scholarly journals Statistical methods for estimating cure fraction of COVID-19 patients in India

2021 ◽  
Vol 16 (1) ◽  
pp. 59-64
Author(s):  
E.P. Sreedevi ◽  
P.G. Sankaran
Keyword(s):  
Statistical Methods ◽  
Proportional Hazards ◽  
Human Race ◽  
Mixture Cure Model ◽  
Long Period ◽  
Cure Fraction ◽  
Gender And Age ◽  
Lifetime Data Analysis ◽  
Long Term Survivors

Human race is under the COVID-19 pandemic menace since beginning of the year 2020. Even though the disease is easily transmissible, a massive fraction of the affected people is recovering. Most of the recovered patients will not experience death due to COVID-19, even if they observed for a long period. They can be treated as long term survivors in the context of lifetime data analysis. In this article, we present statistical methods to estimate the proportion of long term survivors (cure fraction) of the COVID-19 patient population in India. The proportional hazards mixture cure model is used to estimate the cure fraction and the effect of the covariates gender and age, on lifetime. We can see that the cure fraction of the COVID-19 patients in India is more than 90%, which is indeed an optimistic information.

scholarly journals Statistical Methods for Estimating Cure Fraction of COVID-19 Patients In India

2020 ◽  
Author(s):  
E. P. Sreedevi ◽  
P. G. Sankaran
Keyword(s):  
Statistical Methods ◽  
Proportional Hazards ◽  
Human Race ◽  
Mixture Cure Model ◽  
Long Period ◽  
Cure Fraction ◽  
Gender And Age ◽  
Lifetime Data Analysis ◽  
Long Term Survivors

AbstractThe human race is under the COVID-19 pandemic menace since beginning of the year 2020. Even though the disease is easily transmissible, a massive fraction of the affected people are recovering. Most of the recovered patients will not experience death due to COVID-19, even if they observed for a long period. They can be treated as long term survivors (cured population) in the context of lifetime data analysis. In this article, we present some statistical methods to estimate the cure fraction of the COVID-19 patients in India. Proportional hazards mixture cure model is used to estimate the cure fraction and the effect of covariates gender and age on lifetime. The data available on website ‘https://api.cvoid19india.org’ is used in this study. We can see that, the cure fraction of the COVID-19 patients in India is more than 90%, which is indeed an optimistic information.

Survival trends in chronic lymphocytic leukemia in the era of oral targeted therapies in the United States: SEER database analyses (1985 to 2017).

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 7524-7524
Author(s):  
Neda Alrawashdh ◽  
Ali McBride ◽  
Daniel O. Persky ◽  
Joann Sweasy ◽  
Brian Erstad ◽  
...  
Keyword(s):  
Chronic Lymphocytic Leukemia ◽  
Survival Rates ◽  
Relative Survival ◽  
Lymphocytic Leukemia ◽  
Disease Stage ◽  
Seer Database ◽  
Cure Model ◽  
Gender And Age ◽  
Long Term Survivors

7524 Background: The survival of chronic lymphocytic leukemia (CLL) patients has progressively improved after the approval of new targeted therapy for first-line treatment and relapsed disease. We performed a corresponding analysis from the U.S. population-based SEER database (1973–2017) to explore the trend of survival and the effect of advanced CLL treatment on overall survival in CLL patients. Methods: Data were extracted from SEER*Stat for all patients 15 years or older with a primary diagnosis of CLL with or without subsequent cancers. A period analysis was performed to estimate the 5- and 10-year relative survival rates for patients diagnosed (dx) during different calendar periods from 1985 to 2017, based on gender and age at time of diagnosis (15–44, 45–54, 55–64, 65–74, 75–84, 85 years or older). A mixture cure model was used to examine the proportion of long-term survivors per gender and age category among CLL patients diagnosed between 1985 and 2015. Cox proportional hazard modeling was used to calculate the hazard ratios (HRs) of death adjusted for gender and age at diagnosis for two cohorts: (a) diagnosed in 2000–2003 and followed to 2012; (b) 2004–2007 and followed to 2015. Results: For males, the 5-year age-adjusted relative survival rate improved progressively from 72.0% (dx 1985-1989) to 88.2% (dx 2010-2014); for females, from 76.8% (dx 1985-1989) to 90.8% (dx 2010-2014). The corresponding 10-year age-adjusted relative survival rates were 47.3% (dx 1985-1989) and 72.5% (dx 2005-2009) for males; and 58.2% (dx 1985-1989) and 78.7% (dx 2005-2009) for females. The table below shows the proportions of long-term survivors for the 1985–2017 cohort as estimated in the mixed cure model. The HRs (95%CI) of death for cohort (b) in comparison to cohort (a) were 0.58 (0.43–0.78), 0.58 (0.48–0.70), 0.57 (0.49–0.67), 0.68 (0.54–0.85); and 0.83 (0.68–1.02) for age categories of 45–54, 55–64, 65–74, 75–84, and 85 years or old. Conclusions: Survival is significantly improved by calendar period among patients diagnosed after 2004 and treated in the era of advanced therapies. Females and younger patients had a higher probability of long term survival. Future studies should consider such covariates as treatment type, disease stage and genetics.[Table: see text]

