Modelling relative survival in the presence of incomplete data: a tutorial | Zendy

Ula Nur | Zendy; Lorraine Shack | Zendy; Bernard Rachet | Zendy; James R. Carpenter | Zendy; Michel P. Coleman | Zendy

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Modelling relative survival in the presence of incomplete data: a tutorial

Author(s) -

Ula Nur,

Lorraine Shack,

Bernard Rachet,

James R. Carpenter,

Michel P. Coleman

Publication year - 2009

Publication title -

international journal of epidemiology

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.406

H-Index - 208

eISSN - 1464-3685

pISSN - 0300-5771

DOI - 10.1093/ije/dyp309

Subject(s) - missing data , statistics , imputation (statistics) , poisson regression , proportional hazards model , survival analysis , medicine , population , relative survival , colorectal cancer , regression analysis , mathematics , cancer , cancer registry , environmental health

Missing data frequently create problems in the analysis of population-based data sets, such as those collected by cancer registries. Restriction of analysis to records with complete data may yield inferences that are substantially different from those that would have been obtained had no data been missing. 'Naive' methods for handling missing data, such as restriction of the analysis to complete records or creation of a 'missing' category, have drawbacks that can invalidate the conclusions from the analysis. We offer a tutorial on modern methods for handling missing data in relative survival analysis.

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