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人时和事件分层方法——泊松回归和标准化发病比估计的前提条件。

Methods for stratification of person-time and events - a prerequisite for Poisson regression and SIR estimation.

作者信息

Rostgaard Klaus

机构信息

Department of Epidemiology Research, Statens Serum Institut, Artillerivej 5, DK-2300S Copenhagen, Denmark.

出版信息

Epidemiol Perspect Innov. 2008 Nov 14;5:7. doi: 10.1186/1742-5573-5-7.

Abstract

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.

摘要

引言

许多用于分析随访研究的流行病学方法需要基于累积的人时和事件来计算发病率,并按各种因素进行分层。管理这种分层和累积通常是这类分析中最困难的方面。

教程

我们提供了一个关于按各种因素对人时和事件进行累积的教程,即创建事件时间表。我们展示了如何同时有效地为许多不同结局生成事件时间表。我们还提供了一个新的词汇表来描述和区分随时间变化的因素。本教程重点介绍使用SAS宏来执行创建事件时间表中的大多数常见任务。所有最常见类型的随时间变化的协变量都可以由该宏生成并分类。它还可以提供适用于其他类型生存分析(如Cox回归)的输出。我们方法的目的是支持创建无错误、可读、高效、功能强大且易于修改的程序来制作事件时间表。我们简要比较了基于事件时间表的分析与Cox回归以及巢式病例对照研究在随访数据分析中的应用。

结论

任何处理随时间变化协变量的人,尤其是处理大型详细人时数据集的人,都会从将这些方法纳入其编程工具包中受益。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8da/2615420/758f3c5ba6b5/1742-5573-5-7-1.jpg

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