Monika Pobiruchin1, Sylvia Bochum2, Uwe M Martens3, Meinhard Kieser4, Wendelin Schramm5. 1. Heilbronn University, GECKO Institute for Medicine, Informatics and Economics, Max-Planck-Str. 39, 74081 Heilbronn, Germany. Electronic address: monika.pobiruchin@hs-heilbronn.de. 2. SLK-Hospitals, Cancer Center Heilbronn-Franken, Am Gesundbrunnen 20-26, 74078 Heilbronn, Germany. Electronic address: sylvia.bochum@slk-kliniken.de. 3. SLK-Hospitals, Cancer Center Heilbronn-Franken, Am Gesundbrunnen 20-26, 74078 Heilbronn, Germany. Electronic address: uwe.martens@slk-kliniken.de. 4. University of Heidelberg, Institute of Medical Biometry and Informatics, Im Neuenheimer Feld 305, 69120 Heidelberg, Germany. Electronic address: meinhard.kieser@imbi.uni-heidelberg.de. 5. Heilbronn University, GECKO Institute for Medicine, Informatics and Economics, Max-Planck-Str. 39, 74081 Heilbronn, Germany. Electronic address: wendelin.schramm@hs-heilbronn.de.
Abstract
OBJECTIVES: 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.
OBJECTIVES: 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 cancerpatients 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: Yan Cheng Yang; Saad Ul Islam; Asra Noor; Sadia Khan; Waseem Afsar; Shah Nazir Journal: Comput Math Methods Med Date: 2021-09-06 Impact factor: 2.238