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 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 A new perspective on the BO06 trial in osteosarcoma: short- and long-term prognostic value of histologic response and intensified chemotherapy

2021 ◽  
Author(s):  
Eni Musta ◽  
Nan van Geloven ◽  
Jakob Anninga ◽  
Hans Gelderblom ◽  
Marta Fiocco
Keyword(s):  
Prognostic Value ◽  
Cox Regression ◽  
Progression Free Survival ◽  
Histologic Response ◽  
Mixture Cure Model ◽  
Cure Fraction ◽  
Short And Long Term ◽  
New Perspective ◽  
Intensified Chemotherapy

Purpose. Despite evidence of cured patients, previous osteosarcoma studies have not taken it into consideration. We aim to better understand the prognostic value of histologic response and chemotherapy intensification on cure fraction and progression-free survival (PFS) for the uncured patients. Methods. A logistic model is assumed for the effect of histologic response and intensified chemotherapy on the cure status, while a Cox regression model is estimated only for the uncured patients on PFS. The mixture cure model is used to simultaneously study these two effects. Results. Histologic response is a strong prognostic factor for the cure status (OR: 3.00 [1.75-5.17]), but it has no clear effect on PFS for the uncured patients (HR: 0.78 [0.53-1.16]). The cure fractions are 55% [46%-63%] and 29% [22%-35%] among GR and patients with poor histologic response (PR) respectively. The intensified regimen was associated with higher cure fraction among PR (OR: 1.90 [0.93-3.89]), with no evidence of effect for GR (OR: 0.78 [0.38-1.59]). Conclusions. Accounting for cured patients is valuable in distinguishing the covariate effects on cure and PFS for the uncured patients. Estimating cure chances based on these prognostic factors is relevant for counseling patients and can also affect treatment decisions.

scholarly journals Survival Analysis in The Presence of a Cure Fraction Using Data of Dialysis Patients: A Bayesian Approach

2021 ◽  
Author(s):  
Shideh Rafati ◽  
Mohammad Reza Baneshi ◽  
Laleh Hassani ◽  
Abbas Bahrampour
Keyword(s):  
Goodness Of Fit ◽  
Significant Proportion ◽  
Dialysis Patients ◽  
Term Survival ◽  
Long Term Survival ◽  
Mixture Cure Model ◽  
Cure Fraction ◽  
Dagum Distribution ◽  
Cure Models

Abstract Background: The aim of this study was to evaluate the goodness of fit of Bayesian mixture and non-mixture cure models to find the factors affecting dialysis patient’s survival time where a significant proportion of the population has a long-term survival.Study Design: A retrospective cohort study. Methods: The data of 252 dialysis patients were used among whom 35 cases died. Since in this study a part of the population had long-term survival, Bayesian cure models were used and evaluated using DIC index. The data were analyzed by R and Openbugs Softwares. Results: Of the 252 dialysis patients, 136(54%) were males and the mean (SD) age was 53.39 (18.09) years. The patient’s follow-up time mean (SD) was 10.93(7.82) years. The 10 and 20-year survival rate of these patients were 87% and 73%, respectively. The findings show that the best fitting belonged to the Bayesian Non-mixture Cure Model (BNCM) with Dagum distribution. The variables of age, Body Mass Index, dialysis duration, frequency of dialysis, age of onset of dialysis, and occupation affected patients' survival based on BNCM with Dagum distribution.Conclusions: The results demonstrated that the BNCM with Dagum distribution can be a good selection model to analyze survival data, where there is the possibility of a fraction of cure.

Modeling clustered long-term survivors using marginal mixture cure model

2018 ◽  
Vol 60 (4) ◽  
pp. 780-796 ◽  
Author(s):  
Yi Niu ◽  
Lixin Song ◽  
Yufeng Liu ◽  
Yingwei Peng
Keyword(s):  
Cure Model ◽  
Mixture Cure Model ◽  
Long Term Survivors
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