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Updated: Apr 6, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Correction factors for self-selection when evaluating screening programmes
Claudia Spix1, Frank Berthold2, Barbara Hero2
1University Medical Center Mainz, IMBEI, German Childhood Cancer Registry, Mainz, Germany Clauspix@uni-mainz.de.
Objective:
In screening programmes there is recognized bias introduced through participant self-selection (the healthy screenee bias). Methods used to evaluate screening programmes include Intention-to-screen, per-protocol, and the "post hoc" approach in which, after introducing screening for everyone, the only evaluation option is participants versus non-participants. All methods are prone to bias through self-selection. We present an overview of approaches to correct for this bias.
Methods:
We considered four methods to quantify and correct for self-selection bias. Simple calculations revealed that these corrections are actually all identical, and can be converted into each other. Based on this, correction factors for further situations and measures were derived. The application of these correction factors requires a number of assumptions.
Results:
Using as an example the German Neuroblastoma Screening Study, no relevant reduction in mortality or stage 4 incidence due to screening was observed. The largest bias (in favour of screening) was observed when comparing participants with non-participants.
Conclusions:
Correcting for bias is particularly necessary when using the post hoc evaluation approach, however, in this situation not all required data are available. External data or further assumptions may be required for estimation.
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