Analysis of survival in breast cancer patients by using different parametric models

Amran, Syahila Enera; Abdullah, Mohd Asrul Affendi; Kek, Sie Long; Jamil, Siti Afiqah Muhamad

doi:10.1088/1742-6596/890/1/012169

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…e standard Weibull model was fitted first and denoted as Model 1. Random terms were added to capture variation at the regional level (Model 2), and extent was captured as Model 3. e decision to use the Weibull regression model was premise on the bases of previous knowledge on the distribution of breast cancer deaths [9]. e Weibull model [10] is a well-established model appropriate for modelling hazards that are either monotonically decreasing or increasing [11].…”

Section: Statistical Approachmentioning

confidence: 99%

“…e Weibull model [10] is a well-established model appropriate for modelling hazards that are either monotonically decreasing or increasing [11]. Application of parametric survival models to model breast cancer has been carried out [9,12] and elaborated on the advantages of using this model to account for censoring in data from administrative and historical databases [11]. Based on the model formulated by Nasejje et al [13], which was parameterized from Martino et al [14], we utilized the proportional hazards model of the form:…”

Section: Statistical Approachmentioning

confidence: 99%

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Bayesian and Frequentist Analytical Approaches Using Log-Normal and Gamma Frailty Parametric Models for Breast Cancer Mortality

Alotaibi

Guure

2020

Computational and Mathematical Methods in Medicine

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One of the major causes of death among females in Saudi Arabia is breast cancer. Newly diagnosed cases of breast cancer among the female population in Saudi Arabia is 19.5%. With this high incidence, it is crucial that we explore the determinants associated with breast cancer among the Saudi Arabia populace—the focus of this current study. The total sample size for this study is 8312 (8172 females and about 140 representing 1.68% males) patients that were diagnosed with advanced breast cancer. These are facility-based cross-sectional data collected over a 9-year period (2004 to 2013) from a routine health information system database. The data were obtained from the Saudi Cancer Registry (SCR). Both descriptive and inferential (Cox with log-normal and gamma frailties) statistics were conducted. The deviance information criterion (DIC), Watanabe–Akaike information criterion (WAIC), Bayesian information criterion (BIC), and Akaike information criterion were used to evaluate or discriminate between models. For all the six models fitted, the models which combined the fixed and random effects performed better than those with only the fixed effects. This is so because those models had smaller AIC and BIC values. The analyses were done using R and the INLA statistical software. There are evident disparities by regions with Riyadh, Makkah, and Eastern Province having the highest number of cancer patients at 28%, 26%, and 20% respectively. Grade II (46%) and Grade III (45%) are the most common cancer grades. Left paired site laterality (51%) and regional extent (52%) were also most common characteristics. Overall marital status, grade, and cancer extent increased the risk of a cancer patient dying. Those that were married had a hazard ratio of 1.36 (95% CI: 1.03–1.80) while widowed had a hazard ratio of 1.57 (95% CI: 1.14–2.18). Both the married and widowed were at higher risk of dying with cancer relative to respondents who had divorced. For grade, the risk was higher for all the levels, that is, Grade I (Well diff) (HR = 7.11, 95% CI: 3.32–15.23), Grade II (Mod diff) (HR = 7.89, 95% CI: 3.88–16.06), Grade III (Poor diff) (HR = 5.90, 95% CI (2.91–11.96), and Grade IV (Undiff) (HR = 5.44, 95% (2.48–11.9), relative to B-cell. These findings provide empirical evidence that information about individual patients and their region of residence is an important contributor in understanding the inequalities in cancer mortalities and that the application of robust statistical methodologies is also needed to better understand these issues well.

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Section: Statistical Approachmentioning

confidence: 99%

Section: Statistical Approachmentioning

confidence: 99%

Bayesian and Frequentist Analytical Approaches Using Log-Normal and Gamma Frailty Parametric Models for Breast Cancer Mortality

Alotaibi

Guure

2020

Computational and Mathematical Methods in Medicine

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High‐dimensional regression with ordered multiple categorical predictors

Huang

Hang

Chao

2019

Statistics in Medicine

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Models for the ordered multiple categorical (OMC) response variable have already been extensively established and widely applied, but few studies have investigated linear regression problems with OMC predictors, especially in high‐dimensional situations. In such settings, the pseudocategories of the discrete variable and other irrelevant explanatory variables need to be automatically selected. This paper introduces a transformation method of dummy variables for such OMC predictors, an L1 penalty regression method is proposed based on the transformation. Model selection consistency of the proposed method is derived under some common assumptions for high‐dimensional situation. Both simulation studies and real data analysis present good performance of this method, showing its wide applicability in relevant regression analysis.

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Bootstrapping Simulation in Determining the Prognostic Factors of Lung Cancer Disease by Parametric Survival Analysis

Muhamad Jamil,

Affendi Abdullah,

Ibrahim

et al. 2023

2023 IEEE International Conference on Computing (ICOCO)

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Analysis of survival in breast cancer patients by using different parametric models

Cited by 3 publications

References 5 publications

Bayesian and Frequentist Analytical Approaches Using Log-Normal and Gamma Frailty Parametric Models for Breast Cancer Mortality

Bayesian and Frequentist Analytical Approaches Using Log-Normal and Gamma Frailty Parametric Models for Breast Cancer Mortality

High‐dimensional regression with ordered multiple categorical predictors

Bootstrapping Simulation in Determining the Prognostic Factors of Lung Cancer Disease by Parametric Survival Analysis

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