On Logistic Regression Analysis Using Propensity Score Matching

성향점수매칭 방법을 사용한 로지스틱 회귀분석에 관한 연구

  • Kim, So Youn (Division of Mathematics and Informational Statistics, Wonkwang University) ;
  • Baek, Jong Il (Division of Mathematics and Informational Statistics, Wonkwang University)
  • 김소연 (원광대학교 자연과학대학 수학정보통계학부) ;
  • 백종일 (원광대학교 자연과학대학 수학정보통계학부)
  • Received : 2016.09.30
  • Accepted : 2016.12.16
  • Published : 2016.12.25

Abstract

Purpose: Recently, propensity score matching method is used in a large number of research paper, nonetheless, there is no research using fitness test of before and after propensity score matching. Therefore, comparing fitness of before and after propensity score matching by logistic regression analysis using data from 'online survey of adolescent health' is the main significance of this research. Method: Data that has similar propensity in two groups is extracted by using propensity score matching then implement logistic regression analysis on before and after matching separately. Results: To test fitness of logistic regression analysis model, we use Model summary, -2Log Likelihood and Hosmer-Lomeshow methods. As a result, it is confirmed that the data after matching is more suitable for logistic regression analysis than data before matching. Conclusion: Therefore, better result which has appropriate fitness will be shown by using propensity score matching shows better result which has better fitness.

Keywords

Acknowledgement

Supported by : 원광대학교

References

  1. Berk, R. A. (1983). "An Introduction to Sample Selection Bias in Sociological Data". American sociological review, Vol. 48, No. 3, pp. 386-398. https://doi.org/10.2307/2095230
  2. No, S. Y. (2008). "Reassessment of risk factors for the development of liver cirrhosis based on propensity score matching methods". Yonsei University, Seoul.
  3. Rosenbaum, P. R and Rubin, D. B. (1983). "The central role of the propensity score in observational studies for causal effects". Biometrika, Vol. 70, No. 1, pp. 41-55. https://doi.org/10.1093/biomet/70.1.41
  4. D'agostino, R. B. (1998). "Tutorial in Biostatistics Propensity Score methods for Bias Reduction in The Comparison of a Treatment to a Non-randomized Control Group". Statistics in medicine, Vol. 17. pp. 2265-2281. https://doi.org/10.1002/(SICI)1097-0258(19981015)17:19<2265::AID-SIM918>3.0.CO;2-B
  5. Park, S. H. and Kim, T. I. (2012). "Specific examples of how to apply and propensity score matching". The Korean association for policy studies proceedings.
  6. Jeong, S. H. (2011). "Effect of perceived social support on treatment responses and outcomes of depressive disorders using a propensity score matching method". Catholic University, Seoul.
  7. Lee, S. J. et al. (2007). "The use of propensity score matching for evaluation of the effects of nursing interventions". Journal of Korean Academy of nursing, Vol. 37, pp. 414-421. https://doi.org/10.4040/jkan.2007.37.3.414
  8. Thabut, G. et al. (2008). "Survival after bilateral versus single lug transplantation for patients with chronic obstructive pulmonary disease: a retropective analysis of registry data". The Lancet, Vol. 371, No. 9614, pp. 744-751. https://doi.org/10.1016/S0140-6736(08)60344-X
  9. Hiatt, W. R. (2006). "Observational Studies of Drug Satety-Aprotinin and the Absence of transparency". New England Journal of Medicine, Vol. 355, pp. 2171-2173 . https://doi.org/10.1056/NEJMp068252
  10. Kim, E. S. (2008). "National basic livelihood security system and labour supply". Korean labor & Income panel study, pp. 457-471.
  11. Chai, G. M. (2013). "Advanced Statistic using SPSS and AMOS". Yangseowon.
  12. Cox, D. R. and Snell, E. J. (1989). "Analysis of Binary Data". CRC Press.
  13. Nagelkerke, N. J. D. (1991). "Anote on a General Definition of the coefficient of Determination". Biometrika, Vol. 78, No. 3, pp. 691-692. https://doi.org/10.1093/biomet/78.3.691
  14. Hosmer, D. and Lemeshow (2000). "Applied Logistic Regression". New York: John Wiley & Sons.
  15. Kwon, Y. M. and Kim S. Y. and Baek, J. I. (2015). "The Risk Factors Analysis of Adolescent Suicide due to Depression Experience". Journal of Applied Reliability, Vol. 15, No. 2, pp. 76-83.
  16. Gottman, J. M. and Roy, A. K. (1990). "Sequential analysis: A guide for behavorial researchers". Cambridge University Press.
  17. Menard, S. (2000). "Coefficients of determination for multiple logistic regression analysis". The American Statistician. Vol. 54, pp. 17-24.
  18. Paul Allison (2013). http://www.statisticalhorizons.com/r2logistic.
  19. Tjur, T. (2009). "Coeffocoents of determination in logistic regression models-A new proposal: The coefficient of discrimination". The American Statistician Vol. 63, pp. 366-372. https://doi.org/10.1198/tast.2009.08210