Non-mixture cure model for interval censored data: a simulation study

With the ongoing advance in the medical sciences, we may quite often encounter data sets where some patients have been cured from disease. Standard survival models are usually not appropriate for modeling such data because they simply do not take into account the possibility of cure. In this article, a non-mixture cure model is proposed based on lognormal distribution when the exact time of the event of disease is subject to interval censoring. The maximum likelihood estimation (MLE) method is implemented to estimate the parameters and a simulation study is conducted to assess the performance of the estimators under various conditions. The study results demonstrate that the bias, standard error, and root mean squared error values of the parameters estimates decrease with the increase in sample size and that the estimation method is more robust for data sets that have low censoring rates.

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