Linked Life Data

17112339

Source:http://linkedlifedata.com/resource/pubmed/id/17112339

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2007-5-17
pubmed:abstractText
Standard inference procedures for regression analysis make assumptions that are rarely satisfied in practice. Adjustments must be made to insure the validity of statistical inference. These adjustments, known for many years, are used routinely by some health researchers but not by others. We review some of these methods and give an example of their use in a health services study for a continuous and a count outcome. For the continuous outcome, we describe re-transformation using the smear factor, accounting for missing cases via multiple imputation and attrition weights and improving results with bootstrap methods. For the count outcome, we describe zero inflated Poisson and negative binomial models and the two-part model to account for overabundance of zero values. Recent advances in computing and software development have produced user-friendly computer programs that enable the data analyst to improve prediction and inference based on regression analysis.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
0163-7525
pubmed:author
pubmed:issnType
Print
pubmed:volume
28
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
95-111
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
Methods for improving regression analysis for skewed continuous or counted responses.
pubmed:affiliation
School of Public Health, University of California-Los Angeles, CA 90095-1772, USA. afifi@ucla.edu
pubmed:publicationType
Journal Article, Comparative Study