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Showing 2 results for Interval Censored Data

Reza Pakyari, Mohammad Rafiei, Somayeh Abolhasani,
Volume 19, Issue 6 (9-2016)
Abstract

Background: The failure time of permanent tooth is of the form of interval censored since the exact time of tooth decay is not available and it is only known that tooth decay occurs between two consecutive visits. There are a few techniques available in the literature for the problem of goodness-of-fit for interval censored data. In this paper, we propose a new goodness-of-fit testing procedure for interval censored data and employ this for the failure time of the first permanent molar tooth (sixth tooth) data.

Materials and Methods: Two methods of goodness-of-fit for interval censored data that are based on randomly generated data from each interval and averaging over the test statistics or over the p-values are employed for the failure time of the first permanent molar tooth data.

Results:  The mean of the failure time of the first permanent molar tooth is found to be at 95 months. The p-values of the two goodness-of-fit testing procedures for the Weibull, log-normal and gamma models are calculated.

Conclusion: By comparing the p-values, the log-normal model is considered as the best model to describe the failure time of the first permanent molar tooth data.


Danial Habibi, Mohammad Rafiei,
Volume 21, Issue 6 (12-2018)
Abstract

Background and Aim: Interval censored data occur in repeated data in medical studies. There are common methods to analysis this type of data. The purpose of this study is to examine the random imputation technique in the analysis of interval censored data.
Materials and Methods: Using the Monte Carlo simulation technique, we evaluate the power of Random Imputation method, and finally we assess its performance using the actual data set. Actual dataset is related to dental information in Urmia, which contains 207 children. All calculations are done using R 3.2.3 software.
Findings: The simulation results show that the power of random imputation technique is good and acceptable. The p-value in real data shows that there is no difference using the random imputation technique.
Conclusion: Random imputation technique can be used as an alternative method in comparison with other conventional methods.

 


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