Literature DB >> 19196902

Model choice can obscure results in longitudinal studies.

Christopher H Morrell1, Larry J Brant, Luigi Ferrucci.   

Abstract

BACKGROUND: This article examines how different parameterizations of age and time in modeling observational longitudinal data can affect results.
METHODS: When individuals of different ages at study entry are considered, it becomes necessary to distinguish between longitudinal and cross-sectional differences to overcome possible selection biases.
RESULTS: Various models were fitted using data from longitudinal studies with participants with different ages and different follow-up lengths. Decomposing age into two components-age at entry into the study (first age) and the longitudinal follow-up (time) compared with considering age alone-leads to different conclusions.
CONCLUSIONS: In general, models using both first age and time terms performed better, and these terms are usually necessary to correctly analyze longitudinal data.

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Year:  2009        PMID: 19196902      PMCID: PMC2655028          DOI: 10.1093/gerona/gln024

Source DB:  PubMed          Journal:  J Gerontol A Biol Sci Med Sci        ISSN: 1079-5006            Impact factor:   6.053


  21 in total

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Authors:  J D Pearson; C H Morrell; L J Brant; P K Landis; J L Fleg
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8.  Modelling hearing thresholds in the elderly.

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Authors:  J D Pearson; C H Morrell; P K Landis; H B Carter; L J Brant
Journal:  Stat Med       Date:  1994 Mar 15-Apr 15       Impact factor: 2.373

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  39 in total

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9.  Real longitudinal data analysis for real people: building a good enough mixed model.

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