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A Bayesian Sensitivity Analysis to Partition BMI into Components of Body Composition: An Application to Head and Neck Cancer Survival

Citation

Bradshaw, Patrick T.; Zevallos, Jose P.; Wisniewski, Kathy; & Olshan, Andrew F. (2019). A Bayesian Sensitivity Analysis to Partition BMI into Components of Body Composition: An Application to Head and Neck Cancer Survival. American Journal of Epidemiology, 188(11), 2031-2039.

Abstract

Previous studies have suggested a "J-shaped" relationship between body mass index (BMI, kg/m2) and survival among head and neck cancer (HNC) patients. However, BMI is a vague measure of body composition. To provide greater resolution we used Bayesian sensitivity analysis, informed by external data, to model the relationship between predicted fat mass index (FMI, kg adipose tissue/m2), lean mass index (LMI, kg lean tissue/m2) and survival. We estimated posterior median hazard ratios (HR) and 95% credible intervals for the BMI-mortality relationship in a Bayesian framework using data from 1,180 adults in North Carolina with HNC diagnosed between 2002 and 2006. Risk factors were assessed by interview shortly after diagnosis and vital status through 2013 via the National Death Index. The relationship between BMI and all-cause mortality was convex with a nadir at 28.6 with greater risk observed throughout the normal weight range. The sensitivity analysis indicated that this was consistent with opposing increases in risk with FMI (1 kg/m2 increase, HR = 1.04 [1.00, 1.08]) and decreases with LMI (1 kg/m2 increase, HR = 0.90 [0.85, 0.95]). Patterns were similar for HNC-specific mortality but associations were stronger. Measures of body composition, rather than BMI, should be considered in relation to mortality risk.

URL

http://dx.doi.org/10.1093/aje/kwz188

Reference Type

Journal Article

Year Published

2019

Journal Title

American Journal of Epidemiology

Author(s)

Bradshaw, Patrick T.
Zevallos, Jose P.
Wisniewski, Kathy
Olshan, Andrew F.