Linked Life Data

12090408

Source:http://linkedlifedata.com/resource/pubmed/id/12090408

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
2002-7-1
pubmed:abstractText
Statistical procedures for missing data have vastly improved, yet misconception and unsound practice still abound. The authors frame the missing-data problem, review methods, offer advice, and raise issues that remain unresolved. They clear up common misunderstandings regarding the missing at random (MAR) concept. They summarize the evidence against older procedures and, with few exceptions, discourage their use. They present, in both technical and practical language, 2 general approaches that come highly recommended: maximum likelihood (ML) and Bayesian multiple imputation (MI). Newer developments are discussed, including some for dealing with missing data that are not MAR. Although not yet in the mainstream, these procedures may eventually extend the ML and MI methods that currently represent the state of the art.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jun
pubmed:issn
1082-989X
pubmed:author
pubmed:issnType
Print
pubmed:volume
7
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
147-77
pubmed:dateRevised
2007-11-15
pubmed:meshHeading
pubmed:year
2002
pubmed:articleTitle
Missing data: our view of the state of the art.
pubmed:affiliation
Department of Statistics and the Methodology Center, Pennsylvania State University, University Park 16802, USA. jls@stat.psu.edu
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, P.H.S.