Application of a Mixture Cure Fraction Model Based on the Generalized Modified Weibull Distribution for Analyzing Survival of Patients with Breast Cancer

Naseri, Parisa; Baghestani, Ahmad Reza; Momenyan, Narges; Akbari, Mohammad Esmaeil

doi:10.5812/ijcm.62863

Cited by 10 publications

(10 citation statements)

References 37 publications

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“…In the previous studies, which aimed to determine the risk factors associated with breast cancer, the Bayesian mixture cure fraction model was used, based on Generalized Modified Weibull distribution. In those studies, as in the present study, tumor metastasis status was found to be an effective factor in the survival of patients (Karimi et al, 2014;Naseri et al, 2018).…”

Section: Discussionsupporting

confidence: 78%

Factors Affecting Long-Survival of Patients with Breast Cancer by Non-Mixture and Mixture Cure Models Using the Weibull, Log-logistic and Dagum Distributions: A Bayesian Approach

Rafati

Baneshi

Bahrampour

2020

Asian Pac J Cancer Prev

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Breast cancer is the most prevalent cancer and the second cause of cancer-related death among Iranian women, following gastric carcinoma (Asadabadi et al., 2014; Karimi et al., 2014; Moqaddasi-Amiri and Bahrampour, 2015; Rezaianzadeh et al., 2017). The mean age of Iranian breast cancer patients is 30.0 years; these patients are diagnosed with cancer at least a decade earlier compared with the developed countries(Abedi et al., 2016; Rahimzadeh et al., 2016). As this cancer occurs progressively in women worldwide, it is of paramount importance to identify factors that affect the survival of breast cancer patients. An assumption behind common survival models such as

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Section: Discussionsupporting

confidence: 78%

Factors Affecting Long-Survival of Patients with Breast Cancer by Non-Mixture and Mixture Cure Models Using the Weibull, Log-logistic and Dagum Distributions: A Bayesian Approach

Rafati

Baneshi

Bahrampour

2020

Asian Pac J Cancer Prev

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“…This research combined the cure fraction model based on generalized modified Weibull distribution and the inferences obtained using Markov chain Monte Carlo method, which is a Bayesian approach for determining the risk factors associated with breast cancer [18].…”

Section: Literature Reviewmentioning

confidence: 99%

Generalized cure rate model for infectious diseases with possible co-infections

et al. 2020

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This research mainly aims to develop a generalized cure rate model, estimate the proportion of cured patients and their survival rate, and identify the risk factors associated with infectious diseases. The generalized cure rate model is based on bounded cumulative hazard function, which is a non-mixture model, and is developed using a two-parameter Weibull distribution as the baseline distribution, to estimate the cure rate using maximum likelihood method and real data with R and STATA software. The results showed that the cure rate of tuberculosis (TB) patients was 26.3%, which was higher than that of TB patients coinfected with human immunodeficiency virus (HIV; 23.1%). The non-parametric median survival time of TB patients was 51 months, while that of TB patients co-infected with HIV was 33 months. Moreover, no risk factors were associated with TB patients co-infected with HIV, while age was a significant risk factor for TB patients among the suspected risk factors considered. Furthermore, the bounded cumulative hazard function was extended to accommodate infectious diseases with co-infections by deriving an appropriate probability density function, determining the distribution, and using real data. Governments and related health authorities are also encouraged to take appropriate actions to combat infectious diseases with possible co-infections.

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“…Example of such studies include time taken for a patient to recover from diseases, time to die from a disease, and the likes. Several studies have been conducted using parametric models to analyse health related issues (Awodutire et al, 2018;Naseri, et al, 2018). In recent times, survival analysis has been extended to finances (Laitinen, 2005;Witzany et al, 2012;Lee, 2014) engineering (reliability analysis, Awodutire et al, 2021), politics (Jonathan, 2014) and similar.…”

Section: Introductionmentioning

confidence: 99%

Parametric modelling of rainfall return periods in south-western Nigeria: Survival analysis approach

Awodutire¹,

Sasanya

Ufuoma³

et al. 2022

F1000Res

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Background: Rainfall is the main source of water on the earth’s surface. It infiltrates and percolates deep into the soil for groundwater recharge. Rainfall patterns, amounts, durations, and intensities can vary daily, monthly, annually, and spatially. It is therefore important to accurately estimate rainfall return periods, which can be employed in hydraulic design and flood control measures. Methods: This research considered the survival analysis approach for the prediction of rainfall return periods including intensity, and months during which these would occur in south-western Nigeria. Twenty years’ of annual rainfall data were obtained from three metrological stations and these were subjected to nine different probability plotting position methods. Results from the plotting positions was further subjected to four survival models using five years of censor time. The Akaike Information Criterion (AIC) was used to determine the best-fitting model for the dataset. Results: The Laplace probability plotting position in conjunction with the log-logistic distribution best describes the datasets, since it gave the lowest AIC value of 22.53. The log-logistic distribution is also suitable for the prediction of return period from the Weibull probability plotting position since the AIC values were 6.934 and -4.332 respectively. The Hirsh plotting position in conjunction with the Weibull distribution is also suitable for the description of the dataset. Conclusion: The established parametric models are suitable for the accurate prediction of return periods of peak rainfall events during any month of the year.

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Application of a Mixture Cure Fraction Model Based on the Generalized Modified Weibull Distribution for Analyzing Survival of Patients with Breast Cancer

Cited by 10 publications

References 37 publications

Factors Affecting Long-Survival of Patients with Breast Cancer by Non-Mixture and Mixture Cure Models Using the Weibull, Log-logistic and Dagum Distributions: A Bayesian Approach

Factors Affecting Long-Survival of Patients with Breast Cancer by Non-Mixture and Mixture Cure Models Using the Weibull, Log-logistic and Dagum Distributions: A Bayesian Approach

Generalized cure rate model for infectious diseases with possible co-infections

Parametric modelling of rainfall return periods in south-western Nigeria: Survival analysis approach

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