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IJE Advance Access published online on July 28, 2005
International Journal of Epidemiology, doi:10.1093/ije/dyi148
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Published by Oxford University Press on behalf of the International Epidemiological Association © The Author 2005; all rights reserved.
Accepted June 21, 2005

Original paper

The effect of measurement error in risk factors that change over time in cohort studies: do simple methods overcorrect for ‘regression dilution’?

Chris Frost 1* and Ian R. White 2

1 Department of Epidemiology and Population Health, Medical Statistics Unit, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
2 MRC Biostatistics Unit, Institute of Public Health, University Forvie Site, Robinson Way, Cambridge, UK

* To whom correspondence should be addressed.
Chris Frost, E-mail: chris.frost{at}lshtm.ac.uk


  Abstract

Background The attenuation of the relationship between disease and a risk factor subject to error through ‘regression dilution’ is well recognized, and researchers often make attempts to adjust for its effects. However, the adjustment methods most often adopted in cohort studies make an implicit assumption that the relationship is driven exclusively by current error-free levels of the risk factor and not by past levels. Here we investigate the bias that is introduced if this assumption is invalid.

Methods We model disease risk at a particular time in terms of error-free levels of the risk factor at that time and in past periods, and summarize the ‘life-course’ risk factor-disease relationship using crude current level, history adjusted current level and lifetime level associations. Using systolic blood pressure data from the Framingham Heart Study we show the impact of measurement error on these associations and investigate the biases that can occur with simple correction methods.

Results A simple ‘ratio of ranges’ type correction factor overestimates the lifetime level association by 29% in the presence of a relatively modest dependency of current risk on past levels (levels 5 years ago half as predictive of current risk as current levels).

Conclusions Simple methods of correction for regression dilution bias can lead to substantial overcorrection if the risk factor-disease relationship is not short term.

Keywords: Regression dilution bias; measurement error; cohort studies; ratio of ranges; Framingham study; blood pressure.
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Commentary: Over-correction for regression dilution bias? Not for blood pressure vs coronary heart disease
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