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Confidence Intervals

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An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a sample proportion. However, unlike the point estimate which is a single value, the confidence interval contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
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An R-Based Landscape Validation of a Competing Risk Model

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Published on: September 16, 2022

Comparison of confidence intervals for adjusted attributable risk estimates under multinomial sampling.

Andrea Lehnert-Batar¹, Annette Pfahlberg, Olaf Gefeller

¹Department of Medical Informatics, Biometry and Epidemiology, Friedrich-Alexander-University Erlangen-Nuremberg, Waldstrasse 6, 91054 Erlangen, Germany. Andrea.Lehnert-Batar@imbe.imed.uni-erlangen.de

Biometrical Journal. Biometrische Zeitschrift

|November 11, 2006

Summary

Computer-intensive methods like bootstrap and jackknife provide more accurate confidence intervals for adjusted attributable risk than the delta method. These methods are crucial for reliable risk factor analysis in epidemiology.

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Area of Science:

Epidemiology
Biostatistics

Background:

Adjusted attributable risk quantifies population-level risk factor importance.
Current methods often use the delta method for variance estimation.
The delta method may underestimate standard errors, biasing confidence intervals.

Purpose of the Study:

To compare the accuracy of confidence intervals for adjusted attributable risk.
To evaluate computer-intensive methods (bootstrap, jackknife) against the delta method.

Main Methods:

Extensive Monte Carlo simulations were performed.
A real-world cohort study in cardiovascular disease epidemiology was analyzed.
Confidence intervals from bootstrap, jackknife, and delta methods were compared.

Main Results:

Bootstrap and jackknife confidence intervals demonstrated superior performance.
Intervals based on asymptotic theory (delta method) showed underestimation.
Computer-intensive methods yielded more reliable estimates.

Conclusions:

Bootstrap and jackknife methods are recommended for calculating confidence intervals for adjusted attributable risk.
These methods offer improved accuracy over traditional asymptotic approaches.
Specific variants of computer-intensive methods are best suited for different epidemiological scenarios.