Nonidentifiability in Model Calibration and Implications for Medical Decision Making

Fernando Alarid-Escudero, Richard F. MacLehose, Yadira Peralta, Karen M. Kuntz and Eva A. Enns
Additional contact information
Fernando Alarid-Escudero: Division of Health Policy and Management, University of Minnesota School of Public Health, Minneapolis, MN
Richard F. MacLehose: Division of Epidemiology and Community Health, University of Minnesota School of Public Health, Minneapolis, MN
Yadira Peralta: Department of Educational Psychology, University of Minnesota, Minneapolis, MN
Karen M. Kuntz: Division of Health Policy and Management, University of Minnesota School of Public Health, Minneapolis, MN
Eva A. Enns: Division of Health Policy and Management, University of Minnesota School of Public Health, Minneapolis, MN

Medical Decision Making, 2018, vol. 38, issue 7, 810-821

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, but equally good-fitting, parameter sets. We also applied collinearity analysis to a realistic model of the natural history of colorectal cancer. Results. When only RS is used as the calibration target, 2 different parameter sets yield similar maximum likelihood values. The high collinearity index and the bimodal likelihood profile on both parameters demonstrated the presence of nonidentifiability. These different, equally good-fitting parameter sets produce different estimates of the treatment effectiveness (0.67 v. 0.31 years), which could influence the optimal decision. By incorporating the additional target, the model becomes identifiable with a collinearity index of 3.5 and a unimodal likelihood profile. Conclusions. In the presence of nonidentifiability, equally likely parameter estimates might yield different conclusions. Checking for the existence of nonidentifiability and its implications should be incorporated into standard model calibration procedures.

Keywords: calibration; decision-analytic models; estimation; likelihood function; nonidentifiability (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/0272989X18792283 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:38:y:2018:i:7:p:810-821

DOI: 10.1177/0272989X18792283

Access Statistics for this article

More articles in Medical Decision Making
Bibliographic data for series maintained by SAGE Publications ().