PROPENSITY SCORE Untuk Data Kesehatan
Abstract
Metode Propensity score didefinisikan sebagai probabilitas bersyarat untuk menerima suatu treatment berdasarkan pada karakteristik sebelum treatment. Buku ini membahas tentang konsep dasar dan metode propensity score, penggunaan propensity score dalam stratification, weighting serta dalam boostrap. Buku ini disusun dalam 6 bab yang diterjemahkan dari beberapa referensi dan memuat panduan praktis yang bisa diaplikasikan untuk data kesehatan. Buku ini dilengkapi lampiran-lampiran data yang bisa dipraktekkan pembaca
ISBN. 978-602-53485-9-4
References
Akolo, I.R. Propensity Score Stratification Bootstrap menggunakan Regresi Logistik Biner pada Kasus Diabetes Melitus (DM). Surabaya: Statistika ITS
Austin, P.C. (2011). A Tutorial and Case Study in Propensity Score Analysis: AN Application to Estimating the Effect of In-Hospital Smoking Cessation Counseling on Mortality. Multivariate Behavioral Research, 46:119–151
Dehejia, R.H., & Wahba, S. (1999). Causal Effect in Nonexperimental Studies: Reevaluating the Evaluation of Training Programs. Journal of the American Statistical Association, Vol.94 No. 448
D’Agostino, R.B. (1998) Tutorial in Biostatistics Propensity Score Method for Bias Reduction in the Comparison of a Treatment to a Non-Randomized Control Group. 17, 2265-2281
Efron,B., & Tibshirani, Robert J. (1993). An introduction to the Bootstrap. New York: Chapman & Hall
Guo, S. & Fraser, M. W. (2010). Propensity score analysis: Statistical methods and applications. California: Sage Publications
Imbens, G. W. (2004). Nonparametric estimation of average treatment effects under exogeneity: A review. The Review of Economics and Statistics, 86, pp. 4–29
Islamiah, F. Propensity Score menggunakan Regresi Logistik pada Kasus Data HIV/AIDS LSM Orbit Surabaya. Surabaya: Statistika ITS
Littnerova, S., Jarkovsky, J., Parenica, J., Pavlik, T., Spinar, J., & Dusek, L. (2013). Why to use Propensity Score in Observational Studies? Case Study Based on Data from the Czech Clinical Database AHEAD 2006-09, cor et Vasa, 55(4), pp. 383-390.
McCaffrey, D.F., Ridgeway, G., & Moral, A.R. (2004). Propensity Score Estimation with Boosted Regression for Evaluating Causal Effect in Observational Studies. Pschological Method, 9(4), pp 403.
McCandless, L.C., Gustafson, P., & Austin, P.C. (2009). Bayesian propensity score analysis for observational data. Statistics in Medicine, 28, pp 94-112.
Ningsih, S. (2016).
Pan, W., & Bai, H. (2015). Propensity Score Analysis: Fundamental and Developments. New York: Gulford Press
Rosenbaum, P.R. (2010). Design of Observational Studies. New York: Springer
Rosenbaum, P.R., & Rubin, D.B. (1983). The Central Role of the Propensity Score in Observational Studies for Causal Effects. Journal Biometrika, vol.70, No.1, pp 41-55.
Tu, W., & Zhou, X.H. (2002). A Bootstrap Confidence Interval Procedure for the Treatment Effect Using Propensity Score Subclassification. Health Services & Outcomes Research Methodology 3: 135–147
Yanovitzky, I., Zanutto, E., & Hornik, R. (2005). Estimating Causal Effects of Public Health Education Campaigns using Propensity Score Methodology. Journal Elsevier Evaluation and Program Planning 28 (2005) pp. 209–220.
Austin, P.C. (2011). A Tutorial and Case Study in Propensity Score Analysis: AN Application to Estimating the Effect of In-Hospital Smoking Cessation Counseling on Mortality. Multivariate Behavioral Research, 46:119–151
Dehejia, R.H., & Wahba, S. (1999). Causal Effect in Nonexperimental Studies: Reevaluating the Evaluation of Training Programs. Journal of the American Statistical Association, Vol.94 No. 448
D’Agostino, R.B. (1998) Tutorial in Biostatistics Propensity Score Method for Bias Reduction in the Comparison of a Treatment to a Non-Randomized Control Group. 17, 2265-2281
Efron,B., & Tibshirani, Robert J. (1993). An introduction to the Bootstrap. New York: Chapman & Hall
Guo, S. & Fraser, M. W. (2010). Propensity score analysis: Statistical methods and applications. California: Sage Publications
Imbens, G. W. (2004). Nonparametric estimation of average treatment effects under exogeneity: A review. The Review of Economics and Statistics, 86, pp. 4–29
Islamiah, F. Propensity Score menggunakan Regresi Logistik pada Kasus Data HIV/AIDS LSM Orbit Surabaya. Surabaya: Statistika ITS
Littnerova, S., Jarkovsky, J., Parenica, J., Pavlik, T., Spinar, J., & Dusek, L. (2013). Why to use Propensity Score in Observational Studies? Case Study Based on Data from the Czech Clinical Database AHEAD 2006-09, cor et Vasa, 55(4), pp. 383-390.
McCaffrey, D.F., Ridgeway, G., & Moral, A.R. (2004). Propensity Score Estimation with Boosted Regression for Evaluating Causal Effect in Observational Studies. Pschological Method, 9(4), pp 403.
McCandless, L.C., Gustafson, P., & Austin, P.C. (2009). Bayesian propensity score analysis for observational data. Statistics in Medicine, 28, pp 94-112.
Ningsih, S. (2016).
Pan, W., & Bai, H. (2015). Propensity Score Analysis: Fundamental and Developments. New York: Gulford Press
Rosenbaum, P.R. (2010). Design of Observational Studies. New York: Springer
Rosenbaum, P.R., & Rubin, D.B. (1983). The Central Role of the Propensity Score in Observational Studies for Causal Effects. Journal Biometrika, vol.70, No.1, pp 41-55.
Tu, W., & Zhou, X.H. (2002). A Bootstrap Confidence Interval Procedure for the Treatment Effect Using Propensity Score Subclassification. Health Services & Outcomes Research Methodology 3: 135–147
Yanovitzky, I., Zanutto, E., & Hornik, R. (2005). Estimating Causal Effects of Public Health Education Campaigns using Propensity Score Methodology. Journal Elsevier Evaluation and Program Planning 28 (2005) pp. 209–220.
