Survival analysis and regression in infectious disease epidemiology

传染病流行病学中的生存分析和回归

基本信息

批准号：
8432206
负责人：
Eben Kenah
金额：
$ 9.62万
依托单位：
UNIVERSITY OF FLORIDA
依托单位国家：
美国
项目类别：
财政年份：
2011
资助国家：
美国
起止时间：
2011-06-07 至 2012-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8432206
关键词：
Survival analysis regression infectious disease

项目摘要

DESCRIPTION (provided by applicant): The project aims to develop more flexible and powerful statistical methods for infectious disease epidemiology based on survival analysis and contact intervals. The contact interval from person A to person B is the time between the onset of infectiousness in A and infectious contact from A to B, where infectious contact is defined to be a contact sufficient to infect a susceptible person. It can be right-censored if B is infected by someone other than A or if A recovers from infectiousness prior to infectious contact with B. Because of this censoring, survival analysis is the natural approach to estimating the contact interval distribution. During the K99 phase, this project will develop nonparametric estimates of the contact interval distribution and semiparametric regression models allowing the estimation of log hazard ratios representing the effects of covariates on infectiousness and susceptibility. Since many of these estimates can be represented as sums or averages over possible transmission trees, they provide a natural bridge between traditional epidemic data and phylogenetic data. During the R00 phase, generalizations of the K99 methods will be developed and placed into a Bayesian framework for the analysis of partially-observed epidemics. Survival analysis is one of the pillars of modern biostatistics, so it will be a rich source of novel study designs and analytical methods in infectious disease epidemiology.

描述（由申请人提供）：该项目旨在基于生存分析和接触间隔，开发更灵活、更强大的传染病流行病学统计方法。 A 人与 B 人的接触间隔是指 A 发生传染性与 A 与 B 发生传染性接触之间的时间，其中传染性接触定义为足以感染易感者的接触。如果 B 被 A 以外的人感染，或者 A 在与 B 发生传染性接触之前从传染性中恢复，则可以对它进行右删失。由于这种删失，生存分析是估计接触间隔分布的自然方法。在 K99 阶段，该项目将开发接触间隔分布的非参数估计和半参数回归模型，从而可以估计代表协变量对传染性和易感性影响的对数风险比。由于许多这些估计可以表示为可能的传播树的总和或平均值，因此它们在传统流行病数据和系统发育数据之间提供了天然的桥梁。在 R00 阶段，将开发 K99 方法的概括，并将其放入贝叶斯框架中，以分析部分观察到的流行病。生存分析是现代生物统计学的支柱之一，因此它将成为传染病流行病学中新颖的研究设计和分析方法的丰富来源。