Bayesian Decision Tree Model

Uses

How it works

Highlighted claims

• Effectiveness is measured as the number of people with confirmed TB detected by each algorithm. — Start4All protocol for a Bayesian cost-effectiveness model of tuberculosis screening and diagnosis in seven high burden low-income and middle-income countries
• Diagnostic performance parameters use beta distributions derived from posterior distributions estimated in the diagnostic performance analysis. — Start4All protocol for a Bayesian cost-effectiveness model of tuberculosis screening and diagnosis in seven high burden low-income and middle-income countries
• A total of 22 independent model runs will be performed across settings, including HIV-status stratification for aggregate analyses. — Start4All protocol for a Bayesian cost-effectiveness model of tuberculosis screening and diagnosis in seven high burden low-income and middle-income countries
• The study uses a Bayesian decision tree model to simulate costs and confirmed TB detections for all pre-selected algorithms, built separately for facility-based and community-based case finding in Amua version 0.3.1. — Start4All protocol for a Bayesian cost-effectiveness model of tuberculosis screening and diagnosis in seven high burden low-income and middle-income countries
• The model simulates a hypothetical cohort of 1,000 participants for every algorithm, country, and population setting. — Start4All protocol for a Bayesian cost-effectiveness model of tuberculosis screening and diagnosis in seven high burden low-income and middle-income countries