is not observed, we derive an EM algorithm to maximize the profile likelihood integrated over all possible combinations of who-infected-whom. This extends the most important class of regression models in survival analysis to infectious disease epidemiology. These methods can be implemented in standard statistical software, and they will be able to address important scientific questions about emerging infectious diseases with greater clarity, flexibility, and rigor than current statistical methods allow. Supplementary materials for this article are available online.

Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1080/01621459.2014.896807 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:jnlasa:v:110:y:2015:i:509:p:313-325

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/UASA20

DOI: 10.1080/01621459.2014.896807

Access Statistics for this article

Journal of the American Statistical Association is currently edited by Xuming He, Jun Liu, Joseph Ibrahim and Alyson Wilson

More articles in Journal of the American Statistical Association from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().