Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia.
Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia.
Value Health. 2018 Jun;21(6):724-731. doi: 10.1016/j.jval.2018.02.002. Epub 2018 Apr 9.
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.
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.
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.
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.
第八届胡德山挑战赛（2016 年 9 月在瑞士圣加仑举行）评估了两项已发表的健康经济学研究的模型输入文件的透明度，并制定了改善基于模型的经济学分析中输入数据报告透明度的指南，该分析以糖尿病为研究对象。
要求参与建模的小组使用文章中描述的输入数据重现两项已发表研究的结果。通过建模小组报告的假设来填补输入数据中的空白。使用线性回归线的斜率和确定系数（R）评估目标研究中报告的结果与小组复制结果之间的拟合优度。在对结果进行一般性讨论之后，制定了一个用于糖尿病模型输入透明度的检查表。
有 7 个小组参加了透明度挑战赛。两项研究中关键模型输入参数的报告，包括模拟患者的基线特征、治疗效果和治疗强化阈值假设、治疗效果演变、并发症预测和成本数据，不够透明（而且经常完全缺失）。毫不奇怪，报告输入数据更透明的研究的拟合优度更好。为了提高糖尿病建模的透明度，制定了糖尿病建模输入检查表，列出了大多数糖尿病建模应用程序中重现所需的最小输入数据。
糖尿病模型输入的透明度对于模拟结果的可重复性和可信度很重要。在第八届胡德山挑战赛中，制定了糖尿病建模输入检查表，旨在提高输入数据报告的透明度和糖尿病模拟模型结果的可重现性。
