title: Adjusting a cancer mortality-prediction model for disease status-related eligibility criteria.
abstract: BACKGROUND
  Volunteering participants in disease studies tend to be healthier than the general population partially due to specific enrollment criteria. Using modeling to accurately predict outcomes of cohort studies enrolling volunteers requires adjusting for the bias introduced in this way. Here we propose a new method to account for the effect of a specific form of healthy volunteer bias resulting from imposing disease status-related eligibility criteria, on disease-specific mortality, by explicitly modeling the length of the time interval between the moment when the subject becomes ineligible for the study, and the outcome.
  
  
  METHODS
  Using survival time data from 1190 newly diagnosed lung cancer patients at MD Anderson Cancer Center, we model the time from clinical lung cancer diagnosis to death using an exponential distribution to approximate the length of this interval for a study where lung cancer death serves as the outcome. Incorporating this interval into our previously developed lung cancer risk model, we adjust for the effect of disease status-related eligibility criteria in predicting the number of lung cancer deaths in the control arm of CARET. The effect of the adjustment using the MD Anderson-derived approximation is compared to that based on SEER data.
  
  
  RESULTS
  Using the adjustment developed in conjunction with our existing lung cancer model, we are able to accurately predict the number of lung cancer deaths observed in the control arm of CARET.
  
  
  CONCLUSIONS
  The resulting adjustment was accurate in predicting the lower rates of disease observed in the early years while still maintaining reasonable prediction ability in the later years of the trial. This method could be used to adjust for, or predict the duration and relative effect of any possible biases related to disease-specific eligibility criteria in modeling studies of volunteer-based cohorts.
authors.affiliation: Brown Foundation Institute of Molecular Medicine, University of Texas Health Science Center at Houston, 1825 Pressler St, Houston, TX 77030, USA. millennia.foy@uth.tmc.edu
authors.forename: Millennia
authors.initials: M
authors.lastname: Foy
authors.forename: Xing
authors.initials: X
authors.lastname: Chen
authors.forename: Marek
authors.initials: M
authors.lastname: Kimmel
authors.forename: Olga Y
authors.initials: OY
authors.lastname: Gorlova
mesh_terms: D000328:Adult; D000368:Aged; D002338:Carotenoids / Q000627:therapeutic use; D015331:Cohort Studies; D004608:Eligibility Determination; D005544:Forecasting; D006801:Humans; D053208:Kaplan-Meier Estimate; D008175:Lung Neoplasms / Q000175:diagnosis / Q000188:drug therapy / Q000401:mortality*; D008875:Middle Aged; D011379:Prognosis; D012107:Research Design; D015983:Selection Bias; D012907:Smoking; D016896:Treatment Outcome; D014801:Vitamin A / Q000627:therapeutic use
chemical_list: D014801:Vitamin A; D002338:Carotenoids