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

Yıl 2019, Cilt: 23 Sayı: 4, 605 - 609, 01.08.2019

Nesrin Alkan B. Baris Alkan

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

Öz

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.

Anahtar Kelimeler

Missing Value Multiple Imputation Bayesian Cox Regression

Kaynakça

  • [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.

Yıl 2019, Cilt: 23 Sayı: 4, 605 - 609, 01.08.2019

Nesrin Alkan B. Baris Alkan

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

Öz

Kaynakça

  • [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.
Toplam 11 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Matematik
Bölüm Araştırma Makalesi
Yazarlar

Nesrin Alkan 0000-0003-1452-4780

B. Baris Alkan 0000-0002-5851-7833

Yayımlanma Tarihi 1 Ağustos 2019
Gönderilme Tarihi 3 Ocak 2019
Kabul Tarihi 5 Şubat 2019
Yayımlandığı Sayı Yıl 2019 Cilt: 23 Sayı: 4

Kaynak Göster

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. Ağustos 2019;23(4):605-609. doi:10.16984/saufenbilder.507450
Chicago Alkan, Nesrin, ve B. Baris Alkan. “Investigation of the Multiple Imputation Method in Different Missing Ratios and Sample Sizes”. Sakarya University Journal of Science 23, sy. 4 (Ağustos 2019): 605-9. https://doi.org/10.16984/saufenbilder.507450.
EndNote Alkan N, Alkan BB (01 Ağustos 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 ve B. B. Alkan, “Investigation of the Multiple Imputation Method in Different Missing Ratios and Sample Sizes”, SAUJS, c. 23, sy. 4, ss. 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 (Ağustos 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 ve B. Baris Alkan. “Investigation of the Multiple Imputation Method in Different Missing Ratios and Sample Sizes”. Sakarya University Journal of Science, c. 23, sy. 4, 2019, ss. 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.
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Sakarya University Journal of Science (SAUJS)