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Correction for regression dilution bias using replicates from subjects with extreme first measurements
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Public Health and Caring Sciences. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Medicinska och farmaceutiska vetenskapsområdet, centrumbildningar mm , UCR-Uppsala Clinical Research center. (Geriatrics)
Regional Oncologic Center, University Hospital, Uppsala.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Medicinska och farmaceutiska vetenskapsområdet, centrumbildningar mm , UCR-Uppsala Clinical Research center.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Public Health and Caring Sciences. (Geriatrics)
2007 (English)In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 26, no 10, 2246-2257 p.Article in journal (Refereed) Published
Abstract [en]

The least squares estimator of the slope in a simple linear regression model will be biased towards zero when the predictor is measured with random error, i.e. intra-individual variation or technical measurement error. A correction factor can be estimated from a reliability study where one replicate is available on a subset of subjects from the main study. Previous work in this field has assumed that the reliability study constitutes a random subsample from the main study.We propose that a more efficient design is to collect replicates for subjects with extreme values on their first measurement. A variance formula for this estimator of the correction factor is presented. The variance for the corrected estimated regression coefficient for the extreme selection technique is also derived and compared with random subsampling. Results show that variances for corrected regression coefficients can be markedly reduced with extreme selection. The variance gain can be estimated from the main study data. The results are illustrated using Monte Carlo simulations and an application on the relation between insulin sensitivity and fasting insulin using data from the population-based ULSAM study.In conclusion, an investigator faced with the planning of a reliability study may wish to consider an extreme selection design in order to improve precision at a given number of subjects or alternatively decrease the number of subjects at a given precision.

Place, publisher, year, edition, pages
2007. Vol. 26, no 10, 2246-2257 p.
Keyword [en]
regression dilution bias, reliability study, extreme selection, corrected regression coefficient, insulin sensitivity
National Category
Medical and Health Sciences
Identifiers
URN: urn:nbn:se:uu:diva-10991DOI: 10.1002/sim.2698ISI: 000245965200008PubMedID: 16969892OAI: oai:DiVA.org:uu-10991DiVA: diva2:38759
Available from: 2007-05-08 Created: 2007-05-08 Last updated: 2017-12-11Bibliographically approved
In thesis
1. Measurement Variability Related to Insulin Secretion and Sensitivity: Assessment and Implications in Epidemiological Studies
Open this publication in new window or tab >>Measurement Variability Related to Insulin Secretion and Sensitivity: Assessment and Implications in Epidemiological Studies
2009 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

There is a growing interest in random measurement variability of biological variables. In regression models, such variability of the predictors yields biased estimators of coefficients (regression dilution bias). The objectives of this thesis were to develop an efficient method to correct for such bias, to reveal the relative importance of insulin sensitivity and insulin secretion, corrected for regression dilution bias, on glucose tolerance, and to explore the seasonal nature of the variability of insulin sensitivity.

A reliability study is often designed to randomly select subjects from the main study. Our idea was to collect replicates for subjects with extreme values on their first measurement. The extreme selection design, in combination with maximum likelihood estimation, resulted in an efficient estimator of a corrected regression coefficient in a simple linear regression model. Results were presented theoretically and with an application: The relation between insulin sensitivity and fasting insulin in Uppsala Longitudinal Study of Adult Men (ULSAM) where the extreme selection design decreased the standard error of the estimated regression coefficient with 28 per cent compared with the random sampling design.

We estimated the partial longitudinal effects of the predictors insulin sensitivity and insulin secretion, corrected for regression dilution bias, on glucose tolerance in ULSAM. The effects of the predictors, when corrected, were similar.

Insulin sensitivity in ULSAM increased during summer and decreased during winter and insulin secretion exposed opposite variation keeping glucose homeostasis nearly constant. Insulin sensitivity was related to outdoor temperature.

In summary, we developed a cost-efficient reliability design for correction for regression dilution bias. Insulin sensitivity and insulin secretion had similar longitudinal effects on glucose tolerance, which implies that interventions aimed at these targets are equally important. Further, we revealed the seasonal nature of variations of insulin sensitivity and insulin secretion. This result has implications on glycaemic control in diabetic patients.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2009. 67 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Medicine, ISSN 1651-6206 ; 440
National Category
Geriatrics
Research subject
Geriatrics
Identifiers
urn:nbn:se:uu:diva-99636 (URN)978-91-554-7469-0 (ISBN)
Public defence
2009-04-29, Enghoffsalen, Akademiska sjukhuset, Uppsala, 13:15 (Swedish)
Opponent
Supervisors
Available from: 2009-04-07 Created: 2009-03-18 Last updated: 2012-07-26Bibliographically approved

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Berglund, LarsLindbäck, JohanZethelius, Björn

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