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Comparisons of confidence intervals for attributable risk

H M Leung, L L Kupper

|June 1, 1981

Summary

This study introduces a logarithmic transformation (LT) method for calculating confidence intervals for attributable risk. The LT-based interval is narrower than the maximum likelihood (ML)-based interval when attributable risk is between 0.21 and 0.79.

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Area of Science:

Epidemiology
Biostatistics
Public Health

Background:

Accurate estimation of attributable risk is crucial in epidemiology for understanding the impact of risk factors.
Existing methods for calculating confidence intervals for attributable risk have limitations in certain scenarios.

Purpose of the Study:

To develop and evaluate a novel method for computing confidence intervals for attributable risk.
To compare the performance of the proposed logarithmic transformation (LT)-based interval with the maximum likelihood (ML)-based interval.

Main Methods:

Confidence intervals for attributable risk were derived using a transformation of the confidence interval for the natural logarithm.
The method involves the natural logarithm of the product of exposure probability and (risk ratio - 1).
Computer simulations were used to assess interval widths across different epidemiologic study designs.

Main Results:

The logarithmic transformation (LT)-based confidence interval is narrower than the maximum likelihood (ML)-based interval when the estimated attributable risk falls between 0.21 and 0.79.
This finding holds true for three common epidemiologic study designs.
Simulation results confirm the superiority of the LT-based interval under the specified condition.

Conclusions:

The logarithmic transformation method provides a more precise confidence interval for attributable risk within a specific range.
This approach offers a valuable alternative for epidemiologists when assessing the impact of risk factors.
The LT-based method is applicable across various standard epidemiologic study designs.

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