Estimation of mortality rates for disease simulation models using Bayesian evidence synthesis.

View 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.

Investigators
Abbreviation
Med Decis Making
Publication Date
2006-09-01
Volume
26
Issue
5
Page Numbers
497-511
Pubmed ID
16997927
Medium
Print
Full Title
Estimation of mortality rates for disease simulation models using Bayesian evidence synthesis.
Authors
McMahon PM, Zaslavsky AM, Weinstein MC, Kuntz KM, Weeks JC, Gazelle GS