Survival analysis and regression in infectious disease epidemiology

描述(申请人提供)：该项目旨在开发基于生存分析和接触间隔的更灵活和更强大的传染病流行病学统计方法。从人A到人B的接触间隔是从A的传染性开始到从A到B的传染性接触之间的时间，其中传染性接触被定义为足以感染易感人群的接触。如果B是被A以外的人感染的，或者如果A在与B有传染性接触之前从传染性中恢复过来，则可以进行右审查。由于这种审查，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，允许估计代表协变量对传染性和易感性的影响的对数风险比。由于这些估计中的许多可以表示为可能的传播树上的总和或平均值，它们在传统的流行病数据和系统发育数据之间提供了一座天然的桥梁。在R00阶段，将制定K99方法的概括，并将其纳入贝叶斯框架，用于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新的研究设计和分析方法的丰富来源。

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.