Skip to content
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Menu
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Menu
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
medicine
SAS and R code for probabilistic quantitative bias analysis for misclassified binary variables and binary unmeasured confounders
International Journal of Epidemiology, Volume 52, No. 5, Year 2023
Notification
URL copied to clipboard!
Description
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. © 2023 The Author(s) 2023; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.
Authors & Co-Authors
Fox, Matthew P.
United States, Boston
School of Public Health
Neumark-Sztainer, Dianne Fleming
United States, Minneapolis
University of Minnesota Twin Cities
Lash, Timothy Lee
United States, Atlanta
Emory University
Statistics
Citations: 1
Authors: 3
Affiliations: 3
Identifiers
Doi:
10.1093/ije/dyad053
ISSN:
03005771
Study Approach
Quantitative