Estimation of Mortality Rates for Disease Simulation Models Using Bayesian Evidence Synthesis

Abstract

Purpose. The authors propose a Bayesian approach for estimating competing risks for inputs to disease simulation models. This approach is suggested when modeling a disease that causes a large proportion of all-cause mortality, particularly when mortality from the disease of interest and other-cause mortality are both affected by the same risk factor. Methods. The authors demonstrate a Bayesian evidence synthesis by estimating other-cause mortality, stratified by smoking status, for use in a simulation model of lung cancer. National (US) survey data linked to death registries (National Health Interview Survey [NHIS]—Multiple Cause of Death files) were used to fit cause-specific hazard models for 3 causes of death (lung cancer, heart disease, and all other causes), controlling for age, sex, race, and smoking status. Synthesis of NHIS data with national vital statistics data on numbers and causes of deaths was performed in WinBUGS (version 1.4.1, MRC Biostatistics Unit, UK). Correction for inconsistencies between the NHIS and vital statistics data is described. A published cohort study was a source of prior information for smoking-related mortality. Results. Marginal posterior densities of annual mortality rates for lung cancer and other-cause death (further divided into heart disease and all other causes), stratified by 5-year age interval, race (white and black), gender, and smoking status (current, former, never), were estimated, specific to a time period (1987-1995). Overall, black current smokers experienced the highest mortality rates. Conclusions. Bayesian evidence synthesis is an effective method for estimation of cause-specific mortality rates, stratified by demographic factors.