Global sensitivity analysis of a model simulating an individual’s health state through their lifetime

Abstract

In public health, model predictions are used by decision-makers to minimise health burdens and monetary costs of non-communicable diseases. Fully understanding the uncertainty underlying those predictions is thus crucial. One-parameter-at-a-time methods are typically used for model uncertainty analysis but are often impractical for large-scale nonlinear models containing a very large number of parameters and cannot be used to examine the uncertainty associated with parameter interaction. An individual-based chronic disease model was developed to model the impact of Body Mass Index (BMI) trends on the rates of non-communicable diseases. The model was simulated for overweight and obese male case studies and used to predict their life expectancy, disease-free life expectancy and quality-adjusted life years. Uncertainty was estimated by carrying out a global sensitivity analysis by assessing the contribution to the overall uncertainty from a selection of parameters individually and from their interactions. Results show that the uncertainty of the BMI input parameter had the greatest impact on the disease-free life expectancy and quality- adjusted life year uncertainty compared to the relative risks of colorectal cancer and stroke. Life expectancy uncertainty was influenced by BMI and colorectal cancer relative risks. Global sensitivity analysis enables the assessment of the parametric uncertainty for individual parameters and their interaction. This allows the communication of the uncertainty of different policy options. A strategy for scaling-up uncertainty analysis from an individual to a population level is discussed.