Source:http://linkedlifedata.com/resource/pubmed/id/16342923
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| Predicate | Object |
|---|---|
| rdf:type | |
| lifeskim:mentions | |
| pubmed:issue |
4
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| pubmed:dateCreated |
2005-12-13
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| pubmed:abstractText |
In fitting regression models, data analysts must often choose a model based on several candidate predictor variables which may influence the outcome. Most analysts either assume a linear relationship for continuous predictors, or categorize them and postulate step functions. By contrast, we propose to model possible non-linearity in the relationship between the outcome and several continuous predictors by estimating smooth functions of the predictors. We aim to demonstrate that a structured approach based on fractional polynomials can give a broadly satisfactory practical solution to the problem of simultaneously identifying a subset of 'important' predictors and determining the functional relationship for continuous predictors.
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| pubmed:language |
eng
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| pubmed:journal | |
| pubmed:citationSubset |
IM
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| pubmed:status |
MEDLINE
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| pubmed:issn |
0026-1270
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| pubmed:author | |
| pubmed:issnType |
Print
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| pubmed:volume |
44
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| pubmed:owner |
NLM
|
| pubmed:authorsComplete |
Y
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| pubmed:pagination |
561-71
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| pubmed:meshHeading |
pubmed-meshheading:16342923-Algorithms,
pubmed-meshheading:16342923-Computer Simulation,
pubmed-meshheading:16342923-Epidemiologic Research Design,
pubmed-meshheading:16342923-Humans,
pubmed-meshheading:16342923-Models, Statistical,
pubmed-meshheading:16342923-Prognosis,
pubmed-meshheading:16342923-Proportional Hazards Models,
pubmed-meshheading:16342923-Regression Analysis,
pubmed-meshheading:16342923-Risk Factors
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| pubmed:year |
2005
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| pubmed:articleTitle |
Building multivariable regression models with continuous covariates in clinical epidemiology--with an emphasis on fractional polynomials.
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| pubmed:affiliation |
Cancer Division, MRC Clinical Trials Unit, London, UK. patrick.royston@ctu.mrc.ac.uk
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| pubmed:publicationType |
Journal Article
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