title: A method for using real world data in breast cancer modeling.
abstract: OBJECTIVE
  Today, hospitals and other health care-related institutions are accumulating a growing bulk of real world clinical data. Such data offer new possibilities for the generation of disease models for the health economic evaluation. In this article, we propose a new approach to leverage cancer registry data for the development of Markov models. Records of breast cancer patients from a clinical cancer registry were used to construct a real world data driven disease model.
  
  
  METHODS
  We describe a model generation process which maps database structures to disease state definitions based on medical expert knowledge. Software was programmed in Java to automatically derive a model structure and transition probabilities. We illustrate our method with the reconstruction of a published breast cancer reference model derived primarily from clinical study data. In doing so, we exported longitudinal patient data from a clinical cancer registry covering eight years. The patient cohort (n=892) comprised HER2-positive and HER2-negative women treated with or without Trastuzumab.
  
  
  RESULTS
  The models generated with this method for the respective patient cohorts were comparable to the reference model in their structure and treatment effects. However, our computed disease models reflect a more detailed picture of the transition probabilities, especially for disease free survival and recurrence.
  
  
  CONCLUSIONS
  Our work presents an approach to extract Markov models semi-automatically using real world data from a clinical cancer registry. Health care decision makers may benefit from more realistic disease models to improve health care-related planning and actions based on their own data.
authors: Heilbronn University, GECKO Institute for Medicine, Informatics and Economics, Max-Planck-Str. 39, 74081 Heilbronn, Germany. Electronic address: monika.pobiruchin@hs-heilbronn.de.
authors: Monika
authors: M
authors: Pobiruchin
authors: SLK-Hospitals, Cancer Center Heilbronn-Franken, Am Gesundbrunnen 20-26, 74078 Heilbronn, Germany. Electronic address: sylvia.bochum@slk-kliniken.de.
authors: Sylvia
authors: S
authors: Bochum
authors: SLK-Hospitals, Cancer Center Heilbronn-Franken, Am Gesundbrunnen 20-26, 74078 Heilbronn, Germany. Electronic address: uwe.martens@slk-kliniken.de.
authors: Uwe M
authors: UM
authors: Martens
authors: University of Heidelberg, Institute of Medical Biometry and Informatics, Im Neuenheimer Feld 305, 69120 Heidelberg, Germany. Electronic address: meinhard.kieser@imbi.uni-heidelberg.de.
authors: Meinhard
authors: M
authors: Kieser
authors: Heilbronn University, GECKO Institute for Medicine, Informatics and Economics, Max-Planck-Str. 39, 74081 Heilbronn, Germany. Electronic address: wendelin.schramm@hs-heilbronn.de.
authors: Wendelin
authors: W
authors: Schramm
mesh_terms: D000465:Algorithms; D000970:Antineoplastic Agents / Q000627:therapeutic use; D001943:Breast Neoplasms / Q000188:drug therapy* / Q000473:pathology; D015331:Cohort Studies; D003362:Cost-Benefit Analysis; D003625:Data Collection; D016208:Databases, Factual; D003657:Decision Making; D004470:Economics, Medical; D005260:Female; D006801:Humans; D008390:Markov Chains; D008490:Medical Informatics / Q000379:methods*; D015233:Models, Statistical; D009362:Neoplasm Metastasis; D009364:Neoplasm Recurrence, Local; D011336:Probability; D012042:Registries; D000068878:Trastuzumab / Q000627:therapeutic use
chemical_list: D000970:Antineoplastic Agents; D000068878:Trastuzumab
keywords: Cancer registry; Disease model; Markov model; Real world data; Secondary use