ArticleJournal of clinical epidemiology2023
Complete case logistic regression with a dichotomised continuous outcome led to biased estimates.
Article in Journal of clinical epidemiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The trial behind it
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Who cites it
5 citing papers in PubMed.
- Who's at risk for emergent depression years later? Predictive modeling in a nine-year longitudinal cohort.BMC psychiatry · 2026Article
- Machine learning models to predict 6-month mortality risk in home-based hospice patients with advanced cancer.Asia-Pacific journal of oncology nursing · 2025Article
- ProWaste for proactive urban waste management using IoT and machine learning.Scientific reports · 2025Article
- Convolutional neural networks combined with classification algorithms for the diagnosis of periodontitis.Oral radiology · 2024Article
- Predicting health insurance uptake in Kenya using Random Forest: An analysis of socio-economic and demographic factors.PloS one · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
objectivesTo investigate whether a complete case logistic regression gives a biased estimate of the exposure odds ratio (OR) if missingness depends on a continuous outcome, but a binary version is used for analysis; to examine whether any bias could be reduced by including a misclassified form of the incomplete outcome as an auxiliary variable in multiple imputation (MI). STUDY DESIGN AND
settingAnalytical investigation, simulation study, and data from a UK cohort.
resultsThere was bias in the exposure OR when the probability of being a complete case was independently associated with the exposure and (continuous) outcome but this was generally small unless the association with the outcome was strong. Where exposure and (continuous) outcome interacted in their effect on this probability, the bias was large, particularly at high levels of missing data. Inclusion of the auxiliary variable resulted in important bias reductions when this had high sensitivity and specificity.
conclusionThe robustness of logistic regression to missing data is not maintained when the outcome is a binary version of an underlying continuous measure, but the bias will be small unless the association between the continuous outcome and missingness is strong.
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Registered trials
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