scholarly journals A sup‐score test for the cure fraction in mixture models for long‐term survivors

Biometrics ◽  
2016 ◽  
Vol 72 (4) ◽  
pp. 1348-1357 ◽  
Author(s):  
Wei‐Wen Hsu ◽  
David Todem ◽  
KyungMann Kim
Keyword(s):  
Mixture Models ◽  
Score Test ◽  
Cure Fraction ◽  
Long Term Survivors

scholarly journals Exponential Mixture Models with Long-Term Survivors and Covariates

1994 ◽  
Vol 49 (2) ◽  
pp. 218-241 ◽  
Author(s):  
M.E. Ghitany ◽  
R.A. Maller ◽  
S. Zhou
Keyword(s):  
Mixture Models ◽  
Exponential Mixture ◽  
Long Term Survivors

scholarly journals Nadarajah-Haghighi Model for Survival Data With Long Term Survivors in the Presence of Right Censored Data

2021 ◽  
pp. 695-709
Author(s):  
Umar Usman ◽  
Shamsuddeen Suleiman ◽  
Bello Magaji Arkilla ◽  
Yakubu Aliyu
Keyword(s):  
Survival Data ◽  
Likelihood Estimation ◽  
Estimation Procedure ◽  
Right Censoring ◽  
Right Censored Data ◽  
Term Survival ◽  
Proposed Model ◽  
Cure Fraction ◽  
Long Term Survivors

In this paper, a new long term survival model called Nadarajah-Haghighi model for survival data with long term survivors was proposed. The model is used in fitting data where the population of interest is a mixture of individuals that are susceptible to the event of interest and individuals that are not susceptible to the event of interest. The statistical properties of the proposed model including quantile function, moments, mean and variance were provided. Maximum likelihood estimation procedure was used to estimate the parameters of the model assuming right censoring. Furthermore, Bayesian method of estimation was also employed in estimating the parameters of the model assuming right censoring. Simulations study was performed in order to ascertain the performances of the MLE estimators. Random samples of different sample sizes were generated from the model with some arbitrary values for the parameters for 5%, 1:3% and 1:5% cure fraction values. Bias, standard error and mean square error were used as discrimination criteria. Additionally, we compared the performance of the proposed model with some competing models. The results of the applications indicates that the proposed model is more efficient than the models compared with. Finally, we fitted some models considering type of treatment as a covariate. It was observed that the covariate  have effect on the shape parameter of the proposed model.

scholarly journals Mixture and Non-Mixture Cure Fraction Models Based on Generalized Gompertz Distribution under Bayesian Approach

2016 ◽  
Vol 66 (1) ◽  
pp. 121-135 ◽  
Author(s):  
Prafulla Kumar Swain ◽  
Gurprit Grover ◽  
Komal Goel
Keyword(s):  
Monte Carlo ◽  
Censored Data ◽  
Bayesian Approach ◽  
New Drugs ◽  
Lifetime Data ◽  
Gompertz Distribution ◽  
Cure Fraction ◽  
Long Term Survivors ◽  
Fraction Models

Abstract The cure fraction models are generally used to model lifetime data with long term survivors. In a cohort of cancer patients, it has been observed that due to the development of new drugs some patients are cured permanently, and some are not cured. The patients who are cured permanently are called cured or long term survivors while patients who experience the recurrence of the disease are termed as susceptibles or uncured. Thus, the population is divided into two groups: a group of cured individuals and a group of susceptible individuals. The proportion of cured individuals after the treatment is typically known as the cure fraction. In this paper, we have introduced a three parameter Gompertz (viz. scale, shape and acceleration) or generalized Gompertz distribution in the presence of cure fraction, censored data and covariates for estimating the proportion of cure fraction through Bayesian Approach. Inferences are obtained using the standard Markov Chain Monte Carlo technique in openBUGS software.

scholarly journals Estimating a Logistic Weibull Mixture Models with Long–Term Survivors

2012 ◽  
Author(s):  
Mohd. Rizam Abu Bakar ◽  
Isa Daud ◽  
Noor Akma Ibrahim ◽  
Desi Rahmatina
Keyword(s):  
Heart Transplantation ◽  
Key Words ◽  
Mixture Models ◽  
Mixture Model ◽  
Weibull Model ◽  
Mixed Population ◽  
Simple Logistic ◽  
Long Term Survivors

Model gabungan mempostulatkan sebuah populasi yang terdiri daripada dua jenis individu iaitu peka dan kebal. Individu yang peka merupakan individu yang berisiko mengalami peristiwa terhadap kajian yang dibuat. Manakala individu yang kebal adalah individu yang tidak berisiko mengalami peristiwa tersebut. Kertas kerja ini memfokuskan kepada kovariat pada individu seperti umur, pembedahan dan pemindahan yang dihubungkan dengan kebarangkalian wujudnya kebal dalam sebuah model logistik Weibull dan menghuraikan kesan pemindahan jantung terhadap masa hayat seterusnya. Kata kunci: Model gabungan, model logistik Weibull mudah, model Weibull belahan, model logistik Weibull belahan The mixture model postulates a mixed population with two types of individuals, the susceptible and long–term survivors. The susceptibles are at the risk of developing the event under consideration. However, the long–term survivors or immune individuals will never experience the event. This paper focuses on the covariates associated with individuals such as age, surgery and transplant related to the probability of being immune in a logistic Weibull model and to evaluate the effect of heart transplantation on subsequent survival. Key words: Mixture model, simple logistic Weibull model, split Weibull model, split logistic Weibull model

scholarly journals On Containment Plan Amid COVID-19 in Red Zone Districts of India: Using Clinical Life Table and Cure Fraction Model

2020 ◽  
Vol 52 (04) ◽  
pp. 39-48
Author(s):  
Arpan Kumar Thakur ◽  
Keyword(s):  
General Population ◽  
Population Density ◽  
Life Table ◽  
Hot Spot ◽  
Disease Free Survival ◽  
Total Sample ◽  
Family Welfare ◽  
Cure Fraction ◽  
Long Term Survivors

Background and Objective: Ministry of Health and Family Welfare, Government of India took many measures to arrest the spread of COVID-19 disease. This research is intended to shed light on number of confirmed cases with respect to population density of the affected districts, to study the proportion of positives among total sample tested, to construct clinical life table of general population w.r.t. number of daily positive cases and to estimate the long-term survivors among general population. Materials and Methods: Simple scatter plot has been used to see relation between population density and number of cases in different districts of India, cluster analysis technique is used for making cluster of Districts having similar features. Clinical life table is prepared for general population of affected Districts, and mixture & non-mixture cure fraction models used to estimate the proportion of long-term survivors (disease free survival) of general population. Result: Median daily proportion of positives are found to be below 0.05. In 79 identified hot spot Districts average population is very high (36.29 lakhs) with population density of 3404 per square kilometre. Even among those Districts there are huge inter cluster differences w.r.t. number of cases and population density. Clinical life table is constructed for general population of 429 affected Districts, increasing pattern in hazard is found even though study period is small. Long term survivors of disease is simulated and found to be 99.812%. Conclusion: Government ought to make cluster of Districts among red zone Districts, clustering should be based on number of cases and population density. Different containment strategy may be prepared for each cluster of Districts.

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