Turning the Analysis of Obesity-Mortality Associations Upside Down: Modeling Years of Life Lost Through Conditional Distributions

The analysis of longevity as a function of risk factors such as body mass index (BMI; kg/m(2)), activity levels, and dietary factors is a mainstay of obesity research. Modeling survival through hazard functions, relative risks, or odds of dying with methods such as Cox proportional hazards or logistic regression are the most common approaches and have many advantages. However, they also have disadvantages in terms of the ease of interpretability, especially for non-statisticians; the need for additional data to convert parameter estimates to estimates of years of life lost (YLL); and debates about the appropriate time scale in the model. Parametric survival models are able to provide more direct answers, and in our analysis of an obesity-related data set, gave consistent YLL estimates regardless of the distribution used. Additionally, we offer alternative approaches to the analyses of censored survival data including a modified or ‘compressed’ Gaussian distribution. We therefore recommend increased consideration of parametric survival models in chronic disease and risk factor epidemiology.