Linked Life Data

11315059

Source:http://linkedlifedata.com/resource/pubmed/id/11315059

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
4
pubmed:dateCreated
2001-4-20
pubmed:abstractText
Two approaches are described for estimating the prevalence of a disease that may have developed in a previous restricted age interval among persons of a given age at a particular calendar time. The prevalence for all those who ever developed disease is treated as a special case. The counting method (CM) obtains estimates of prevalence by dividing the estimated number of diseased persons by the total population size, taking loss to follow-up into account. The transition rate method (TRM) uses estimates of transition rates and competing risk calculations to estimate prevalence. Variance calculations are described for CM and TRM as well as for a variant of CM, called counting method times 10 (CM10), that is designed to yield more precise estimates than CM. We compare these three estimators in terms of precision and in terms of the underlying assumptions required to justify the methods. CM makes fewer assumptions but is typically ess precise than TRM or CM10. For common diseases such as breast cancer, CM may be preferred because its precision is excellent even though not as high as for TRM or CM10. For less common diseases, such as brain cancer, however, TRM or CM10 and other methods that make stabilizing assumptions may be preferred to CM.
pubmed:commentsCorrections
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Dec
pubmed:issn
0006-341X
pubmed:author
pubmed:issnType
Print
pubmed:volume
55
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1137-44
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
1999
pubmed:articleTitle
Two approaches for estimating disease prevalence from population-based registries of incidence and total mortality.
pubmed:affiliation
National Cancer Institute, Division of Cancer Epidemiology and Genetics, Bethesda, Maryland 20892-7244, USA. mit@cu.nih.gov
pubmed:publicationType
Journal Article, Comparative Study