A Bayesian Sensitivity Analysis to Partition Body Mass Index Into Components of Body Composition: An Application to Head and Neck Cancer Survival

Previous studies have suggested a "J-shaped" relationship between body mass index (BMI, calculated as weight (kg)/height (m)²) 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, adipose tissue (kg)/height (m)²), lean mass index (LMI, lean tissue (kg)/height (m)²), and survival. We estimated posterior median hazard ratios 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 (per unit increase, hazard ratio = 1.04 (1.00, 1.08)) and decreases with LMI (per unit increase, hazard ratio = 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.