An analytical approach on parametric estimation of cure fraction based on weibull distribution using interval censored data.

In this article, we consider the Bounded Cumulative Hazard (BCH) model that is more appropriate than mixture cure model in case of cancer clinical trials when the population of interest contains long-term survivors or cured. We propose this cure rate model based on the Weibull distribution with interval censored data. Maximum likelihood estimation (MLE) method is proposed to estimate the parameters within the framework of expectation-maximization (EM) algorithm, Newton Raphson method also employed. The analysis showed that the cure fraction cannot be obtained analytically, but may be obtained from the numerical solution of the estimated equations. A simulation study is also provided for assessing the efficiency of the proposed estimation procedure

Turnbull versus Kaplan-Meier estimators of cure rate estimation using interval censored data

This study deals with the analysis of the cure rate estimation based on the Bounded Cumulative Hazard (BCH) model using interval censored data, given that the exact distribution of the data set is unknown. Thus, the non-parametric estimation methods are employed by means of the EM algorithm. The Turnbull and Kaplan Meier estimators were proposed to estimate the survival function, even though the Kaplan Meier estimator faces some restrictions in term of interval survival data. A comparison of the cure rate estimation based on the two estimators was done through a simulation study

Parametric estimation of the immunes proportion based on BCH model and exponential distribution using left censored data.

In population based cancer clinical trials, a proportion of patients will never experience the interested event and considered as "cured" or "immunes". The majority of recent cancer studies focus on the estimation of immune proportion. In this study we investigated the estimation of proportion of patients curd of cancer in case of left censored data based on the Bounded Cumulative Hazard (BCH) model proposed by Chen in 1999. The analysis provided the Maximum Likelihood Estimation (MLE) of the parameters within the framework of the Expectation Maximization (EM) algorithm where the numerical solutions of the estimation equations of the cure rate parameter could be employed

Parametric maximum likelihood estimation of cure fraction using interval-censored data

A significant proportion of patients in cancer clinical trials can be cured. That is, the symptoms of the disease disappear completely and the disease never recurs. In this article, the focus is on estimation of the proportion of patients who are cured. The parametric maximum likelihood estimation method was used for estimation of the cure fraction based on application of the bounded cumulative hazard (BCH) model to interval-censored data. We ran the analysis using the EM algorithm considering two cases: i) when no covariates were involved in the estimation, and ii) when some covariates were involved. This paper shows derivation of the estimation equations for the cure rate parameter followed by a simulation study

Non-parametric maximum likelihood estimation of cure fraction for interval survival data.

In cancer clinical trials, a significant fraction of patients can be cured, that is, the symptoms of the disease are completely eliminated, so that it will never recurs. In this article the focus is on the estimation of the proportion of patients who are cured. The Nonparametric maximum likelihood estimation method is used for interval censored data based on the bounded cumulative hazard (BCH) model. We implement the Turnbull algorithm for the survival function estimation using EM algorithm. The analysis shows the analytical solution of the estimating equations for the cure proportion followed by a simulation study