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Related Experiment Video

Updated: Dec 6, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Accounting for Expected Adjusted Effect.

Kimmo Sorjonen¹, Bo Melin¹, Michael Ingre^1,2,3

¹Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.

Frontiers in Psychology

|October 12, 2020

Summary

Adjusting for confounders may not prevent false findings, especially with large sample sizes and unreliable confounder measurement. New equations can help quantify and mitigate this risk in regression analysis.

Keywords:

adjustment confounder expected effect regression analysis reliability simulation type 1-error

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Area of Science:

Statistics
Epidemiology
Biostatistics

Background:

Confounder adjustment is crucial in statistical analysis to prevent biased results.
However, adjustment does not always eliminate spurious findings or Type 1 errors.

Purpose of the Study:

To investigate the conditions under which confounder adjustment fails in traditional regression methods.
To present methods for calculating and potentially attenuating the risk of spurious findings.

Main Methods:

A simulation study was conducted using traditional regression methods.
The study examined the impact of sample size, confounder reliability, and predictor/outcome reliability.

Main Results:

The risk of spurious findings is amplified by large sample sizes, low confounder measurement reliability, and high predictor/outcome reliability.
Equations were derived to calculate the expected adjusted effect and required confounder reliability.

Conclusions:

Confounder adjustment in regression analysis can be unreliable under specific conditions.
The derived equations offer a way to assess and potentially reduce the risk of Type 1 errors in observational studies.