Linked Life Data

19858106

Source:http://linkedlifedata.com/resource/pubmed/id/19858106

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2010-2-8
pubmed:abstractText
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.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Feb
pubmed:issn
1464-3685
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
39
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
118-28
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
Modelling relative survival in the presence of incomplete data: a tutorial.
pubmed:affiliation
Cancer Research UK Cancer Survival Group, London School of Hygiene and Tropical Medicine, London, UK. ula.nur@lshtm.ac.uk
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't