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事件史分析与观察性流行病学推断

Event history analysis and inference from observational epidemiology.

作者信息

Keiding N

机构信息

Department of Biostatistics, University of Copenhagen, Blegdamsvej 3, DK-2200 Copenhagen N, Denmark.

出版信息

Stat Med. 1999;18(17-18):2353-63. doi: 10.1002/(sici)1097-0258(19990915/30)18:17/18<2353::aid-sim261>3.0.co;2-#.

Abstract

Systematic inclusion of time in observational epidemiological studies may help strengthen the inference to be drawn, but new epidemiological challenges arise, such as time-dependent confounders - covariates which may change from being confounders to being intermediate variables. The focus of this presentation concerns two sets of tools: event history analysis and structural nested failure time models, both applied to a particularly intricate problem in observational epidemiology, of empirically assessing the graft-versus-leukaemia effect after bone marrow transplantation.

摘要

在观察性流行病学研究中系统地纳入时间因素可能有助于加强所得出的推断,但也会出现新的流行病学挑战,比如随时间变化的混杂因素——协变量可能从混杂因素转变为中间变量。本报告重点关注两组工具:事件史分析和结构嵌套失效时间模型,这两种工具都应用于观察性流行病学中一个特别复杂的问题,即实证评估骨髓移植后的移植物抗白血病效应。

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