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The validity of estimating heart disease reduction from a Framingham logistic equation
S Kinlay1, D O'Connell, D Evans
1Centre for Clinical Epidemiology and Biostatistics, Faculty of Medicine, University of Newcastle, NSW, Australia.
Insights
Estimating prevented heart disease events requires careful use of logistic equations. A method using percentage differences in simulated cholesterol reduction more accurately reflects real-world outcomes in cost-effectiveness studies.
Area of Science:
- Cardiovascular Epidemiology
- Biostatistics
Background:
- Estimating the effectiveness of interventions to lower blood cholesterol is crucial for public health.
- Accurate calculation of prevented heart disease events informs cost-effectiveness analyses.
Purpose of the Study:
- To compare two logistic equation methods for estimating heart disease events prevented by cholesterol reduction.
- To identify the most reliable method for use in cost-effectiveness studies.
Main Methods:
- Utilized data from an Australian population survey, selecting men meeting Lipid Research Clinics Coronary Primary Prevention Trial (LRC-CPPT) criteria.
- Calculated expected heart disease events over 7.4 years using a logistic equation with simulated cholesterol reductions.
- Compared two methods for calculating prevented events: absolute difference and percentage difference relative to the LRC-CPPT placebo group.
Main Results:
- The absolute difference method estimated 9.48 events prevented per 1000 men over 7.4 years.
- The percentage difference method estimated 13.66 events prevented per 1000 men over 7.4 years.
- The percentage difference method's estimate more closely aligned with the observed LRC-CPPT incidence of 17.10 events per 1000 men.
Conclusions:
- The logistic equation method using percentage differences provides a more accurate estimation of prevented heart disease events.
- This approach is recommended for cost-effectiveness studies evaluating cholesterol-lowering interventions.
Abstract:
We compared two ways in which a logistic equation could be used to estimate the number of heart disease events prevented after lowering blood cholesterol levels. Men were selected from an Australian population survey who met the entry criteria of the Lipid Research Clinics Coronary Primary Prevention Trial (LRC-CPPT). The numbers of heart disease events expected over 7.4 years were calculated from the logistic equation after reducing the men's blood cholesterol by the amounts achieved in the LRC-CPPT placebo and treatment groups (our simulated placebo and treatment groups). The number of events prevented was calculated as the absolute difference between the simulated groups (9.48 per 1000 men per 7.4 years) and the percentage difference of the simulated groups multiplied by the observed incidence rate in the LRC-CPPT placebo group (13.66 per 1000 men per 7.4 years). The second estimate was closer to that observed in the LRC-CPPT (17.10 per 1000 men per 7.4 years), and we recommend this approach in cost-effectiveness studies.