Analysis of Interval Censored Data Using Random Imputation Technique

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. 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. Random imputation technique can be used as an alternative method in comparison with other conventional methods.