Bayesian Analysis of Cancer Data Using a 4-Component Exponential Mixture Model. 2021

Farzana Noor, and Saadia Masood, and Yumna Sabar, and Syed Bilal Hussain Shah, and Touqeer Ahmad, and Asrin Abdollahi, and Ahthasham Sajid
Department of Mathematics &Statistics, International Islamic University, Islamabad, Pakistan.

Cancer is among the major public health problems as well as a burden for Pakistan. About 148,000 new patients are diagnosed with cancer each year, and almost 100,000 patients die due to this fatal disease. Lung, breast, liver, cervical, blood/bone marrow, and oral cancers are the most common cancers in Pakistan. Perhaps smoking, physical inactivity, infections, exposure to toxins, and unhealthy diet are the main factors responsible for the spread of cancer. We preferred a novel four-component mixture model under Bayesian estimation to estimate the average number of incidences and death of both genders in different age groups. For this purpose, we considered 28 different kinds of cancers diagnosed in recent years. Data of registered patients all over Pakistan in the year 2012 were taken from GLOBOCAN. All the patients were divided into 4 age groups and also split based on genders to be applied to the proposed mixture model. Bayesian analysis is performed on the data using a four-component exponential mixture model. Estimators for mixture model parameters are derived under Bayesian procedures using three different priors and two loss functions. Simulation study and graphical representation for the estimates are also presented. It is noted from analysis of real data that the Bayes estimates under LINEX loss assuming Jeffreys' prior is more efficient for the no. of incidences in male and female. As far as no. of deaths are concerned again, LINEX loss assuming Jeffreys' prior gives better results for the male population, but for the female population, the best loss function is SELF assuming Jeffreys' prior.

UI MeSH Term Description Entries
D008297 Male Males
D009369 Neoplasms New abnormal growth of tissue. Malignant neoplasms show a greater degree of anaplasia and have the properties of invasion and metastasis, compared to benign neoplasms. Benign Neoplasm,Cancer,Malignant Neoplasm,Tumor,Tumors,Benign Neoplasms,Malignancy,Malignant Neoplasms,Neoplasia,Neoplasm,Neoplasms, Benign,Cancers,Malignancies,Neoplasias,Neoplasm, Benign,Neoplasm, Malignant,Neoplasms, Malignant
D010154 Pakistan A country located in southern Asia, bordering the Arabian Sea, between India on the east and Iran and Afghanistan on the west and China in the north. The capital is Islamabad. Islamic Republic of Pakistan
D003198 Computer Simulation Computer-based representation of physical systems and phenomena such as chemical processes. Computational Modeling,Computational Modelling,Computer Models,In silico Modeling,In silico Models,In silico Simulation,Models, Computer,Computerized Models,Computer Model,Computer Simulations,Computerized Model,In silico Model,Model, Computer,Model, Computerized,Model, In silico,Modeling, Computational,Modeling, In silico,Modelling, Computational,Simulation, Computer,Simulation, In silico,Simulations, Computer
D005260 Female Females
D006801 Humans Members of the species Homo sapiens. Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D000090005 Epidemiological Models Mathematical models of the transmission of infectious diseases. They predict spread of a disease by incorporating disease-related (e.g., infectious agent, mode of transmission, latent period, infectious period) and abiotic factors (e.g., social, cultural, demographic, and geographic factors). Communicable Disease Models,Compartmental Models,Models, Epidemiological,SIR Models,SIS Model,Susceptible Infected Recovered Models,Susceptible Infected Susceptible Model,Communicable Disease Model,Compartmental Model,Disease Model, Communicable,Epidemiological Model,Model, Communicable Disease,Model, Compartmental,Model, Epidemiological,Model, SIS,SIR Model,SIS Models
D001499 Bayes Theorem A theorem in probability theory named for Thomas Bayes (1702-1761). In epidemiology, it is used to obtain the probability of disease in a group of people with some characteristic on the basis of the overall rate of that disease and of the likelihood of that characteristic in healthy and diseased individuals. The most familiar application is in clinical decision analysis where it is used for estimating the probability of a particular diagnosis given the appearance of some symptoms or test result. Bayesian Analysis,Bayesian Estimation,Bayesian Forecast,Bayesian Method,Bayesian Prediction,Analysis, Bayesian,Bayesian Approach,Approach, Bayesian,Approachs, Bayesian,Bayesian Approachs,Estimation, Bayesian,Forecast, Bayesian,Method, Bayesian,Prediction, Bayesian,Theorem, Bayes
D015233 Models, Statistical Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc. Probabilistic Models,Statistical Models,Two-Parameter Models,Model, Statistical,Models, Binomial,Models, Polynomial,Statistical Model,Binomial Model,Binomial Models,Model, Binomial,Model, Polynomial,Model, Probabilistic,Model, Two-Parameter,Models, Probabilistic,Models, Two-Parameter,Polynomial Model,Polynomial Models,Probabilistic Model,Two Parameter Models,Two-Parameter Model
D015994 Incidence The number of new cases of a given disease during a given period in a specified population. It also is used for the rate at which new events occur in a defined population. It is differentiated from PREVALENCE, which refers to all cases in the population at a given time. Attack Rate,Cumulative Incidence,Incidence Proportion,Incidence Rate,Person-time Rate,Secondary Attack Rate,Attack Rate, Secondary,Attack Rates,Cumulative Incidences,Incidence Proportions,Incidence Rates,Incidence, Cumulative,Incidences,Person time Rate,Person-time Rates,Proportion, Incidence,Rate, Attack,Rate, Incidence,Rate, Person-time,Rate, Secondary Attack,Secondary Attack Rates

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