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Related Experiment Video

Updated: Jun 12, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Relative excess risk due to interaction: resampling-based confidence intervals.

Lei Nie¹, Haitao Chu, Feng Li

¹Division of Biometrics IV, Office of Biometrics/OTS/CDER/FDA, Silver Spring, MD 20993-0002, USA. lei.nie@fda.hhs.gov

Epidemiology (Cambridge, Mass.)

|May 27, 2010

Summary

New bootstrap methods with continuity correction improve confidence intervals for relative excess risk due to interaction (RERI) in epidemiology. These methods are particularly effective when dealing with sparse data, enhancing the reliability of joint exposure effect estimates.

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Area of Science:

Epidemiology
Biostatistics
Statistical modeling

Background:

Relative excess risk due to interaction (RERI) quantifies joint effects of two exposures in epidemiological studies.
Calculating confidence intervals (CIs) for RERI is challenging, especially with sparse cell counts in the data.

Purpose of the Study:

To propose and evaluate novel nonparametric and parametric bootstrap methods for constructing RERI confidence intervals.
To assess the performance of these bootstrap methods, particularly with a continuity correction, in scenarios with sparse data.

Main Methods:

Development of nonparametric and parametric bootstrap methods incorporating a continuity correction for RERI CI calculation.
Comparison of proposed bootstrap methods against existing methods using three empirical examples.
Evaluation through Monte Carlo simulations to assess CI coverage and length under varying cell sparsity.

Main Results:

Bootstrap methods with continuity correction provide acceptable CIs for RERI, especially when cell counts are sparse.
When cell counts are not sparse, bootstrap methods offer acceptable CI coverage and length, though computationally intensive.
The continuity correction is crucial for bootstrap methods to handle potential zero cells in resampled data.

Conclusions:

Proposed bootstrap methods with continuity correction offer a robust approach for estimating RERI CIs in epidemiological research.
These methods are particularly valuable for improving the reliability of joint exposure effect analysis when data are sparse.