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.

On a Class of Transmuted Distributions to Model Survival Data with a Cure Fraction

Modelling time to event data, when there is always a proportion of the individuals, commonly referred to as immunes who do not experience the event of interest, is of importance in many biomedical studies. Improper distributions are used to model these situations and they are generally referred to as cure rate models. In the literature, two main families of cure rate models have been proposed, namely the mixture cure models and promotion time cure models. Here we propose a new model by extending the mixture model via a generating function by considering a shifted Bernoulli distribution. This gives rise to a new class of popular distributions called the transmuted class of distributions to model survival data with a cure fraction. The properties of the proposed model are investigated and parameters estimated. The Bayesian approach to the estimation of parameters is also adopted. The complexity of the likelihood function is handled through the Metropolis-Hasting algorithm. The proposed method is illustrated with few examples using different baseline distributions. A real life data set is also analysed. AMS subject classifications: 62N02, 62F15

Roles and Rules of Some Regulatory Agencies around the World during COVID-19 Pandemic

Objective: Collecting and synthesizing relevant data on COVID-19 from official sources of some different regulatory agencies around the world. Methods: The information and actions related to responding to the COVID-19 situation were collected from the websites of some regulatory agencies, including the US Food and Drug Administration (FDA), the European Medicines Agency (EMA), Health Canada (HC), Swiss Agency for Therapeutic Products (Swissmedic), and the Australian Therapeutic Goods Administration (TGA). Results: All the regulatory agencies help in expediting the development of COVID-19 treatments and medical devices. These agencies also developed an international regulatory collaboration to develop cure models for the pandemic. While some of the agencies conduct the COVID-19 testing, like the US FDA, the others do not. The agencies also differ in their approaches towards resolving the pandemic. FDA and EMA are more aggressive in a way that they prioritize more testing and hospitalization coverage. However, as of the 22nd of June 2021, the FDA authorized the highest number (388) of diagnostic COVID-19 test kits followed by TGA (128), and EMA (88). Conclusions: Although the regulatory agencies differ in their approaches towards resolving pandemic COVID-19, all regulatory agencies help in expediting the development of COVID-19 treatments and medical devices.

Determining the Factors Affecting Long-Term and Short-Term Survival of Breast Cancer Patients in Rafsanjan Using a Mixture Cure Model

Background: Breast cancer is one of the most common causes of death among women worldwide and the second leading cause of death among Iranian women. The incidence of this malignancy in Iran is 22 per 100,000 women. These patients have long-term survival time with advances in medical sciences. The present study aimed to identify the risk factors of breast cancer using Cox proportional hazard and Cox mixture cure models. Study design: It is a retrospective cohort study. Methods: In this cohort study, we recorded the survival time of 140 breast cancer patients referred to Ali Ibn Abitaleb Hospital in Rafsanjan, Iran, from 2001 to 2015. The Kaplan-Meier curve was plotted; moreover, two Cox proportional hazards and the Cox mixture cure models were fitted for the patients. Data analysis was performed using SAS 9.4 M5 software. Results: The mean age of patients was reported as 47.12 ±12.48 years at the commencement of the study. Moreover, 83.57% of patients were censored. The stage of disease was a significant variable in Cox and the survival portion of Cox mixture cure models (P=0.001). The consumption of herbal tea, tumor size, duration of the last lactation, family history of cancer, and the type of treatment were significant variables in the cured proportion of the Cox mixture cure model (P=0.001). Conclusion: The Cox mixture cure model is a flexible model which is able to distinguish between the long-term and short-term survival of breast cancer patients. For breast cancer patients, cure effective factors were the stage of the disease, consumption of herbal tea, tumor size, duration of the last lactation, family history, and the type of treatment.