Application of Modified Generalized–Gamma Mixture Cure Model in the Analysis of Ovarian Cancer Data

The modeling and analysis of lifetime for terminal diseases such as cancer is a significant aspect of statistical work. This study considered data from thirty-seven women diagnosed with Ovarian Cancer and hospitalized for care at theDepartment of Obstetrics and Gynecology, University of Ibadan, Nigeria. Focus was on the application of a parametric mixture cure model that can handle skewness associated with survival data – a modified generalized-gamma mixture cure model (MGGMCM). The effectiveness of MGGMCM was compared with existing parametric mixture cure models using Akaike Information Criterion, median time-to-cure and variance of the cure rate. It was observed that the MGGMCM is an improved parametric model for the mixture cure model.

Nonparametric Inference for Mixture Cure Model When Cure Information Is Partially Available

We introduce nonparametric estimators to estimate the conditional survival function, cure probability and latency function in the setting of a mixture cure model when the cure status is partially known. For the sake of illustration, we present an application concerning patients hospitalized with COVID-19 in Galicia (Spain) during the first outbreak of the epidemic.

An improved method for the effect estimation of the intermediate event on the outcome based on the susceptible pre-identification

Background In follow-up studies, the occurrence of the intermediate event may influence the risk of the outcome of interest. Existing methods estimate the effect of the intermediate event by including a time-varying covariate in the outcome model. However, the insusceptible fraction to the intermediate event in the study population has not been considered in the literature, leading to effect estimation bias due to the inaccurate dataset. Methods In this paper, we propose a new effect estimation method, in which the susceptible subpopulation is identified firstly so that the estimation could be conducted in the right population. Then, the effect is estimated via the extended Cox regression and landmark methods in the identified susceptible subpopulation. For susceptibility identification, patients with observed intermediate event time are classified as susceptible. Based on the mixture cure model fitted the incidence and time of the intermediate event, the susceptibility of the patient with censored intermediate event time is predicted by the residual intermediate event time imputation. The effect estimation performance of the new method was investigated in various scenarios via Monte-Carlo simulations with the performance of existing methods serving as the comparison. The application of the proposed method to mycosis fungoides data has been reported as an example. Results The simulation results show that the estimation bias of the proposed method is smaller than that of the existing methods, especially in the case of a large insusceptible fraction. The results hold for small sample sizes. Besides, the estimation bias of the new method decreases with the increase of the covariates, especially continuous covariates, in the mixture cure model. The heterogeneity of the effect of covariates on the outcome in the insusceptible and susceptible subpopulation, as well as the landmark time, does not affect the estimation performance of the new method. Conclusions Based on the pre-identification of the susceptible, the proposed new method could improve the effect estimation accuracy of the intermediate event on the outcome when there is an insusceptible fraction to the intermediate event in the study population.