title: Does Breast Cancer Drive the Building of Survival Probability Models among States? An Assessment of Goodness of Fit for Patient Data from SEER Registries
abstract: Background: Breast cancer is a worldwide public health concern and is the most prevalent type of cancer in women
  in the United States. This study concerned the best fit of statistical probability models on the basis of survival times
  for nine state cancer registries: California, Connecticut, Georgia, Hawaii, Iowa, Michigan, New Mexico, Utah, and
  Washington. Materials and Methods: A probability random sampling method was applied to select and extract records
  of 2,000 breast cancer patients from the Surveillance Epidemiology and End Results (SEER) database for each of the
  nine state cancer registries used in this study. EasyFit software was utilized to identify the best probability models by
  using goodness of fit tests, and to estimate parameters for various statistical probability distributions that fit survival
  data. Results: Statistical analysis for the summary of statistics is reported for each of the states for the years 1973 to
  2012. Kolmogorov-Smirnov, Anderson-Darling, and Chi-squared goodness of fit test values were used for survival
  data, the highest values of goodness of fit statistics being considered indicative of the best fit survival model for each
  state. Conclusions: It was found that California, Connecticut, Georgia, Iowa, New Mexico, and Washington followed
  the Burr probability distribution, while the Dagum probability distribution gave the best fit for Michigan and Utah,
  and Hawaii followed the Gamma probability distribution. These findings highlight differences between states through
  selected sociodemographic variables and also demonstrate probability modeling differences in breast cancer survival
  times. The results of this study can be used to guide healthcare providers and researchers for further investigations into
  social and environmental factors in order to reduce the occurrence of and mortality due to breast cancer.
authors.affiliation: Texas Tech University Health Sciences Center, United States. Email: hafiz.khan@ttuhsc.edu
authors.forename: Hafiz
authors.initials: H
authors.lastname: Khan
authors.forename: Anshul
authors.initials: A
authors.lastname: Saxena
authors.forename: Abhilash
authors.initials: A
authors.lastname: Perisetti
authors.forename: Aamrin
authors.initials: A
authors.lastname: Rafiq
authors.forename: Kemesha
authors.initials: K
authors.lastname: Gabbidon
authors.forename: Sarah
authors.initials: S
authors.lastname: Mende
authors.forename: Maria
authors.initials: M
authors.lastname: Lyuksyutova
authors.forename: Kandi
authors.initials: K
authors.lastname: Quesada
authors.forename: Summre
authors.initials: S
authors.lastname: Blakely
authors.forename: Tiffany
authors.initials: T
authors.lastname: Torres
authors.forename: Mahlet
authors.initials: M
authors.lastname: Afesse
keywords: Breast cancer data; survival; probability models; goodness of fit tests