{"title":"人时和事件分层方法--泊松回归和 SIR 估计的先决条件。","authors":"Klaus Rostgaard","doi":"10.1186/1742-5573-5-7","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>Many epidemiological methods for analysing follow-up studies require the calculation of rates based on accumulating person-time and events, stratified by various factors. Managing this stratification and accumulation is often the most difficult aspect of this type of analysis.</p><p><strong>Tutorial: </strong>We provide a tutorial on accumulating person-time and events, stratified by various factors i.e. creating event-time tables. We show how to efficiently generate event-time tables for many different outcomes simultaneously. We also provide a new vocabulary to characterise and differentiate time-varying factors. The tutorial is focused on using a SAS macro to perform most of the common tasks in the creation of event-time tables. All the most common types of time-varying covariates can be generated and categorised by the macro. It can also provide output suitable for other types of survival analysis (e.g. Cox regression). The aim of our methodology is to support the creation of bug-free, readable, efficient, capable and easily modified programs for making event-time tables. We briefly compare analyses based on event-time tables with Cox regression and nested case-control studies for the analysis of follow-up data.</p><p><strong>Conclusion: </strong>Anyone working with time-varying covariates, particularly from large detailed person-time data sets, would gain from having these methods in their programming toolkit.</p>","PeriodicalId":87082,"journal":{"name":"Epidemiologic perspectives & innovations : EP+I","volume":"5 ","pages":"7"},"PeriodicalIF":0.0000,"publicationDate":"2008-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2615420/pdf/","citationCount":"0","resultStr":"{\"title\":\"Methods for stratification of person-time and events - a prerequisite for Poisson regression and SIR estimation.\",\"authors\":\"Klaus Rostgaard\",\"doi\":\"10.1186/1742-5573-5-7\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>Many epidemiological methods for analysing follow-up studies require the calculation of rates based on accumulating person-time and events, stratified by various factors. Managing this stratification and accumulation is often the most difficult aspect of this type of analysis.</p><p><strong>Tutorial: </strong>We provide a tutorial on accumulating person-time and events, stratified by various factors i.e. creating event-time tables. We show how to efficiently generate event-time tables for many different outcomes simultaneously. We also provide a new vocabulary to characterise and differentiate time-varying factors. The tutorial is focused on using a SAS macro to perform most of the common tasks in the creation of event-time tables. All the most common types of time-varying covariates can be generated and categorised by the macro. It can also provide output suitable for other types of survival analysis (e.g. Cox regression). The aim of our methodology is to support the creation of bug-free, readable, efficient, capable and easily modified programs for making event-time tables. We briefly compare analyses based on event-time tables with Cox regression and nested case-control studies for the analysis of follow-up data.</p><p><strong>Conclusion: </strong>Anyone working with time-varying covariates, particularly from large detailed person-time data sets, would gain from having these methods in their programming toolkit.</p>\",\"PeriodicalId\":87082,\"journal\":{\"name\":\"Epidemiologic perspectives & innovations : EP+I\",\"volume\":\"5 \",\"pages\":\"7\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-11-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2615420/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Epidemiologic perspectives & innovations : EP+I\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1186/1742-5573-5-7\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Epidemiologic perspectives & innovations : EP+I","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1186/1742-5573-5-7","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
简介许多流行病学方法在分析随访研究时,都需要根据按各种因素分层的人时和事件的累积来计算比率。管理这种分层和累积往往是这类分析最困难的方面:我们将为您提供有关按各种因素分层累计人时和事件的教程,即创建事件时间表。我们展示了如何同时有效地生成多种不同结果的事件时间表。我们还提供了一个新的词汇来描述和区分时变因素。本教程的重点是使用 SAS 宏来执行创建事件时间表中的大部分常见任务。所有最常见的时变协变量类型都可以通过宏生成和分类。它还能提供适用于其他类型生存分析(如 Cox 回归)的输出结果。我们的方法旨在支持创建无错误、可读性强、高效、有能力且易于修改的程序,用于制作事件-时间表格。我们简要比较了基于事件时间表的分析与用于分析随访数据的 Cox 回归和嵌套病例对照研究:结论:任何处理时变协变量的人,尤其是来自大型详细个人时间数据集的人,都会从他们的编程工具包中获得这些方法。
Methods for stratification of person-time and events - a prerequisite for Poisson regression and SIR estimation.
Introduction: Many epidemiological methods for analysing follow-up studies require the calculation of rates based on accumulating person-time and events, stratified by various factors. Managing this stratification and accumulation is often the most difficult aspect of this type of analysis.
Tutorial: We provide a tutorial on accumulating person-time and events, stratified by various factors i.e. creating event-time tables. We show how to efficiently generate event-time tables for many different outcomes simultaneously. We also provide a new vocabulary to characterise and differentiate time-varying factors. The tutorial is focused on using a SAS macro to perform most of the common tasks in the creation of event-time tables. All the most common types of time-varying covariates can be generated and categorised by the macro. It can also provide output suitable for other types of survival analysis (e.g. Cox regression). The aim of our methodology is to support the creation of bug-free, readable, efficient, capable and easily modified programs for making event-time tables. We briefly compare analyses based on event-time tables with Cox regression and nested case-control studies for the analysis of follow-up data.
Conclusion: Anyone working with time-varying covariates, particularly from large detailed person-time data sets, would gain from having these methods in their programming toolkit.