Research Article
BibTex RIS Cite

Investigation of the Multiple Imputation Method in Different Missing Ratios and Sample Sizes

Year 2019, Volume: 23 Issue: 4, 605 - 609, 01.08.2019

Nesrin Alkan B. Baris Alkan

https://doi.org/10.16984/saufenbilder.507450

Abstract

In many studies, missing data are the
real trouble to researchers. Because the statistical methods are designed for
complete data sets. Multiple imputation method is developed to solve the
missing data problem. The method is also used effectively in some useful
properties of the Bayes method. If there are missing values in the data set,
Bayesian method can be used to prevent the loss of information. In this study,
the performance of the multiple imputation method is evaluated by generating
survival data with different missing rates and different sample sizes. Also,
informative priors and multiple imputation method are used together to prevent
the missing information in the variable with missing value.

Keywords

Missing Value Multiple Imputation Bayesian Cox Regression

References

  • [1] Enders, C. K. (2010). Applied Missing Data Analysis. New York: Guilford Press, pp. 165–286.
  • [2] Cox, D. R. (1972). Regression models and life tables. Journal of the Royal Statistical Society 34:187–220.
  • [3] Alkan, N. (2017). Assessing Convergence Diagnostic Tests for Bayesian Cox Regression, Communication in Statistics-Simulation and Computation, Vol.46, No.4, 3201-3212.
  • [4] Ibrahim, J. G., Chen, M. H., Sinha, D. (2001). Bayesian Survival Analysis. New York: Springer-Verlag
  • [5] Allison, P. D. (2000). Multiple imputation for missing data: a cautionary tale. Sociological Methods and Research 28:301–309.
  • [6] Rubin, D. B. (1976). Inference and missing data. Biometrika 63:581–592.
  • [7] Rubin DB. (1987). Multiple Imputation for Nonresponse in Surveys. 1st ed. New York: John Wiley&Sons; p.303.
  • [8] Schafer JL, Olsen MK. (1998). Multiple imputation for multivariate missing data problems: a data analyst’s perspective. Multivariate Behavioural Research 1998;33(1):545-71.
  • [9] Lam, P.T.,Leung, M.W., Tse, C.Y. (2007). Identifying prognostic factors for survival in advanced cancer patients: a prospectivestudy. Hong Kong Med J, 13, 453-459.
  • [10]Abreu, C. M., Chatkin, J. M., Fritscher, C. C., Wagner, M. B., Pinto, J. A. L. F. (2003). Long-term survival in lung cancer after surgical treatment: is gender a prognostic factor? (http://www.scielo.br/pdf/jbpneu/v30n1/en_v30n1a03.pdf. 26.06.2018)
  • [11] Alkan, N., Terzi, Y., Cengiz, M. A., Alkan B B. (2013). Comparison of Missing Data Analysis Methods in Cox Proportional Hazard Models. Turkiye Klinikleri Journal of Biostatistic, 5(2), 49-54.

