Data · dataset · 2015
Semiparametric Relative-Risk Regression for Infectious Disease Transmission Data
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This article introduces semiparametric relative-risk regression models for infectious disease data.
Description
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.
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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.
Links
Where it is published
- Repository landing page tandf.figshare.com/articles/dataset/Semiparametric_Relative_Risk_Regression_for_I… ↗
landing page · from DataCite
- DOI doi.org/10.6084/m9.figshare.1390602 ↗
DOI / persistent id · from DataCite
Documentation and papers
- Creative Commons Attribution 4.0 International creativecommons.org/licenses/by/4.0/legalcode ↗
license · from DataCite
- IsSupplementTo 10.1080/01621459.2014.896807 doi.org/10.1080/01621459.2014.896807 ↗
publication · from DataCite
Catalogue records · 2
- DataCite API api.datacite.org/dois/10.6084/m9.figshare.1390602 ↗
metadata API · from DataCite
- DataCite Commons commons.datacite.org/doi.org/10.6084/m9.figshare.1390602 ↗
catalogue entry · from DataCite
Topics
- Stated by source
- Biological sciences · Health sciences · Mathematics
- From keywords
- Cancer · Mathematics & Statistics · Medicine & Health
- Inferred from text
- Disease 75%
Provenance · 1 source records, 15 field assertions
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|---|---|---|---|
| DataCite | 10.6084/m9.figshare.1390602 | 15 d ago | JSON v1 |
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