ReviewJournal of the American Medical Informatics Association : JAMIA2019
A primer on quantitative bias analysis with positive predictive values in research using electronic health data.
Review in Journal of the American Medical Informatics Association : JAMIA, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
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
17 citing papers in PubMed.
- Risk of opioid overdose associated with concurrent use of hydrocodone and CYP3A4-inhibiting calcium channel blockers: a population-based cohort study.American journal of epidemiology · 2025Article
- Validity of diagnostic codes used to ascertain maternal injuries during pregnancy.American journal of epidemiology · 2025Article
- A Comparative Real-World Study Evaluating the Safety of Immune Globulin Infusion (Human) 10% Solution and Other Intravenous Immunoglobulin Therapies for the Treatment of Chronic Inflammatory Demyelinating Polyradiculoneuropathy.Pharmacoepidemiology and drug safety · 2025Article
- Assessing the Validity of Claims-Based Diagnostic Codes for Psychotic and Affective Disorders and the Influence of the Coding Transition from the ICD-9 to the ICD-10 in Taiwan's National Health Insurance Research Database.Clinical epidemiology · 2025Article
- Development and validation of identification algorithms for five autoimmune diseases using electronic health records: a retrospective cohort study in China.Frontiers in immunology · 2025Article
- Linking clinical trial participants to their U.S. real-world data through tokenization: A practical guide.Contemporary clinical trials communications · 2024Article
- Inherent Bias in Electronic Health Records: A Scoping Review of Sources of Bias.medRxiv : the preprint server for health sciences · 2024Article
- Identification of opioid use disorder using electronic health records: Beyond diagnostic codes.Drug and alcohol dependence · 2023Article
- Tdap vaccination during pregnancy and risk of chorioamnionitis and related infant outcomes.Vaccine · 2023Observational
- Examination of validity of identifying congenital heart disease from hospital discharge data without a gold standard: Using a data linkage approach.Paediatric and perinatal epidemiology · 2023Article
- Birth weight, childhood body mass index, and risk of diverticular disease in adulthood.International journal of obesity (2005) · 2023Article
- Leveraging electronic health record data for endometriosis research.Frontiers in digital health · 2023Review
- Core concepts in pharmacoepidemiology: Validation of health outcomes of interest within real-world healthcare databases.Pharmacoepidemiology and drug safety · 2023Review
- Oral Fluoroquinolone Use and the Risk of Acute Liver Injury: A Nationwide Cohort Study.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2022Article
- Challenges with misclassification of American Indian/Alaska Native race and Hispanic ethnicity on death records in North Carolina occupational fatalities surveillance.Frontiers in epidemiology · 2022Article
- Positive Predictive Value of COVID-19 ICD-10 Diagnosis Codes Across Calendar Time and Clinical Setting.Clinical epidemiology · 2021Article
- Review of Clinical Research Informatics.Yearbook of medical informatics · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
objectiveIn health informatics, there have been concerns with reuse of electronic health data for research, including potential bias from incorrect or incomplete outcome ascertainment. In this tutorial, we provide a concise review of predictive value-based quantitative bias analysis (QBA), which comprises epidemiologic methods that use estimates of data quality accuracy to quantify the bias caused by outcome misclassification. TARGET AUDIENCE: Health informaticians and investigators reusing large, electronic health data sources for research. SCOPE: When electronic health data are reused for research, validation of outcome case definitions is recommended, and positive predictive values (PPVs) are the most commonly reported measure. Typically, case definitions with high PPVs are considered to be appropriate for use in research. However, in some studies, even small amounts of misclassification can cause bias. In this tutorial, we introduce methods for quantifying this bias that use predictive values as inputs. Using epidemiologic principles and examples, we first describe how multiple factors influence misclassification bias, including outcome misclassification levels, outcome prevalence, and whether outcome misclassification levels are the same or different by exposure. We then review 2 predictive value-based QBA methods and why outcome PPVs should be stratified by exposure for bias assessment. Using simulations, we apply and evaluate the methods in hypothetical electronic health record-based immunization schedule safety studies. By providing an overview of predictive value-based QBA, we hope to bridge the disciplines of health informatics and epidemiology to inform how the impact of data quality issues can be quantified in research using electronic health data sources.
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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.