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Updated: Jul 2, 2026
08:04
Measuring Sensitivity to Viewpoint Change with and without Stereoscopic Cues
Published on: December 4, 2013
When it is better to estimate a slope with only one point
1INSERM U897 Epidemiology & Biostatistics, ISPED, Université Victor Segalen Bordeaux 2, 146 rue Léo Saignat 33076 Bordeaux Cedex, France. rodolphe.thiebaut@isped.u-bordeaux2.fr
QJM : Monthly Journal of the Association of Physicians
|August 12, 2008
Summary
Excluding participants with single biomarker measurements can yield biased results. A standard statistical method effectively addresses this issue, improving data analysis accuracy.
Area of Science:
- Biostatistics
- Biomarker Analysis
- Clinical Research Methodology
Background:
- Biomarker studies often require longitudinal data.
- Excluding participants with single measurements is a common but potentially flawed practice.
Purpose of the Study:
- To highlight the statistical limitations of excluding participants with single biomarker measurements.
- To introduce a robust statistical method for handling incomplete biomarker data.
Main Methods:
- Statistical modeling to assess bias and imprecision.
- Discussion of a standard statistical approach for mixed-effects models.
Main Results:
- Exclusion of single measurements can lead to imprecise and biased biomarker change estimates.
- The discussed statistical method provides more accurate and reliable results.
Conclusions:
- Researchers should reconsider excluding participants with single biomarker measurements.
- Standard statistical methods can effectively incorporate all available participant data for more robust biomarker analysis.
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