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

Farzana Noor1, Saadia Masood2, Yumna Sabar1

  • 1Department of Mathematics &Statistics, International Islamic University, Islamabad, Pakistan.

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|October 22, 2021
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Summary
This summary is machine-generated.

Cancer poses a significant public health challenge in Pakistan, with nearly 148,000 new cases and 100,000 deaths annually. A novel Bayesian mixture model was used to analyze cancer incidence and mortality data, identifying optimal estimation methods for different populations.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health

Background:

  • Cancer is a major health burden in Pakistan, with approximately 148,000 new diagnoses and 100,000 deaths each year.
  • Common cancers include lung, breast, liver, cervical, blood/bone marrow, and oral cancers, influenced by factors like smoking, inactivity, infections, toxins, and diet.

Purpose of the Study:

  • To estimate the average number of cancer incidences and deaths in Pakistan across different age groups and genders.
  • To apply a novel four-component mixture model using Bayesian estimation for analyzing cancer data.

Main Methods:

  • Utilized GLOBOCAN data from 2012 for registered cancer patients in Pakistan.
  • Employed a four-component exponential mixture model under Bayesian analysis, considering 28 types of cancer.
  • Derived estimators using three different priors and two loss functions (LINEX and SELF), including a simulation study.

Main Results:

  • Bayes estimates under LINEX loss with Jeffreys' prior were found to be most efficient for estimating cancer incidence in both males and females.
  • For cancer mortality, LINEX loss with Jeffreys' prior provided better results for males.
  • For female cancer mortality, the SELF loss function with Jeffreys' prior yielded the best estimates.

Conclusions:

  • The study successfully applied a Bayesian mixture model to analyze complex cancer data in Pakistan.
  • Identified optimal Bayesian estimation procedures (loss functions and priors) for cancer incidence and mortality in different demographic groups.
  • Findings can inform public health strategies and statistical modeling for cancer control in Pakistan.