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Updated: Oct 25, 2025

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The change in estimate method for selecting confounders: A simulation study.

Denis Talbot^1,2, Awa Diop^1,2,3, Mathilde Lavigne-Robichaud^1,2

¹Département de médecine sociale et préventive, Université Laval, Québec, Canada.

Statistical Methods in Medical Research

|August 9, 2021

Summary

The change in estimate method, popular for confounder selection in epidemiology, introduces bias and invalid confidence intervals. This simulation study questions its general utility for improving estimate precision or identifying true confounders.

Keywords:

Confounding epidemiologic methods modeling variable selection

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Area of Science:

Epidemiology
Biostatistics

Background:

The change in estimate method is widely recommended for confounder selection in epidemiology.
Concerns exist regarding the validity and performance of this approach compared to significance testing.

Purpose of the Study:

To compare different implementations of the change in estimate method through an extensive simulation study.
To evaluate the method's performance in a real-world data analysis.

Main Methods:

An extensive simulation study was conducted to assess various change in estimate implementations.
The methods were applied to estimate the body mass index-diastolic blood pressure association in a real cohort study.

Main Results:

All tested methods introduced significant bias and produced unreliable confidence intervals in certain scenarios.
Estimator accuracy (mean squared error) yielded mixed results, and no implementation reliably distinguished confounders from non-confounders.
Real data analysis showed no improvement in estimated standard errors with any implementation.

Conclusions:

The change in estimate method's benefits are questionable due to its tendency to introduce bias and its inability to provide valid confidence intervals or accurately identify confounders.
The method shows limited utility in improving estimate precision without compromising accuracy.