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Event history analysis and inference from observational epidemiology
Author(s) -
Keiding Niels
Publication year - 1999
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/(sici)1097-0258(19990915/30)18:17/18<2353::aid-sim261>3.0.co;2-#
Subject(s) - observational study , inference , event (particle physics) , epidemiology , computer science , causal inference , econometrics , data science , statistics , medicine , artificial intelligence , mathematics , physics , quantum mechanics
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. Copyright © 1999 John Wiley & Sons, Ltd.