Survival analysis and regression in infectious disease epidemiology

传染病流行病学中的生存分析和回归

• 批准号: 8507869
• 负责人: Eben Kenah
• 金额: $ 22.6万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-06-07 至 2014-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8507869
• 关键词: Bayesian Analysis; Behavior; Biometry; Chronic Disease; Code; Communicable Diseases; Communities; Confidence Intervals; Data; Data Analyses; Data Set; Disease Outbreaks; Epidemic; Epidemiology; Evaluation; Event; Genetic; Household; Individual; Infection; Infectious Disease Epidemiology; Investigation; Joints; Life; Markov Chains; Methods; Modeling; Observational Study; Persons; Phase; Phylogenetic Analysis; Population; Predisposition; Research; Research Design; Sampling; Source; Statistical Methods; Sum; Survival Analysis; System; Testing; Time; Trees; Vaccines; abstracting; analytical method; base; experience; flexibility; hazard; novel; pathogen; simulation; theories; transmission process

Project summary/abstract 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.

项目摘要/摘要 该项目旨在为传染病开发更灵活和强大的统计方法 基于生存分析和接触间隔的流行病学。人的联系间隔 A到人B是A的传染性发作与A的感染性接触之间的时间 到B，将传染性接触定义为足以感染易感人的接触。 如果B被A以外的其他人感染或A从 传染性在与B传染之前。由于这种检查，生存分析是 估计接触间隔分布的自然方法。在K99阶段，这个 项目将制定触点间隔分布的非参数估计，并 半参数回归模型允许估计代表对数危险比的估计 协变量对传染性和敏感性的影响。由于这些估计中有许多可以是 它们以可能的传输树为代表或平均值，它们提供了自然的 传统流行数据和系统发育数据之间的桥梁。在R00阶段， K99方法的概括将开发并放入贝叶斯框架中 部分观察到的流行病的分析。生存分析是现代的支柱之一 生物统计学，因此它将成为新颖的研究设计和分析方法的丰富来源 传染病流行病学。