Semiparametric relative-risk regression for infectious disease transmission data

• Autores: Eben Kenah
• Localización: Journal of the American Statistical Association, ISSN 0162-1459, Vol. 110, Nº 509, 2015, págs. 313-325
• Idioma: inglés
• DOI: 10.1080/01621459.2014.896807
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• Resumen

  • 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.