DOI: https://doi.org/10.14710/jfma.v2i2.36

METODE MULTIPLE IMPUTATION UNTUK MENGATASI KOVARIAT TAK LENGKAP PADA DATA KEJADIAN BERULANG

*Rianti Siswi Utami  -  Departemen Matematika, Universitas Gadjah Mada, Yogyakarta, Indonesia
Danardono Danardono  -  Departemen Matematika, Universitas Gadjah Mada, Yogyakarta, Indonesia

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Abstract

Multiple imputation is one of estimation method used to impute missing observations. This method imputes missing observation several times then it is more possible to get the right estimate than just one time imputation. In this research, the method will be applied to estimate missing observations in covariates of recurrent event data. Some multiple imputation methods will be considered including combination of the event indicator, the event  times,   the logarithm of event times, and the cumulative baseline hazard. To compare these methods, Monte Carlo simulation will be used based on relative bias and Mean Squared Error (MSE). The recurrent events will be modelled using Cox proportional hazard model. Furthermore, real data application will be presented.

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Section: FUNDAMENTAL MATHEMATICS AND APPLICATIONS
Language : ID
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  3. Y.C. Yuan, Multiple Imputation for Missing Data: Concepts and New Development, Artikel, SAS Institute Inc., Rocville, MD, 2005
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  6. I.R. White, dan P. Royston, Imputing Missing CovariateValues for The Cox Model, Statistics in Medicine, vol. 28, no. 15, pp. 1982-1998, 2009
  7. R.L. Prentice, B.J. Williams, dan A.V. Peterson, On the Regression Analysis of Multivariate Failure Time Data, Biometrika, vol. 68, pp. 373—379, 1981
  8. L.J. Wei, D.Y. Lin, dan L. Weissfeld, Regression Analysis of Multivariate Incomplete Failure Time Data by Modeling Marginal Distribution, Journal of the American Statistical Association, vol. 84, pp. 1065—1073, 1989
  9. X. Liu, Survival Analysis: Models and Applications, John Wiley & Sons, Inc, New York, 2012
  10. R.J.A. Little dan D.B. Rubin, Statistical Analysis with Missing Data, edisi 2, Wiley: Hoboken, N.J., 2002
  11. J.P. Klein dan M.L. Moeschberger, Survival Analysis Techniques for Censored and Truncated Data, edisi 2, Springer-Verlag, New York, 2003
  12. H.J. Lim dan X. Zhang, Additive and Multiplicative Hazards Modeling for Recurrent Event Data Analysis, BMC Medical Research Methodology, vol. 11, pp. 101 - 121, 2011
  13. Z.A. Baso dan J. Raharjo, Kesehatan Reproduksi, Panduan Bagi Perempuan, Pustaka Pelajar, Yogyakarta, 1999
  14. T. Suwarni, Faktor Determinan yang Mempengaruhi Siklus Menstruasi, Indonesian Journal On Medical Science, vol. 2, no. 1, pp. 33 - 38, 2015

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