Linked Life Data

15723426

Source:http://linkedlifedata.com/resource/pubmed/id/15723426

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
10
pubmed:dateCreated
2005-5-12
pubmed:abstractText
In this paper, we propose a model-based approach to detect and adjust for observable selection bias in a randomized clinical trial with two treatments and binary outcomes. The proposed method was evaluated using simulations of a randomized block design in which the investigator favoured the experimental treatment by attempting to enroll stronger patients (with greater probability of treatment success) if the probability of the next treatment being experimental was high, and enroll weak patients (with less probability of treatment success) if the probability of the next treatment being experimental was low. The method allows not only testing for the presence of observable selection bias, but also testing for a difference in treatment effects, adjusting for possible selection bias.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
May
pubmed:issn
0277-6715
pubmed:author
pubmed:issnType
Print
pubmed:day
30
pubmed:volume
24
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1537-46
pubmed:dateRevised
2007-11-15
pubmed:meshHeading
pubmed:year
2005
pubmed:articleTitle
Adjusting for observable selection bias in block randomized trials.
pubmed:affiliation
Department of Biostatistics, The University of North Carolina at Chapel Hill, NC 27599, USA. aivanova@bios.unc.edu
pubmed:publicationType
Journal Article