ArticleInternational journal of epidemiology2023
SAS and R code for probabilistic quantitative bias analysis for misclassified binary variables and binary unmeasured confounders.
Article in International journal of epidemiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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
14 citing papers in PubMed.
- Exposure misclassification of self-reported vegetarian diet and its impact on health outcomes.International journal of epidemiology · 2026Article
- Hypertensive disorders of pregnancy, maternal cardiovascular disease mortality and the role of familial predisposition: a Norwegian population-based sibling-comparison, sibling-spillover and negative-control cohort study.American journal of epidemiology · 2026Article
- Sexual orientation identity and attraction changes among youth: prospective versus retrospective measurement.Annals of epidemiology · 2025Article
- Quantitative bias analysis for mismeasured variables in health research: a review of software tools.BMC medical research methodology · 2025Review
- Maternal motor vehicle crashes during pregnancy and child neurodevelopment.Pediatric research · 2025Article
- Validation of Lactational Mastitis Diagnosis Codes in Electronic Health Care Data.Epidemiology (Cambridge, Mass.) · 2025Article
- Occupational Injury and Suicide in Washington State, Adjusting for Pre-Injury Depression.American journal of industrial medicine · 2025Article
- Using Quantitative Bias Analysis to Adjust for Misclassification of COVID-19 Outcomes: An Applied Example of Inhaled Corticosteroids and COVID-19 Outcomes.Pharmacoepidemiology and drug safety · 2025Article
- unmconf : an R package for Bayesian regression with unmeasured confounders.BMC medical research methodology · 2024Article
- Exploring the Bidirectional Relationship Between Periodontitis and Rheumatoid Arthritis in a Large Danish Cohort.ACR open rheumatology · 2024Article
- Estimating the impact of bias in causal epidemiological studies: the case of health outcomes following assisted reproduction.Human reproduction (Oxford, England) · 2024Article
- Menopausal hormone therapy and risk of sarcoidosis: a population-based nested case-control study in Sweden.European journal of epidemiology · 2024Article
- Application of the target trial emulation framework to external comparator studies.Frontiers in drug safety and regulation · 2024Article
- Quantitative Assessment of Systematic Bias: A Guide for Researchers.Journal of dental research · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Systematic error from selection bias, uncontrolled confounding, and misclassification is ubiquitous in epidemiologic research but is rarely quantified using quantitative bias analysis (QBA). This gap may in part be due to the lack of readily modifiable software to implement these methods. Our objective is to provide computing code that can be tailored to an analyst's dataset. We briefly describe the methods for implementing QBA for misclassification and uncontrolled confounding and present the reader with example code for how such bias analyses, using both summary-level data and individual record-level data, can be implemented in both SAS and R. Our examples show how adjustment for uncontrolled confounding and misclassification can be implemented. Resulting bias-adjusted point estimates can then be compared to conventional results to see the impact of this bias in terms of its direction and magnitude. Further, we show how 95% simulation intervals can be generated that can be compared to conventional 95% confidence intervals to see the impact of the bias on uncertainty. Having easy to implement code that users can apply to their own datasets will hopefully help spur more frequent use of these methods and prevent poor inferences drawn from studies that do not quantify the impact of systematic error on their results.
Indexed as
Identifiers
What OpenQuestion holds
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.