Linked Life Data

19399753

Source:http://linkedlifedata.com/resource/pubmed/id/19399753

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
5
pubmed:dateCreated
2010-4-19
pubmed:abstractText
Traditional sample size calculations for randomized clinical trials are based on the tests of hypotheses and depend on somewhat arbitrarily chosen factors, such as type I and II errors rates and the smallest clinically important difference. In response to this, many authors have proposed the use of methods based on the value of information as an alternative. Previous attempts have assumed perfect implementation, i.e. if current evidence favors the new intervention and no new information is sought or expected, all future patients will receive it. A framework is proposed to allow for this assumption to be relaxed. The profound effect that this can have on the optimal sample size and expected net gain is illustrated on two recent examples. In addition, a model for assessing the value of implementation strategies is proposed and illustrated.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
May
pubmed:issn
1099-1050
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
19
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
549-61
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
Optimal clinical trial design using value of information methods with imperfect implementation.
pubmed:affiliation
SickKids Research Institute and University of Toronto, Canada. andy@andywillan.com
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't