ArticleInternational journal of epidemiology2026
Quantifying and adjusting for selection biases in the Norwegian Mother, Father and Child Cohort Study using population-wide individual-level registry information.
Article in International journal of epidemiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
2 citing papers in PubMed.
- Triangulating evidence for genetic and environmental components of associations between parental behaviours and aggressive behaviour in children.Nature communications · 2026Article
- Estimating returns to education using the genetic lottery.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
backgroundSelective participation in research studies hampers researchers' ability to draw valid and generalizable inferences from analyses. Quantifying and adjusting for selective participation are desirable but can be challenging given the paucity of data for non-participants.
methodsWe used individual-level information from population registers to predict baseline and continued participation in the Norwegian Mother, Father and Child Cohort Study (MoBa). Inverse probability weights were computed from logistic regression models with elastic-net regularization. We predicted selective participation and attrition in 296 987 mothers, of whom 29% returned the first MoBa questionnaire and 12.5% returned a follow-up questionnaire 8 years later. To quantify bias, we computed sample characteristics and exposure-outcome associations in the target population and stratified samples. To compare approaches for adjusting for bias, we computed weighted sample estimates by using three sets of weights.
resultsUnweighted sample estimates were systematically different from target-population values, indicating bias due to selective participation. Baseline participation weights substantially reduced the impact of selection bias on mean values by 93% and associations by 75%. Attrition weights reduced attrition bias on mean values by 78% and associations by 50% but were less effective for reducing participation bias (22% and 14% respectively).
conclusionIn this sample, characteristics and effect estimates are substantially different from target-population values. Participation weights were relatively effective at reducing bias due to selective participation but attrition weights were not. Estimates from studies that use attrition weights may still contain non-negligible selection bias-particularly if the baseline sample is not representative of the target population. Future studies should prioritize opportunities for deriving sampling weights for participation as well as attrition.
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Registered trials
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