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Methods and reagent-lot comparisons by regression analysis: False positives.

William Sadler¹

¹Nuclear Medicine Department, Christchurch Hospital, Christchurch, New Zealand.

Annals of Clinical Biochemistry

|May 12, 2026

Summary

This summary is machine-generated.

Joint confidence regions (CRs) offer more reliable control of false positive rates in regression analysis than confidence intervals (CIs). CRs maintained expected false positive rates, unlike CIs which were range ratio dependent.

Keywords:

Regression bias confidence interval false positives joint confidence region methods comparisons reagent-lot comparisons

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Area of Science:

Statistical analysis
Regression modeling
Biostatistics

Background:

Significance levels in statistical analysis determine expected false positive rates.
Uncontrolled false positives can lead to misleading conclusions, particularly in sample size determination.
A simulation study was conducted to evaluate false positives in regression analysis.

Purpose of the Study:

To investigate the occurrence of false positives in regression analysis.
To compare the reliability of confidence intervals (CIs) and confidence regions (CRs) in controlling false positives.
To assess the impact of range ratios on false positive rates.

Main Methods:

Deming regression analysis was applied to randomly generated X, Y pairs with no underlying bias.
Simulations included equal X, Y errors, unequal parallel errors, and non-parallel errors.
Various maximum:minimum range ratios (2:1, 10:1, 2667:1) were examined.
False positives were identified by the failure of 95% CIs or joint 95% CRs to enclose target values.

Main Results:

False positive rates using CIs varied from 6-10% and were dependent on the range ratio.
False positive rates assessed by CRs remained stable and close to the expected 5% across all conditions.
CRs demonstrated superior control over false positives compared to CIs.

Conclusions:

Joint confidence regions (CRs) are more reliable than confidence intervals (CIs) for controlling false positive rates in regression analysis.
CRs should be the preferred method for regression analysis in comparative studies, such as methods and reagent-lot comparisons.
The computational program used in this study is publicly accessible.