Year 2019, Volume: 23 Issue: 4, 605 - 609, 01.08.2019

Nesrin Alkan B. Baris Alkan

https://doi.org/10.16984/saufenbilder.507450

Abstract

References

  • [1] Enders, C. K. (2010). Applied Missing Data Analysis. New York: Guilford Press, pp. 165–286.
  • [2] Cox, D. R. (1972). Regression models and life tables. Journal of the Royal Statistical Society 34:187–220.
  • [3] Alkan, N. (2017). Assessing Convergence Diagnostic Tests for Bayesian Cox Regression, Communication in Statistics-Simulation and Computation, Vol.46, No.4, 3201-3212.
  • [4] Ibrahim, J. G., Chen, M. H., Sinha, D. (2001). Bayesian Survival Analysis. New York: Springer-Verlag
  • [5] Allison, P. D. (2000). Multiple imputation for missing data: a cautionary tale. Sociological Methods and Research 28:301–309.
  • [6] Rubin, D. B. (1976). Inference and missing data. Biometrika 63:581–592.
  • [7] Rubin DB. (1987). Multiple Imputation for Nonresponse in Surveys. 1st ed. New York: John Wiley&Sons; p.303.
  • [8] Schafer JL, Olsen MK. (1998). Multiple imputation for multivariate missing data problems: a data analyst’s perspective. Multivariate Behavioural Research 1998;33(1):545-71.
  • [9] Lam, P.T.,Leung, M.W., Tse, C.Y. (2007). Identifying prognostic factors for survival in advanced cancer patients: a prospectivestudy. Hong Kong Med J, 13, 453-459.
  • [10]Abreu, C. M., Chatkin, J. M., Fritscher, C. C., Wagner, M. B., Pinto, J. A. L. F. (2003). Long-term survival in lung cancer after surgical treatment: is gender a prognostic factor? (http://www.scielo.br/pdf/jbpneu/v30n1/en_v30n1a03.pdf. 26.06.2018)
  • [11] Alkan, N., Terzi, Y., Cengiz, M. A., Alkan B B. (2013). Comparison of Missing Data Analysis Methods in Cox Proportional Hazard Models. Turkiye Klinikleri Journal of Biostatistic, 5(2), 49-54.
There are 11 citations in total.

Details

Primary Language English
Subjects Mathematical Sciences
Journal Section Research Articles
Authors

Nesrin Alkan 0000-0003-1452-4780

B. Baris Alkan 0000-0002-5851-7833

Publication Date August 1, 2019
Submission Date January 3, 2019
Acceptance Date February 5, 2019
Published in Issue Year 2019 Volume: 23 Issue: 4

Cite

APA Alkan, N., & Alkan, B. B. (2019). Investigation of the Multiple Imputation Method in Different Missing Ratios and Sample Sizes. Sakarya University Journal of Science, 23(4), 605-609. https://doi.org/10.16984/saufenbilder.507450
AMA Alkan N, Alkan BB. Investigation of the Multiple Imputation Method in Different Missing Ratios and Sample Sizes. SAUJS. August 2019;23(4):605-609. doi:10.16984/saufenbilder.507450
Chicago Alkan, Nesrin, and B. Baris Alkan. “Investigation of the Multiple Imputation Method in Different Missing Ratios and Sample Sizes”. Sakarya University Journal of Science 23, no. 4 (August 2019): 605-9. https://doi.org/10.16984/saufenbilder.507450.
EndNote Alkan N, Alkan BB (August 1, 2019) Investigation of the Multiple Imputation Method in Different Missing Ratios and Sample Sizes. Sakarya University Journal of Science 23 4 605–609.
IEEE N. Alkan and B. B. Alkan, “Investigation of the Multiple Imputation Method in Different Missing Ratios and Sample Sizes”, SAUJS, vol. 23, no. 4, pp. 605–609, 2019, doi: 10.16984/saufenbilder.507450.
ISNAD Alkan, Nesrin - Alkan, B. Baris. “Investigation of the Multiple Imputation Method in Different Missing Ratios and Sample Sizes”. Sakarya University Journal of Science 23/4 (August 2019), 605-609. https://doi.org/10.16984/saufenbilder.507450.
JAMA Alkan N, Alkan BB. Investigation of the Multiple Imputation Method in Different Missing Ratios and Sample Sizes. SAUJS. 2019;23:605–609.
MLA Alkan, Nesrin and B. Baris Alkan. “Investigation of the Multiple Imputation Method in Different Missing Ratios and Sample Sizes”. Sakarya University Journal of Science, vol. 23, no. 4, 2019, pp. 605-9, doi:10.16984/saufenbilder.507450.
Vancouver Alkan N, Alkan BB. Investigation of the Multiple Imputation Method in Different Missing Ratios and Sample Sizes. SAUJS. 2019;23(4):605-9.
 Download Cover Image

30930  This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.