ArticleJournal of healthcare engineering2017
The Impact of Diagnostic Code Misclassification on Optimizing the Experimental Design of Genetic Association Studies.
Article in Journal of healthcare engineering, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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.
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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.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prognostic impact of sleep-disordered breathing on mortality and cardiovascular events in renal dialysis: A meta-analysis.Biomolecules & biomedicine · 2025Pooled it
- Integrating Polygenic Scores with Clinical, Lifestyle, and Social Risk Factors to Improve Heart Failure Risk Prediction.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2026Article
- TriNetX and Real-World Evidence: A Critical Review of Its Strengths, Limitations, and Bias Considerations in Clinical Research.ASIDE internal medicine · 2025Article
- Exome sequence analysis identifies rare coding variants associated with a machine learning-based marker for coronary artery disease.Nature genetics · 2024Article
- Animal-Encounter Fatalities, United States, 1999-2016: Cause of Death and Misreporting.Public health reports (Washington, D.C. : 1974)Article
Corrections and comments
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Authors and funding
1 author.
Funding
Abstract
Diagnostic codes within electronic health record systems can vary widely in accuracy. It has been noted that the number of instances of a particular diagnostic code monotonically increases with the accuracy of disease phenotype classification. As a growing number of health system databases become linked with genomic data, it is critically important to understand the effect of this misclassification on the power of genetic association studies. Here, I investigate the impact of this diagnostic code misclassification on the power of genetic association studies with the aim to better inform experimental designs using health informatics data. The trade-off between (i) reduced misclassification rates from utilizing additional instances of a diagnostic code per individual and (ii) the resulting smaller sample size is explored, and general rules are presented to improve experimental designs.
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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.