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Extent of regression dilution for established and novel coronary risk factors: results from the British Regional
Jonathan R Emberson1, Peter H Whincup, Richard W Morris
1Department of Primary Care and Population Sciences, Royal Free and University College Medical School, London, NW3 2PF, UK. j.emberson@pcps.ucl.ac.uk
Insights
Using single measurements underestimates coronary heart disease (CHD) risk factors. Repeated measurements are crucial for accurate CHD risk assessment and prevention strategies.
Area of Science:
- Cardiovascular Epidemiology
- Biostatistics
Background:
- Measurement error in risk factors causes underestimation of disease associations (regression dilution bias).
- Established and novel risk factors for coronary heart disease (CHD) are examined for this bias.
- Consequences for CHD prevention are considered.
Purpose of the Study:
- To quantify regression dilution bias for CHD risk factors.
- To assess the impact of this bias on CHD risk factor importance and prevention.
Main Methods:
- Prospective study of middle-aged British men followed for 20 years.
- Repeated measurements of blood lipids, blood pressure, insulin, homocysteine, and hemostatic factors at various intervals.
Main Results:
- Single baseline measures significantly underestimate risk associations over time.
- Total cholesterol showed 47% underestimation; diastolic blood pressure showed 76% underestimation in the third decade.
- Regression dilution bias can misrepresent the importance of even precisely measured risk markers.
Conclusions:
- Single baseline measures in prospective studies greatly underestimate the importance of CHD risk factors.
- Accounting for regression dilution is essential for accurate estimation of usual exposure levels and disease risk.
- Failure to address regression dilution leads to misinterpretation of CHD risk factor significance.
Background:
Imprecision in measurement of risk factors leads to underestimation of associations with disease outcomes (through regression dilution bias). We examine the extent of this bias for established and novel risk factors for coronary heart disease (CHD) and consider the consequences for CHD prevention.
Design:
Prospective cardiovascular study of middle-aged British men followed up over 20 years.
Methods:
Repeated measurements of blood lipids, blood pressure and insulin were available at intervals of 1 week, 4, 16 and 20 years; repeated measurements of homocysteine and haemostatic factors were available over 1 week and 4 years.
Results:
The use of single baseline measures of both established and novel risk factors in analysis results in marked underestimation of risk associations, increasing over time. The use of a single baseline measurement of total cholesterol results in a 47% (95% confidence interval 44 to 50%) underestimation of its association with CHD risk during the third decade of follow-up; for diastolic blood pressure the corresponding underestimation is 76% (95% confidence interval 73 to 78%). Ignoring the consequences of regression dilution can also lead to error in the assessment of other risk markers, even those measured precisely.
Conclusions:
The importance of risk factors for CHD can be greatly underestimated by using a single baseline measure in prospective study analyses. Studies that wish to estimate associations between disease risk and usual exposure levels need to take regression dilution effects into account. Failure to do so can lead to serious misinterpretation of the importance of CHD risk factors.
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