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http://dx.doi.org/10.5351/KJAS.2018.31.4.529

Fitting competing risks models using medical big data from tuberculosis patients  

Kim, Gyeong Dae (Department of Statistics, Pukyong National University)
Noh, Maeng Seok (Department of Statistics, Pukyong National University)
Kim, Chang Hoon (Pusan National University Hospital)
Ha, Il Do (Department of Statistics, Pukyong National University)
Publication Information
The Korean Journal of Applied Statistics / v.31, no.4, 2018 , pp. 529-538 More about this Journal
Abstract
Tuberculosis causes high morbidity and mortality. However, Korea still has the highest tuberculosis (TB) incidence and mortality among OECD countries despite decreasing incidence and mortality due to the development of modern medicine. Korea has now implemented various policy projects to prevent and control tuberculosis. This study analyzes the effects of public-private mix (PPM) tuberculosis control program on treatment outcomes and identifies the factors that affecting the success of TB treatment. We analyzed 130,000 new tuberculosis patient cohort from 2012 to 2015 using data of tuberculosis patient reports managed by the Disease Control Headquarters. A cumulative incidence function (CIF) compared the cumulative treatment success rates for each factor. We compared the results of the analysis using two popular types of competition risk models (cause-specific Cox's proportional hazards model and subdistribution hazard model) that account for the main event of interest (treatment success) and competing events (death).
Keywords
tuberculosis; public-private mix (PPM); competing event; cumulative incidence function; PPM tuberculosis control program;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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