Stratified Probabilistic Bias Analysis for Body Mass Index-related Exposure Misclassification in Postmenopausal Women

Hailey R Banack; Andrew Stokes; Matthew P Fox; Kathleen M Hovey; Elizabeth M Cespedes Feliciano; Erin S LeBlanc; Chloe Bird; Bette J Caan; Candyce H Kroenke; Matthew A Allison; Scott B Going; Linda Snetselaar; Ting-Yuan David Cheng; Rowan T Chlebowski; Marcia L Stefanick; Michael J LaMonte; Jean Wactawski-Wende

doi:10.1097/EDE.0000000000000863

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Stratified Probabilistic Bias Analysis for Body Mass Index-related Exposure Misclassification in Postmenopausal Women

Journal article

Peer reviewed

Stratified Probabilistic Bias Analysis for Body Mass Index-related Exposure Misclassification in Postmenopausal Women

Hailey R Banack, Andrew Stokes, Matthew P Fox, Kathleen M Hovey, Elizabeth M Cespedes Feliciano, Erin S LeBlanc, Chloe Bird, Bette J Caan, Candyce H Kroenke, Matthew A Allison, …

Epidemiology (Cambridge, Mass.), Vol.29(5), pp.604-613

09/2018

DOI: 10.1097/EDE.0000000000000863

PMCID: PMC6481627

PMID: 29864084

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Abstract

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). 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/m), 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. Validation analyses highlight that using a BMI cutpoint of 30 kg/m 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). These results demonstrate the importance of using quantitative bias analysis techniques to account for nondifferential exposure misclassification of BMI-defined obesity. See video abstract at, http://links.lww.com/EDE/B385.

Probability

Body Mass Index

Body Weight

Bias

Humans

Middle Aged

Adipose Tissue - pathology

Postmenopause

Body Height

Female

Obesity - diagnosis

Aged

Obesity - mortality

Details

Title: Subtitle: Stratified Probabilistic Bias Analysis for Body Mass Index-related Exposure Misclassification in Postmenopausal Women
Creators: Hailey R Banack - From the Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, The State University of New York, NY
Andrew Stokes - Department of Global Health and Center for Global Health and Development, Boston University School of Public Health, MA
Matthew P Fox - Boston University School of Medicine
Kathleen M Hovey - From the Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, The State University of New York, NY
Elizabeth M Cespedes Feliciano - Division of Research, Kaiser Permanente Northern California, Oakland, CA
Erin S LeBlanc - Kaiser Permanente Center for Health Research NW, Portland, OR
Chloe Bird - RAND Corporation, Santa Monica, CA
Bette J Caan - Division of Research, Kaiser Permanente Northern California, Oakland, CA
Candyce H Kroenke - Division of Research, Kaiser Permanente Northern California, Oakland, CA
Matthew A Allison - Department of Family Medicine and Public Health, University of California, San Diego, CA
Scott B Going - The Department of Nutritional Sciences, College of Agriculture and Life Sciences, The University of Arizona
Linda Snetselaar - College of Public Health, University of Iowa, IA
Ting-Yuan David Cheng - Department of Epidemiology, University of Florida, Gainesville, FL
Rowan T Chlebowski - Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA
Marcia L Stefanick - Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA
Michael J LaMonte - From the Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, The State University of New York, NY
Jean Wactawski-Wende - From the Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, The State University of New York, NY
Resource Type: Journal article
Publication Details: Epidemiology (Cambridge, Mass.), Vol.29(5), pp.604-613
DOI: 10.1097/EDE.0000000000000863
PMID: 29864084
PMCID: PMC6481627
NLM abbreviation: Epidemiology
ISSN: 1531-5487
eISSN: 1531-5487
Publisher: United States
Grant note: HHSN268201600018C / NHLBI NIH HHS HHSN268201600003C / NHLBI NIH HHS HHSN268201600004C / NHLBI NIH HHS HHSN268201600002C / NHLBI NIH HHS CIHR HHSN268201600001C / NHLBI NIH HHS K07 CA201334 / NCI NIH HHS
Language: English
Date published: 09/2018
Academic Unit: Epidemiology; Fraternal Order of Eagles Diabetes Research Center; Internal Medicine
Record Identifier: 9983995058102771

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