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The Use of Propensity Score Matching for Evaluation of the Effects of Nursing Interventions  

Lee, Suk-Jeong (Red Cross College of Nursing)
Yoo, Ji-Soo (College of Nursing, Yonsei University)
Shin, Mi-Kyung (Public health, University of Illinois at Chicago)
Park, Chang-Gi (University of Illinois at Chicago)
Lee, Hyun-Chul (College of Medicine, Yonsei University)
Choi, Eun-Jin (Nursing Policy Research Institute, College of Nursing, Yonsei University)
Publication Information
Journal of Korean Academy of Nursing / v.37, no.3, 2007 , pp. 414-421 More about this Journal
Abstract
Background: Nursing intervention studies often suffer from a selection bias introduced by failure of random assignment. Evaluation with selection bias could under or over-estimate any intervention's effects. PS matching (PSM) can reduce a selection bias through matching similar Propensity Scores (PS). PS is defined as the conditional probability of being treated given the individual's covariates and it can be reused to balance the covariates of two groups. Purpose: This study was done to assess the significance of PSM as an alternative evaluation method of nursing interventions. Method: An intervention study for patients with some baseline individual characteristic differences between two groups was used for this demonstration. The result of a t-test with PSM was compared with a t-test without matching. Results: The level of HbA1c at 12 months after baseline was different between the two groups in terms of matching or not. Conclusion: This study demonstrated the effects of a quasi-random assignment. Evaluation using PSM can reduce a selection bias impact that affects the result of the nursing intervention. Analyzing nursing research more objectively to reduce selection bias using PSM is needed.
Keywords
Propensity score matching; Nursing intervention; Evaluation;
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1 Baskett, R. J., O'Connor, G. T., Hirsh, G. M., Ghali, W. A., Sabadosa, K. A., Morton, J. R., Ross, C. S., Hernandez, F., Nugent, W. C., Lahey, S. J., Sisto, D., Dacey, L. J., Klemperer, J. D., Helm, R. E., & Maitland, A. (2005). The preoperative intra-aortic ballon pump in coronary bypass surgery: A lack of evidence of effectiveness. Am Heart J, 150(6), 1122-1127   DOI   ScienceOn
2 Bloom, H. S., Charles M., Caryoly, J. H., & Ying Lei. (2002). Can non-experimental comparison group methods the findings from a random assignment evaluation of mandatory welfare to work programs? MDRS working Papers on Research Methodology. New York, NY: Manpower Demonstration Research Corporation
3 Dehejia, R. H., & Sadek, W. (1999). Causal effects in nonexperimental studies; Reevaluating the evaluation of training programs. J Am Stat Assoc, 94(448), 1053-1062   DOI
4 Ozminkowski, R. J., Burton, W. N., Goetzel, R. Z., Maclean, R., & Wang, S. (2006). The impact of rhematoid arthritis on medical expenditures, absenteeism, and short-term disability benefits. JOEM, 48(2), 135-147   DOI   ScienceOn
5 Yoo, J. S, Lee, S. J, Lee, H. C., Kim, S. H., Kang, E. S.,Park, E. J. (2004). The effects of short term comprehensive life style modification program on glycemic metabolism, lipid metabolism and body composition in type 2 diabetes mellitus. J Korean Acad Nurs, 34(7), 1277-1287   과학기술학회마을   DOI
6 Rubin, D. B., & Thomas, N. (2000). Combining propensity score matching with assertional adjustment for prognostic covariates. J Am Stat Assoc, 95, 573-585   DOI
7 Lee, S. W. (2003). Evaluating the effectiveness of vocational training programs in Korea using propensity score matching. Korean Public Adm Rev, 37(3), 181-199
8 Heckman, J., & Smith, J. (1995). Assessing the case for social experiments. J Econ Perspect, 9, 85-110   DOI   ScienceOn
9 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. Statist Med, 17, 2265-2281   DOI   ScienceOn
10 Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41-55   DOI   ScienceOn
11 Rubin, D. B. (1978). Baysian inference for causal effects: The role of randomization. Ann Stat, 6(1), 34-58   DOI