Nicolas Silva-Illanes1, Manuel Espinoza2. 1. School of Public Health, University of Chile, Santiago, Chile. Electronic address: nicosilva@uchile.cl. 2. HTA Unit, Centre for Clinical Research UC, Pontifical Catholic University of Chile, Santiago, Chile.
Abstract
BACKGROUND: The economic evaluation of colorectal cancer screening is challenging because of the need to model the underlying unobservable natural history of the disease. OBJECTIVES: To describe the available Markov models and to critically analyze their main structural assumptions. METHODS: A systematic search was performed in eight relevant databases (MEDLINE, Embase, Econlit, National Health Service Economic Evaluation Database, Health Economic Evaluations Database, Health Technology Assessment database, Cost-Effective Analysis Registry, and European Network of Health Economics Evaluation Databases), identifying 34 models that met the inclusion criteria. A comparative analysis of model structure and parameterization was conducted using two checklists and guidelines for cost-effectiveness screening models. RESULTS: Two modeling techniques were identified. One strategy used a Markov model to reproduce the natural history of the disease and an overlaying model that reproduced the screening process, whereas the other used a single model to represent a screening program. Most of the studies included only adenoma-carcinoma sequences, a few included de novo cancer, and none included the serrated pathway. Parameterization of adenoma dwell time, sojourn time, and surveillance differed between studies, and there was a lack of validation and statistical calibration against local epidemiological data. Most of the studies analyzed failed to perform an adequate literature review and synthesis of diagnostic accuracy properties of the screening tests modeled. CONCLUSIONS: Several strategies to model colorectal cancer screening have been developed, but many challenges remain to adequately represent the natural history of the disease and the screening process. Structural uncertainty analysis could be a useful strategy for understanding the impact of the assumptions of different models on cost-effectiveness results.
BACKGROUND: The economic evaluation of colorectal cancer screening is challenging because of the need to model the underlying unobservable natural history of the disease. OBJECTIVES: To describe the available Markov models and to critically analyze their main structural assumptions. METHODS: A systematic search was performed in eight relevant databases (MEDLINE, Embase, Econlit, National Health Service Economic Evaluation Database, Health Economic Evaluations Database, Health Technology Assessment database, Cost-Effective Analysis Registry, and European Network of Health Economics Evaluation Databases), identifying 34 models that met the inclusion criteria. A comparative analysis of model structure and parameterization was conducted using two checklists and guidelines for cost-effectiveness screening models. RESULTS: Two modeling techniques were identified. One strategy used a Markov model to reproduce the natural history of the disease and an overlaying model that reproduced the screening process, whereas the other used a single model to represent a screening program. Most of the studies included only adenoma-carcinoma sequences, a few included de novo cancer, and none included the serrated pathway. Parameterization of adenoma dwell time, sojourn time, and surveillance differed between studies, and there was a lack of validation and statistical calibration against local epidemiological data. Most of the studies analyzed failed to perform an adequate literature review and synthesis of diagnostic accuracy properties of the screening tests modeled. CONCLUSIONS: Several strategies to model colorectal cancer screening have been developed, but many challenges remain to adequately represent the natural history of the disease and the screening process. Structural uncertainty analysis could be a useful strategy for understanding the impact of the assumptions of different models on cost-effectiveness results.
Authors: Vivek Nair; Samuel Auger; Sara Kochanny; Frederick M Howard; Daniel Ginat; Olga Pasternak-Wise; Aditya Juloori; Matthew Koshy; Evgeny Izumchenko; Nishant Agrawal; Ari Rosenberg; Everett E Vokes; M Reza Skandari; Alexander T Pearson Journal: JAMA Netw Open Date: 2022-04-01