title: Discrete mixture modeling to address genetic heterogeneity in time-to-event regression.
abstract: BACKGROUND
  Time-to-event regression models are a critical tool for associating survival time outcomes with molecular data. Despite mounting evidence that genetic subgroups of the same clinical disease exist, little attention has been given to exploring how this heterogeneity affects time-to-event model building and how to accommodate it. Methods able to diagnose and model heterogeneity should be valuable additions to the biomarker discovery toolset.
  
  
  RESULTS
  We propose a mixture of survival functions that classifies subjects with similar relationships to a time-to-event response. This model incorporates multivariate regression and model selection and can be fit with an expectation maximization algorithm, we call Cox-assisted clustering. We illustrate a likely manifestation of genetic heterogeneity and demonstrate how it may affect survival models with little warning. An application to gene expression in ovarian cancer DNA repair pathways illustrates how the model may be used to learn new genetic subsets for risk stratification. We explore the implications of this model for censored observations and the effect on genomic predictors and diagnostic analysis.
  
  
  METHODS
  R implementation of CAC using standard packages is available at https://gist.github.com/programeng/8620b85146b14b6edf8f Data used in the analysis are publicly available.
authors.affiliation: Department of Biostatistics and Bioinformatics, Roswell Park Cancer Institute, Elm and Carlton Streets, Buffalo, NY 14263, USA and Department of Statistics, University of Wisconsin-Madison, 1300 University Avenue, Madison, WI 53705, USA.
authors.forename: Kevin H
authors.initials: KH
authors.lastname: Eng
authors.affiliation: Department of Biostatistics and Bioinformatics, Roswell Park Cancer Institute, Elm and Carlton Streets, Buffalo, NY 14263, USA and Department of Statistics, University of Wisconsin-Madison, 1300 University Avenue, Madison, WI 53705, USA.
authors.forename: Bret M
authors.initials: BM
authors.lastname: Hanlon
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D004260:DNA Repair; D004815:Epidermal Growth Factor; D066260:Epiregulin; D005260:Female; D015870:Gene Expression; D018740:Genetic Heterogeneity; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D010051:Ovarian Neoplasms / Q000235:genetics / Q000378:metabolism / Q000401:mortality; D012044:Regression Analysis; D016019:Survival Analysis
chemical_list: C587880:EREG protein, human; D066260:Epiregulin; D004815:Epidermal Growth Factor