Parametric model based on imputations techniques for partly interval censored data
The term ‘survival analysis’ has been used in a broad sense to describe collection of statistical procedures for data analysis. In this case, outcome variable of interest is time until an event occurs where the time to failure of a specific experimental unit might be censored which can be right, lef...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
IOP Publishing
2017
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/61989/ http://irep.iium.edu.my/61989/ http://irep.iium.edu.my/61989/ http://irep.iium.edu.my/61989/7/61989_Parametric%20Model%20Based%20On%20Imputations%20Techniques_scopus.pdf http://irep.iium.edu.my/61989/13/61989_Parametric%20Model%20Based%20On%20Imputations%20Techniques_article.pdf |
| Summary: | The term ‘survival analysis’ has been used in a broad sense to describe collection of statistical procedures for data analysis. In this case, outcome variable of interest is time until an event occurs where the time to failure of a specific experimental unit might be censored which can be right, left, interval, and Partly Interval Censored data (PIC). In this paper, analysis of this model was conducted based on parametric Cox model via PIC data. Moreover, several imputation techniques were used, which are: midpoint, left & right point, random, mean, and median. Maximum likelihood estimate was considered to obtain the estimated survival function. These estimations were then compared with the existing model, such as: Turnbull and Cox model based on clinical trial data
(breast cancer data), for which it showed the validity of the proposed model. Result of data set indicated that the parametric of Cox model proved to be more superior in terms of estimation of survival functions, likelihood ratio tests, and their P-values. Moreover, based on imputation techniques; the midpoint, random, mean, and median showed better results with respect to the estimation of survival function. |
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