Linked Life Data

11113952

Source:http://linkedlifedata.com/resource/pubmed/id/11113952

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
23
pubmed:dateCreated
2001-1-26
pubmed:abstractText
For many biomarkers, the range (L,R) over which they can be quantified is restricted by technical limitations, leading to some measurements that are left or right censored. However, despite the widespread availability of statistical methods for the analysis of censored data, many studies use an imputed value for censored measurements (for example, replacing a value <L by L, or by L/2). Commonly, an analysis that ignores such imputation is then used. In clinical trials, this leads to bias and a loss of power in evaluating treatment effects. In this paper, a review of appropriate statistical methods for parametric and non-parametric analysis of such measurements is presented. This includes methods for situations in which baseline measurements are available. New results concerning design issues such as sample size determination are also presented. The paper is illustrated using two examples of studies that included censored measurements of viral load in HIV-infected subjects.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Dec
pubmed:issn
0277-6715
pubmed:author
pubmed:issnType
Print
pubmed:day
15
pubmed:volume
19
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
3171-91
pubmed:dateRevised
2007-11-15
pubmed:meshHeading
pubmed:year
2000
pubmed:articleTitle
Analysis and design issues for studies using censored biomarker measurements with an example of viral load measurements in HIV clinical trials.
pubmed:affiliation
Department of Biostatistics, Harvard School of Public Health, 655 Huntington Avenue, Boston, Massachusetts 02115, USA.
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, P.H.S., Review