Using informative priors for handling missing data problem in Cox regression

Alkan N., Terzi Y., Cengiz M. A.

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, cilt.46, sa.10, ss.7614-7623, 2017 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 46 Sayı: 10
  • Basım Tarihi: 2017
  • Doi Numarası: 10.1080/03610918.2016.1248568
  • Dergi Adı: COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.7614-7623
  • Anahtar Kelimeler: Bayesian Cox regression, Cox regression, Missing at random, Missing value
  • Akdeniz Üniversitesi Adresli: Hayır

Özet

The aim of this study is to determine the effect of informative priors for variables with missing value and to compare Bayesian Cox regression and Cox regression analysis. For this purpose, firstly simulated data sets with different sample size within different missing rate were generated and each of data sets were analysed by Cox regression and Bayesian Cox regression with informative prior. Secondly lung cancer data set as real data set was used foranalysis. Consequently, using informative priors for variables with missing value solved the missing data problem.