ArticleDiagnostic and prognostic research2020
Informative missingness in electronic health record systems: the curse of knowing.
Article in Diagnostic and prognostic research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 82 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
82 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Maternal early warning scores shown to be methodologically weak and at high risk of bias.Journal of clinical epidemiology · 2025Pooled it
- Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review.BMJ (Clinical research ed.) · 2021Pooled it
- Informative presence and observation in routine health data: A review of methodology for clinical risk prediction.Journal of the American Medical Informatics Association : JAMIA · 2021Pooled it
- RWD-derived response in multiple myeloma.PloS one · 2023Trial
- Handling missingness in electronic health record laboratory data for secondary use: a decision-making process.BMJ open · 2026Review
- Observed Clinical and Operational Factors Are Associated with Biomarker Test Utilization in Respiratory Virus RT-PCR Testing: A Real-World Analysis of Inflammatory, Cardiac, and Thrombotic Biomarkers.Diagnostics (Basel, Switzerland) · 2026Article
- Clarifying Methodological Considerations in the Evaluation of Dev-NSRAS for Neonatal Pressure Injury Risk.Journal of clinical nursing · 2026Article
- Silent Pain or Silent Records? Pain Visibility, Documentation Ethics, and Nursing Management in Neurocritical Care.Nursing philosophy : an international journal for healthcare professionals · 2026Article
- Investigating respiratory infection as a post-COVID-19 condition using a large passive surveillance cohort from Track PCC.BMC infectious diseases · 2026Article
- Predicting Adverse ICU Outcomes from Admission-Time Frailty and Nocturnal Sleep Fragmentation Using Explainable AI.Journal of clinical medicine · 2026Article
- Performance of the Pre-Eclampsia Integrated Estimate of Risk-Machine Learning (PIERS-ML) model in a Kenyan cohort of women with pre-eclampsia- a retrospective test derivation validation study.BMC pregnancy and childbirth · 2026Article
- Predicting Treatment Failure With Sodium-Glucose Cotransporter-2 Inhibitors in People With Type 2 Diabetes: Novel Artificial Intelligence and Machine Learning Approach.JMIR diabetes · 2026Article
- Multi-source data integration through pooling and transfer learning improves generalizability and specialization of deep learning models for ICU mortality and length of stay prediction: a four-database external validation study.Critical care (London, England) · 2026Article
- A dynamic risk prediction framework for Alzheimer's disease and related dementias with interpretability.NPJ digital medicine · 2026Article
- Comparing methods for handling missing data in electronic health records for dynamic risk prediction of central-line associated bloodstream infection.BMC medical research methodology · 2026Article
- Development and external validation of a novel prediction model for the TraumaTriage App.European journal of trauma and emergency surgery : official publication of the European Trauma Society · 2026Article
- Powering RCTs for Marginal Effects With GLMs Using Prognostic Score Adjustment.Statistics in medicine · 2026Article
- Integrative analysis and imputation of multiple data streams via deep Gaussian processes.Bioinformatics advances · 2026Article
- Resources and applications of public biomedical data.Frontiers in bioinformatics · 2026Review
- Missingness-aware machine learning using routine laboratory data for distinguishing hepatitis from cirrhosis.Frontiers in genetics · 2026Article
22 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Electronic health records provide a potentially valuable data source of information for developing clinical prediction models. However, missing data are common in routinely collected health data and often missingness is informative. Informative missingness can be incorporated in a clinical prediction model, for example by including a separate category of a predictor variable that has missing values. The predictive performance of such a model depends on the transportability of the missing data mechanism, which may be compromised once the model is deployed in practice and the predictive value of certain variables becomes known. Using synthetic data, this phenomenon is explained and illustrated.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.