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 2 ). 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.
This journal is a member of and subscribes to the principles of the Committee on Publication Ethics (COPE) (www.publicationethics.org/).Editorial contact: journals.library@nihr.ac.ukThe full HTA archive is freely available to view online at www.journalslibrary.nihr.ac.uk/hta. Print-on-demand copies can be purchased from the report pages of the NIHR Journals Library website: www.journalslibrary.nihr.ac.uk Criteria for inclusion in the Health Technology Assessment journalReports are published in Health Technology Assessment (HTA) if (1) they have resulted from work for the HTA programme, and (2) they are of a sufficiently high scientific quality as assessed by the reviewers and editors.Reviews in Health Technology Assessment are termed 'systematic' when the account of the search appraisal and synthesis methods (to minimise biases and random errors) would, in theory, permit the replication of the review by others. HTA programmeThe HTA programme, part of the National Institute for Health Research (NIHR), was set up in 1993. It produces high-quality research information on the effectiveness, costs and broader impact of health technologies for those who use, manage and provide care in the NHS. 'Health technologies' are broadly defined as all interventions used to promote health, prevent and treat disease, and improve rehabilitation and long-term care.The journal is indexed in NHS Evidence via its abstracts included in MEDLINE and its Technology Assessment Reports inform National Institute for Health and Care Excellence (NICE) guidance. HTA research is also an important source of evidence for National Screening Committee (NSC) policy decisions.For more information about the HTA programme please visit the website: http://www.nets.nihr.ac.uk/programmes/hta This reportThe research reported in this issue of the journal was funded by the HTA programme as project number 08/107/01. The contractual start date was in June 2011. The draft report began editorial review in March 2016 and was accepted for publication in July 2016. The authors have been wholly responsible for all data collection, analysis and interpretation, and for writing up their work. The HTA editors and publisher have tried to ensure the accuracy of the authors' report and would like to thank the reviewers for their constructive comments on the draft document. However, they do not accept liability for damages or losses arising from material published in this report.This report presents independent research funded by the National Institute for Health Research (NIHR). The views and opinions expressed by authors in this publication are those of the authors and do not necessarily reflect those of the NHS, the NIHR, NETSCC, the HTA programme or the Department of Health. If there are verbatim quotations included in this publication the views and opinions expressed by the interviewees are those of the interviewees and do not necessarily reflect those of the authors, those of the NHS, the NIHR, NETSCC, the HTA programme or the Department of Health. Published by the NIH...
The quality of evidence that psychological interventions are effective in improving glycemic control in adults with type 2 diabetes (T2D) is weak.We conducted a systematic review and meta-analysis of psychological interventions in T2D to assess whether their effectiveness in improving glycemic levels has improved over the past 30 years. We applied the protocol of a systematic review and aggregate meta-analysis conducted to January 2003. We added network meta-analysis (NMA) to compare intervention and control group type against usual care. MEDLINE, Cumulative Index to Nursing and Allied Health Literature, PsycINFO, EMBASE, Cochrane Controlled Trials Database, Web of Science, and Dissertation Abstracts International were searched from January 2003 to July 2018. Only randomized controlled trials (RCT) of psychological interventions for adults with T2D reported in any language were included. The primary outcome was change in glycemic control (glycated hemoglobin (HbA1c) in mmol/mol). Data were extracted from study reports and authors were contacted for missing data.94 RCTs were eligible for inclusion in the systematic review since the last review. In 70 RCTs (n=14 796 participants) the pooled mean difference in HbA1c in those randomized to psychological intervention compared with control group was −0.19 (95% CI −0.25 to −0.12), equivalent to a reduction in HbA1c of 3.7 mmol/mol, with moderate heterogeneity across studies (I2=64.7%, p<0.001). NMA suggested the probability of intervention effectiveness is highest for self-help materials, cognitive–behavioral therapy, and counseling, compared with usual care. Limitations of this study include that there is a possibility that some studies may have been missed if diabetes did not appear in the title or abstract.The effectiveness of psychological interventions for adults with T2D have minimal clinical benefit in improving glycemic control.PROSPERO registration numberCRD42016033619.
The cardiovascular outcomes challenge examined the predictive accuracy of 10 diabetes models in estimating hard outcomes in 2 recent cardiovascular outcomes trials (CVOTs) and whether recalibration can be used to improve replication. Methods: Participating groups were asked to reproduce the results of the Empagliflozin Cardiovascular Outcome Event Trial in Type 2 Diabetes Mellitus Patients (EMPA-REG OUTCOME) and the Canagliflozin Cardiovascular Assessment Study (CANVAS) Program. Calibration was performed and additional analyses assessed model ability to replicate absolute event rates, hazard ratios (HRs), and the generalizability of calibration across CVOTs within a drug class. Results: Ten groups submitted results. Models underestimated treatment effects (ie, HRs) using uncalibrated models for both trials. Calibration to the placebo arm of EMPA-REG OUTCOME greatly improved the prediction of event rates in the placebo, but less so in the active comparator arm. Calibrating to both arms of EMPA-REG OUTCOME individually enabled replication of the observed outcomes. Using EMPA-REG OUTCOME-calibrated models to predict CANVAS Program outcomes was an improvement over uncalibrated models but failed to capture treatment effects adequately. Applying canagliflozin HRs directly provided the best fit. Conclusions: The Ninth Mount Hood Diabetes Challenge demonstrated that commonly used risk equations were generally unable to capture recent CVOT treatment effects but that calibration of the risk equations can improve predictive accuracy. Although calibration serves as a practical approach to improve predictive accuracy for CVOT outcomes, it does not extrapolate generally to other settings, time horizons, and comparators. New methods and/or new risk equations for capturing these CV benefits are needed.
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