Full Citation
Title: Nonidentifiability in Model Calibration and Implications for Medical Decision Making
Citation Type: Journal Article
Publication Year: 2018
ISBN:
ISSN: 0272-989X
DOI: 10.1177/0272989X18792283
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PMCID:
PMID:
Abstract: Background. Calibration is the process of estimating parameters of a mathematical model by matching model outputs to calibration targets. In the presence of nonidentifiability, multiple parameter sets solve the calibration problem, which may have important implications for decision making. We evaluate the implications of nonidentifiability on the optimal strategy and provide methods to check for nonidentifiability. Methods. We illustrate nonidentifiability by calibrating a 3-state Markov model of cancer relative survival (RS). We performed 2 different calibration exercises: 1) only including RS as a calibration target and 2) adding the ratio between the 2 nondeath states over time as an additional target. We used the Nelder-Mead (NM) algorithm to identify parameter sets that best matched the calibration targets. We used collinearity and likelihood profile analyses to check for nonidentifiability. We then estimated the benefit of a hypothetical treatment in terms of life expectancy gains using different, b...
Url: http://journals.sagepub.com/doi/10.1177/0272989X18792283
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Authors: Alarid-Escudero, Fernando; MacLehose, Richard F; Peralta, Yadira; Kuntz, Karen M; Enns, Eva A
Periodical (Full): Medical Decision Making
Issue: 7
Volume: 38
Pages: 810-821
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