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
Stratified probabilistic bias analysis for body mass index-related exposure misclassification in postmenopausal women
Epidemiology, Volume 29, No. 5, Year 2018
Notification
URL copied to clipboard!
Description
Background: There is widespread concern about the use of body mass index (BMI) to define obesity status in postmenopausal women because it may not accurately represent an individual's true obesity status. The objective of the present study is to examine and adjust for exposure misclassification bias from using an indirect measure of obesity (BMI) compared with a direct measure of obesity (percent body fat). Methods: We used data from postmenopausal non-Hispanic black and non-Hispanic white women in the Women's Health Initiative (n=126,459). Within the Women's Health Initiative, a sample of 11,018 women were invited to participate in a sub-study involving dual-energy x-ray absorptiometry scans. We examined indices of validity comparing BMI-defined obesity (≥30 kg/m2), with obesity defined by percent body fat. We then used probabilistic bias analysis models stratified by age and race to explore the effect of exposure misclassification on the obesity-mortality relationship. Results: Validation analyses highlight that using a BMI cutpoint of 30 kg/m2 to define obesity in postmenopausal women is associated with poor validity. There were notable differences in sensitivity by age and race. Results from the stratified bias analysis demonstrated that failing to adjust for exposure misclassification bias results in attenuated estimates of the obesity-mortality relationship. For example, in non-Hispanic white women 50-59 years of age, the conventional risk difference was 0.017 (95% confidence interval = 0.01, 0.023) and the bias-adjusted risk difference was 0.035 (95% simulation interval = 0.028, 0.043). Conclusions: These results demonstrate the importance of using quantitative bias analysis techniques to account for nondifferential exposure misclassification of BMI-defined obesity. © 2018 Wolters Kluwer Health, Inc.
Authors & Co-Authors
Banack, Hailey R.
United States, Buffalo
University at Buffalo, the State University of new York
Stokes, Andrew C.
United States, Boston
Boston University
Fox, Matthew P.
United States, Boston
Boston University
Hovey, Kathleen M.
United States, Buffalo
University at Buffalo, the State University of new York
Bird, Chloe E.
United States, Santa Monica
Rand Corporation
Caan, Bette J.
United States, Oakland
Kaiser Permanente Division of Research
Kroenke, Candyce H.
United States, Oakland
Kaiser Permanente Division of Research
Allison, Matthew A.
United States, La Jolla
University of California, San Diego
Going, Scott B.
United States, Tucson
The University of Arizona
Snetselaar, Linda G.
United States, Iowa City
University of Iowa
Cheng, Ting Yuan David
United States, Gainesville
University of Florida
Chlebowski, Rowan T.
United States, Duarte
City of Hope National Med Center
Stefanick, Marcia L.
United States, Palo Alto
Stanford University
LaMonte, Michael J.
United States, Buffalo
University at Buffalo, the State University of new York
Wactawski-Wende, Jean
United States, Buffalo
University at Buffalo, the State University of new York
Statistics
Citations: 17
Authors: 15
Affiliations: 11
Identifiers
Doi:
10.1097/EDE.0000000000000863
ISSN:
10443983
Research Areas
Noncommunicable Diseases
Study Approach
Quantitative
Participants Gender
Female