Paal Joranger1, Arild Nesbakken2, Geir Hoff3, Halfdan Sorbye4, Arne Oshaug5, Eline Aas6. 1. Norwegian University of Life Science, Ås, Norway/Oslo and Akershus University College of Applied Sciences, Oslo, Norway (PJ) 2. Oslo University Hospital, Oslo, Norway/K.G. Jebsen Colorectal Cancer Research Centre, Oslo, Norway/University of Oslo, Oslo, Norway (AN) 3. Cancer Registry of Norway/University of Oslo/Telemark Hospital, Skien, Norway (GH) 4. Haukeland University Hospital, Bergen, Norway (HS) 5. Oslo and Akershus University College of Applied Sciences, Oslo, Norway (AO) 6. University of Oslo, Oslo, Norway (EA).
Abstract
BACKGROUND: Cancer is a major cause of morbidity and mortality, and colorectal cancer (CRC) is the third most common cancer in the world. The estimated costs of CRC treatment vary considerably, and if CRC costs in a model are based on empirically estimated total costs of stage I, II, III, or IV treatments, then they lack some flexibility to capture future changes in CRC treatment. The purpose was 1) to describe how to model CRC costs and survival and 2) to validate the model in a transparent and reproducible way. METHODS: We applied a semi-Markov model with 70 health states and tracked age and time since specific health states (using tunnels and 3-dimensional data matrix). The model parameters are based on an observational study at Oslo University Hospital (2049 CRC patients), the National Patient Register, literature, and expert opinion. The target population was patients diagnosed with CRC. The model followed the patients diagnosed with CRC from the age of 70 until death or 100 years. The study focused on the perspective of health care payers. RESULTS: The model was validated for face validity, internal and external validity, and cross-validity. The validation showed a satisfactory match with other models and empirical estimates for both cost and survival time, without any preceding calibration of the model. CONCLUSIONS: The model can be used to 1) address a range of CRC-related themes (general model) like survival and evaluation of the cost of treatment and prevention measures; 2) make predictions from intermediate to final outcomes; 3) estimate changes in resource use and costs due to changing guidelines; and 4) adjust for future changes in treatment and trends over time. The model is adaptable to other populations.
BACKGROUND:Cancer is a major cause of morbidity and mortality, and colorectal cancer (CRC) is the third most common cancer in the world. The estimated costs of CRC treatment vary considerably, and if CRC costs in a model are based on empirically estimated total costs of stage I, II, III, or IV treatments, then they lack some flexibility to capture future changes in CRC treatment. The purpose was 1) to describe how to model CRC costs and survival and 2) to validate the model in a transparent and reproducible way. METHODS: We applied a semi-Markov model with 70 health states and tracked age and time since specific health states (using tunnels and 3-dimensional data matrix). The model parameters are based on an observational study at Oslo University Hospital (2049 CRC patients), the National Patient Register, literature, and expert opinion. The target population was patients diagnosed with CRC. The model followed the patients diagnosed with CRC from the age of 70 until death or 100 years. The study focused on the perspective of health care payers. RESULTS: The model was validated for face validity, internal and external validity, and cross-validity. The validation showed a satisfactory match with other models and empirical estimates for both cost and survival time, without any preceding calibration of the model. CONCLUSIONS: The model can be used to 1) address a range of CRC-related themes (general model) like survival and evaluation of the cost of treatment and prevention measures; 2) make predictions from intermediate to final outcomes; 3) estimate changes in resource use and costs due to changing guidelines; and 4) adjust for future changes in treatment and trends over time. The model is adaptable to other populations.
Authors: Ankur Pandya; Stephen Sy; Sylvia Cho; Sartaj Alam; Milton C Weinstein; Thomas A Gaziano Journal: Med Decis Making Date: 2017-05-10 Impact factor: 2.583
Authors: Nasuh C Büyükkaramikli; Maureen P M H Rutten-van Mölken; Johan L Severens; Maiwenn Al Journal: Pharmacoeconomics Date: 2019-11 Impact factor: 4.981