Survivability Prognosis for Lung Cancer Patients at Different Severity Stages by a Risk FactorBased Bayesian Network Modeling Lung cancer is a major reason of mortalities. Estimating the survivability for this disease has become a key issue to families, hospitals, and countries. A conditional Gaussian Bayesian network model was presented in this study. This model considered 15 risk factors to predict the survivability of a lung cancer patient at 4 severity stages. We surveyed 1075 patients. The presented model is constructed by using the demographic, diagnosedbased, and priorutilization variables. The proposed model for the survivability prognosis at different four stages performed R2 of 93.57%, 86.83%, 67.22%, and 52.94%, respectively. The model predicted the lung cancer survivability with high accuracy compared with the reported models. Our model also shows that it reached the ceiling of an ideal Bayesian network.

Bayesian and Frequentist Analytical Approaches Using LogNormal and Gamma Frailty Parametric Models for Breast Cancer Mortality 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 populacexe2x80x94the 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 facilitybased crosssectional data collected over a 9year 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 lognormal and gamma frailties) statistics were conducted. The deviance information criterion (DIC), Watanabexe2x80x93Akaike 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: 1.03xe2x80x931.80) while widowed had a hazard ratio of 1.57 (95% CI: 1.14xe2x80x932.18). 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) (HRxe2x80x89xe2x80x897.11, 95% CI: 3.32xe2x80x9315.23), Grade II (Mod diff) (HRxe2x80x89xe2x80x897.89, 95% CI: 3.88xe2x80x9316.06), Grade III (Poor diff) (HRxe2x80x89xe2x80x895.90, 95% CI (2.91xe2x80x9311.96), and Grade IV (Undiff) (HRxe2x80x89xe2x80x895.44, 95% (2.48xe2x80x9311.9), relative to Bcell. 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.