Bayesian multistate modelling of incomplete chronic disease burden data.

ABSTRACT: A widely-used model for determining the long-term health impacts of public health interventions, often called a "multistate lifetable", requires estimates of incidence, case fatality, and sometimes also remission rates, for multiple diseases by age and gender. Generally, direct data on both incidence and case fatality are not available in every disease and setting. For example, we may know population mortality and prevalence rather than case fatality and incidence. This paper presents Bayesian continuous-time multistate models for estimating transition rates between disease states based on incomplete data. This builds on previous methods by using a formal statistical model with transparent data-generating assumptions, while providing accessible software as an R package. Rates for people of

SUBMITTER: Jackson C 

PROVIDER: S-EPMC7614284 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature