Publication
Computer Modeling of Diabetes and Its Transparency: A Report on the Eighth Mount Hood Challenge
Journal Paper/Review - Apr 9, 2018
Palmer Andrew J, Gahn James C, Valentine William J, Pollock Richard F, Breeze Penny, Brennan Alan, Pollard Daniel, Ye Wen, Herman William H, Isaman Deanna J, Kuo Shihchen, Laiteerapong Neda, Tran-Duy An, Smolen Harry J, Brändle Michael, Si Lei, Tew Michelle, Hua Xinyang, Willis Michael S, Asseburg Christian, McEwan Phil, Leal José, Gray Alastair, Foos Volker, Lamotte Mark, Feenstra Talitha, O'Connor Patrick J, Clarke Philip M
Units
PubMed
Doi
Citation
Type
Journal
Publication Date
Issn Electronic
Pages
Brief description/objective
OBJECTIVES
The Eighth Mount Hood Challenge (held in St. Gallen, Switzerland, in September 2016) evaluated the transparency of model input documentation from two published health economics studies and developed guidelines for improving transparency in the reporting of input data underlying model-based economic analyses in diabetes.
METHODS
Participating modeling groups were asked to reproduce the results of two published studies using the input data described in those articles. Gaps in input data were filled with assumptions reported by the modeling groups. Goodness of fit between the results reported in the target studies and the groups' replicated outputs was evaluated using the slope of linear regression line and the coefficient of determination (R). After a general discussion of the results, a diabetes-specific checklist for the transparency of model input was developed.
RESULTS
Seven groups participated in the transparency challenge. The reporting of key model input parameters in the two studies, including the baseline characteristics of simulated patients, treatment effect and treatment intensification threshold assumptions, treatment effect evolution, prediction of complications and costs data, was inadequately transparent (and often missing altogether). Not surprisingly, goodness of fit was better for the study that reported its input data with more transparency. To improve the transparency in diabetes modeling, the Diabetes Modeling Input Checklist listing the minimal input data required for reproducibility in most diabetes modeling applications was developed.
CONCLUSIONS
Transparency of diabetes model inputs is important to the reproducibility and credibility of simulation results. In the Eighth Mount Hood Challenge, the Diabetes Modeling Input Checklist was developed with the goal of improving the transparency of input data reporting and reproducibility of diabetes simulation model results.