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Frailty modelling for the excess hazard
Author(s) -
Zahl PerHenrik
Publication year - 1997
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(19970730)16:14<1573::aid-sim585>3.0.co;2-q
Subject(s) - excess mortality , hazard ratio , multivariate statistics , proportional hazards model , hazard , selection (genetic algorithm) , absolute risk reduction , statistics , population , medicine , econometrics , demography , mathematics , environmental health , confidence interval , computer science , biology , ecology , artificial intelligence , sociology
Long‐term excess hazards for cancer survival sometimes tend to zero or become negative even though we expect them to be positive. This may be explained by selection at diagnosis; individuals with certain cancers may have an increased risk of dying of other diseases in general. Then comparing with population mortality rates is not correct. Alternatively, we may have a continuous selection of the most robust individuals after diagnosis. When there are unobserved heterogeneity, and those with highest risk of dying of cancer also have the highest risk of dying of other diseases, this will cause selection after diagnosis. This may be modelled by multivariate frailty variables, and a corrected excess hazard may be estimated. In two examples, these corrected excess hazards give a better estimate when comparing to the cause‐specific cancer mortality. Actually, this study questions the usefulness of long‐term excess hazard rates. © 1997 John Wiley & Sons, Ltd.