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: 8164352
• 负责人: Eben Kenah
• 金额: $ 2.39万
• 依托单位: UNIVERSITY OF WASHINGTON
• 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 2011-09-01
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8164352
• 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; analytical method; base; experience; flexibility; hazard; novel; pathogen; simulation; theories; transmission process

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. PUBLIC HEALTH RELEVANCE (provided by applicant): The project aims to extend statistical methods from survival analysis, which are used throughout chronic-disease epidemiology, to the analysis of infectious disease data. The resulting methods will be more flexible and powerful than current methods in infectious disease epidemiology, allowing determinants of susceptibility and infectiousness to be identified with greater accuracy.

项目描述（由申请人提供）：该项目旨在基于生存分析和接触间隔为传染病流行病学开发更灵活和强大的统计方法。从A人到B人的接触间隔是从A人开始传染到A人与B人发生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者的接触。如果B被A以外的人感染，或者如果A在与B发生传染性接触之前从传染性中恢复，则可以进行右删失。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值和半参数回归模型，以估计代表协变量对传染性和易感性影响的对数风险比。由于这些估计值中的许多可以表示为可能的传播树的总和或平均值，因此它们在传统的流行病数据和系统发育数据之间提供了一个天然的桥梁。在R00阶段，将开发K99方法的推广，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研究设计和分析方法的丰富来源。 公共卫生相关性（由申请人提供）：该项目旨在将统计方法从用于整个慢性病流行病学的生存分析扩展到传染病数据分析。由此产生的方法将比目前的传染病流行病学方法更加灵活和强大，从而能够更准确地确定易感性和传染性的决定因素。