The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
권호 표지
DOI QR코드

DOI QR Code

Fitting competing risks models using medical big data from tuberculosis patients

전국 결핵 신환자 의료빅데이터를 이용한 경쟁위험모형 적합

  • Received : 2018.06.28
  • Accepted : 2018.07.09
  • Published : 2018.08.31
Download PDF
⟨ Previous Next ⟩

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).

결핵은 높은 이환과 사망을 일으키는 질병으로 현대의학의 발달에 따라 발생률과 사망률은 감소하고 있다. 그러나 한국은 아직까지 OECD 국가 중 결핵 발생률과 사망률이 가장 높다. 이에 따라 한국은 결핵의 예방 및 통제를 위해 여러 정책 사업을 실시하고 있다. 본 연구에서는 공공민간협력(public-private mix) 결핵관리사업이 치료결과에 미치는 영향을 분석하고 결핵환자의 치료 성공에 영향을 미치는 요인을 확인하고자 한다. 질병관리본부에서 관리하는 결핵환자 신고 자료를 이용하여 2012-2015년 전국 결핵 신환자 코호트 약 13만명을 대상으로 분석하였다. 누적 발생 함수(cumulative incidence function)를 이용하여 요인별로 누적 치료 성공률을 비교하였으며. 주 관심사건(치료성공) 및 경쟁사건(사망)을 고려한 두 가지 경쟁위험모형(cause-specific Cox's proportional hazards model and subdistribution hazard model)을 사용하여 분석 결과를 비교하였다.

Keywords

References

  1. Cox, D. R. (1972). Regression models and life tables (with discussion), Journal of the Royal Statistical Society. Series B (Methodological), 34, 187-220.
  2. Falzon, D., Le Strat, Y., Belghiti, F., Infuso, A., and EuroTB correspondents (2005). Exploring the determinants of treatment success for tuberculosis cases in Europe, International Journal of Tuberculosis and Lung Disease, 9, 1224-1229.
  3. Fine, J. P. and Gray, R. J. (1999). A proportional hazards model for the subdistribution of a competing risk, Journal of the American Statistical Association, 94, 496-509. https://doi.org/10.1080/01621459.1999.10474144
  4. Gooley, T. A., Leisenring, W., Crowley, J., and Storer, B. E. (1999). Estimation of failure probabilities in the presence of competing risks: new representations of old estimators, Statistics in Medicine, 18, 695-706. https://doi.org/10.1002/(SICI)1097-0258(19990330)18:6<695::AID-SIM60>3.0.CO;2-O
  5. Gray, R. J. (1988). A Class of K-Sample Tests for Comparing the Cumulative Incidence of a Competing Risk,Annals of Statistics, 16, 1141-1154. https://doi.org/10.1214/aos/1176350951
  6. Ha, I. D., Jeong, J. H., and Lee, Y. J. (2017). Statistical Modelling of Survival Data with Random Effects: H-likelihood Approach. Springer.
  7. Kalbfleisch, J. D. and Prentice, R. L. (1980). The Statistical Analysis of Failure Time Data, John Wiley & Sons, New York.
  8. Kaplan, E. L. and Meier, P. (1958). Nonparametric estimation from incomplete observations, Journal of the American Statistical Association, 53, 457-481. https://doi.org/10.1080/01621459.1958.10501452
  9. Kim, M. J. (2016). Estimation methods and interpretation of competing risk regression models, The Korean Journal of Applied Statistics, 29, 1231-1246.
  10. Korea Center for Disease Control and Prevention (2017). Guidelines for National Tuberculosis Control 2017. Cheongju
  11. Lau, B., Cole, S. R., and Gange, S. J. (2009). Competing risk regression models for epidemiologic data, American Journal of Epidemiology, 170, 244-256. https://doi.org/10.1093/aje/kwp107
  12. Okanurak, K., Kitayaporn, D. and Akarasewi, P. (2008). Factors contributing to treatment success among tuberculosis patients: a prospective cohort study in Bangkok, International Journal of Tuberculosis and Lung Disease, 12, 1160-1165.
  13. Park J. S. (2011). Increasing the Treatment Success Rate of Tuberculosis in a Private, Tuberculosis and Respiratory Diseases, 70, 143-149. https://doi.org/10.4046/trd.2011.70.2.143
  14. Prentice, R., Kalbfleisch, J. D., Peterson, A. V., Flournoy, N., Farewell, V. T. and Breslow, N. E. (1978). The analysis of failure times in the presence of competing risks, Biometrics, 34, 541-554. https://doi.org/10.2307/2530374
  15. Son, H.J. (2018). Current Status and Determinants of Treatment Outcomes among New Tuberculosis Patients in Republic of Korea: Achievement in Public-private mix program and challenges, Hanyang University doctoral dissertation.
  16. World Health Organization (2017). Global Tuberculosis report 2017. Geneva.
  17. Yoon, T. H. (2010). Regional Health Inequalities in Korea The Status and Policy Tasks, Critical Social Welfare Academy, 30, 49-77.