Estimating the benefits of cholesterol lowering: are risk factors for coronary heart disease multiplicative?
Related Experiment Videos
1Division of Cardiology, Royal Victoria Hospital, Montreal, Canada.
Insights
Major coronary heart disease (CHD) risk factors like smoking and high blood pressure may have additive, not multiplicative, effects. Models assuming multiplicative interactions overestimate cholesterol-lowering benefits, especially for high-risk individuals.
Area of Science:
- Cardiovascular Medicine
- Biostatistics
- Epidemiology
Background:
- Major risk factors for coronary heart disease (CHD) include smoking, hypertension, and hypercholesterolemia.
- It is commonly assumed that these risk factors interact multiplicatively, meaning their combined effect is greater than the sum of their individual effects.
- Economic analyses often incorporate this multiplicative assumption when estimating the benefits of risk factor modification.
Purpose of the Study:
- To critically evaluate the assumption of multiplicative interactions among major CHD risk factors.
- To explain how standard logistic regression models can inherently predict multiplicative effects, irrespective of the underlying data.
- To determine the most appropriate statistical model for estimating the benefits of cholesterol lowering in CHD prevention.
Main Methods:
- Analysis of CHD death rates from the Multiple Risk Factor Intervention Trial (MRFIT) screenee population.
- Comparison of observed risk factor relationships against both additive and multiplicative models.
- Evaluation of predictive models based on the multiple logistic function.
Main Results:
- CHD death rates in the MRFIT population suggest an additive, rather than multiplicative, relationship between cholesterol and other major risk factors (smoking, hypertension).
- The inherent structure of simple multiple logistic regression models can lead to the prediction of multiplicative effects, potentially misrepresenting the true data.
- Models assuming additive risk, including interaction terms, provide a more accurate representation of the observed data.
Conclusions:
- The relationship between major CHD risk factors is likely closer to additive than multiplicative.
- Using models that incorporate additive risk with interaction terms is preferred for accurately estimating the benefits of cholesterol lowering.
- Mismodeling risk factor interactions can lead to inaccurate economic evaluations of CHD prevention strategies.
Abstract:
It is often stated that the major risk factors for coronary heart disease (CHD)--smoking, high blood pressure and high serum cholesterol--are not merely additive but act together such that each multiplies the effects of the others. Economic analyses in which the benefits of risk factor modification are estimated often reflect this. This paper explains how predictive models based on the simplest form of the multiple logistic function inevitably predict greater benefit from cholesterol lowering in those in whom other risk factors are adverse; this results from the model itself, rather than the data. CHD death rates from the screenee population of the Multiple Risk Factor Intervention Trial are examined: these suggest that the relationship between cholesterol and both other major risk factors is closer to additive than to multiplicative. When the benefits of cholesterol lowering are estimated, a model based on additive risk, specifying product ("interaction") terms, is to be preferred.