Linked Life Data

16981181

Source:http://linkedlifedata.com/resource/pubmed/id/16981181

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PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
9
pubmed:dateCreated
2007-4-30
pubmed:abstractText
For diseases with some level of associated mortality, the case fatality ratio measures the proportion of diseased individuals who die from the disease. In principle, it is straightforward to estimate this quantity from individual follow-up data that provides times from onset to death or recovery. In particular, in a competing risks context, the case fatality ratio is defined by the limiting value of the sub-distribution function, F(1)(t) = Pr(T <or=t and J = 1), associated with death, as t --> infinity, where T denotes the time from onset to death (J = 1) or recovery (J = 2). When censoring is present, however, estimation of F(1)(infinity) is complicated by the possibility of little information regarding the right tail of F(1), requiring use of estimators of F(1)(t(*)) or F(1)(t(*))/(F(1)(t(*))+F(2)(t(*))) where t(*) is large, with F(2)(t) = Pr(T <or=t and J = 2) being the analogous sub-distribution function associated with recovery. With right censored data, the variability of such estimators increases as t(*) increases, suggesting the possibility of using estimators at lower values of t(*) where bias may be increased but overall mean squared error be smaller. These issues are investigated here for non-parametric estimators of F(1) and F(2). The ideas are illustrated on case fatality data for individuals infected with Severe Acute Respiratory Syndrome (SARS) in Hong Kong in 2003.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Apr
pubmed:issn
0277-6715
pubmed:author
pubmed:issnType
Print
pubmed:day
30
pubmed:volume
26
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1982-98
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
Non-parametric estimation of the case fatality ratio with competing risks data: an application to Severe Acute Respiratory Syndrome (SARS).
pubmed:affiliation
Division of Biostatistics and Department of Statistics, University of California, Berkeley, USA. jewell@stat.berkeley.edu
pubmed:publicationType
Journal Article, Comparative Study, Research Support, Non-U.S. Gov't