Linked Life Data

8063983

Source:http://linkedlifedata.com/resource/pubmed/id/8063983

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
1994-9-20
pubmed:abstractText
An important, frequent, and unresolved problem in treatment research is deciding how to analyze outcome data when some of the data are missing. After a brief review of alternative procedures and the underlying models on which they are based, an approach is presented for dealing with the most common situation--comparing the outcome results in a 2-group, randomized design in the presence of missing data. The proposed analysis is based on the concept of "modeling our ignorance" by examining all possible outcomes, given a known number of missing results with a binary outcome, and then describing the distribution of those results. This method allows the researcher to define the range of all possible results that could have resulted had the missing data been observed. Extensions to more complex designs are discussed.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jun
pubmed:issn
0022-006X
pubmed:author
pubmed:issnType
Print
pubmed:volume
62
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
569-75
pubmed:dateRevised
2009-11-11
pubmed:meshHeading
pubmed:year
1994
pubmed:articleTitle
Methods for the analysis of binary outcome results in the presence of missing data.
pubmed:affiliation
San Francisco Department of Veterans Administration Medical Center, California 94121.
pubmed:publicationType
Journal Article, Comparative Study, Research Support, U.S. Gov't, P.H.S.