| pubmed-article:19858106 | rdf:type | pubmed:Citation | lld:pubmed |
| pubmed-article:19858106 | lifeskim:mentions | umls-concept:C1511726 | lld:lifeskim |
| pubmed-article:19858106 | lifeskim:mentions | umls-concept:C1711356 | lld:lifeskim |
| pubmed-article:19858106 | lifeskim:mentions | umls-concept:C0205257 | lld:lifeskim |
| pubmed-article:19858106 | lifeskim:mentions | umls-concept:C0150312 | lld:lifeskim |
| pubmed-article:19858106 | pubmed:issue | 1 | lld:pubmed |
| pubmed-article:19858106 | pubmed:dateCreated | 2010-2-8 | lld:pubmed |
| pubmed-article:19858106 | 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. | lld:pubmed |
| pubmed-article:19858106 | pubmed:grant | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:19858106 | pubmed:language | eng | lld:pubmed |
| pubmed-article:19858106 | pubmed:journal | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:19858106 | pubmed:citationSubset | IM | lld:pubmed |
| pubmed-article:19858106 | pubmed:status | MEDLINE | lld:pubmed |
| pubmed-article:19858106 | pubmed:month | Feb | lld:pubmed |
| pubmed-article:19858106 | pubmed:issn | 1464-3685 | lld:pubmed |
| pubmed-article:19858106 | pubmed:author | pubmed-author:MeiY WYW | lld:pubmed |
| pubmed-article:19858106 | pubmed:author | pubmed-author:RachetBernard... | lld:pubmed |
| pubmed-article:19858106 | pubmed:author | pubmed-author:ColemanMichel... | lld:pubmed |
| pubmed-article:19858106 | pubmed:author | pubmed-author:CarpenterJame... | lld:pubmed |
| pubmed-article:19858106 | pubmed:author | pubmed-author:ShackLorraine... | lld:pubmed |
| pubmed-article:19858106 | pubmed:issnType | Electronic | lld:pubmed |
| pubmed-article:19858106 | pubmed:volume | 39 | lld:pubmed |
| pubmed-article:19858106 | pubmed:owner | NLM | lld:pubmed |
| pubmed-article:19858106 | pubmed:authorsComplete | Y | lld:pubmed |
| pubmed-article:19858106 | pubmed:pagination | 118-28 | lld:pubmed |
| pubmed-article:19858106 | pubmed:meshHeading | pubmed-meshheading:19858106... | lld:pubmed |
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| pubmed-article:19858106 | pubmed:year | 2010 | lld:pubmed |
| pubmed-article:19858106 | pubmed:articleTitle | Modelling relative survival in the presence of incomplete data: a tutorial. | lld:pubmed |
| pubmed-article:19858106 | pubmed:affiliation | Cancer Research UK Cancer Survival Group, London School of Hygiene and Tropical Medicine, London, UK. ula.nur@lshtm.ac.uk | lld:pubmed |
| pubmed-article:19858106 | pubmed:publicationType | Journal Article | lld:pubmed |
| pubmed-article:19858106 | pubmed:publicationType | Research Support, Non-U.S. Gov't | lld:pubmed |
