Yazar "Cengiz, M. Ali" seçeneğine göre listele

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    Comparison of Missing Data Analysis Methods in Cox Proportional Hazard Models
    (Türkiye Klinikleri Journal of Biostatistics, 2013) Alkan, Nesrin; Terzi, Yüksel; Cengiz, M. Ali; Alkan, B. Barış
    Data with missing value are common in clinical studies. This study investigated to assess the effects of different missing data analysis techniques on the performance of Cox proportional hazard model. Material and Methods: In order to see how sample size and missing rate effect the missing data analysis techniques, we derived the survival data with 25, 50 and 100 sample sizes. Some elements of the survival data with different sample size were deleted in different rates under MAR (Missing at Random) assumption to generate incomplete data sets which had 5%, 10%, 20% and 40% missing value for each data. Data sets with missing values were completed by five missing data analysis techniques (complete case Analysis-CCA, mean imputation, regression imputation-REG, expectation maximization-EM algorithm, multiple imputation-MI). The new completed data sets were analyzed by Cox proportional hazard model and their results were compared with results of original data. Results: The difference between the techniques grew for increasing missing rate and while the sample size increased the methods were similar to each other. CCA was the most affected from sample size. The estimates from the methods REG, EM and MI were very similar to each other and real value. Conclusion: Multiple imputation method as impute more than one value for each missing value should be preferred instead of single imputation methods as impute only one value for each missing value.
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    Öğe
    Using informative priors for handling missing data problem in Cox regression
    (Taylor & Francis Inc, 2017) Alkan, Nesrin; Terzi, Yuksel; Cengiz, M. Ali
    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.