Dataset Information

Computer Modeling of Diabetes and Its Transparency: A Report on the Eighth Mount Hood Challenge.

ABSTRACT: 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 (R2). 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.

SUBMITTER: Palmer AJ

PROVIDER: S-EPMC6659402 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Computer Modeling of Diabetes and Its Transparency: A Report on the Eighth Mount Hood Challenge.

Palmer Andrew J AJ Si Lei L Tew Michelle M Hua Xinyang X Willis Michael S MS Asseburg Christian C McEwan Phil P Leal José J Gray Alastair A Foos Volker V Lamotte Mark M Feenstra Talitha T O'Connor Patrick J PJ Brandle Michael M Smolen Harry J HJ Gahn James C JC Valentine William J WJ Pollock Richard F RF Breeze Penny P Brennan Alan A Pollard Daniel D Ye Wen W Herman William H WH Isaman Deanna J DJ Kuo Shihchen S Laiteerapong Neda N Tran-Duy An A Clarke Philip M PM

Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research 20180409 6

<h4>Objectives</h4>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.<h4>Methods</h4>Participating modeling groups were asked to reproduce the results of two published studies using the input data described in those articles. Gap ...[more]

PMID: 29909878

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Project description:BackgroundStructural uncertainty can affect model-based economic simulation estimates and study conclusions. Unfortunately, unlike parameter uncertainty, relatively little is known about its magnitude of impact on life-years (LYs) and quality-adjusted life-years (QALYs) in modeling of diabetes. We leveraged the Mount Hood Diabetes Challenge Network, a biennial conference attended by international diabetes modeling groups, to assess structural uncertainty in simulating QALYs in type 2 diabetes simulation models.MethodsEleven type 2 diabetes simulation modeling groups participated in the 9th Mount Hood Diabetes Challenge. Modeling groups simulated 5 diabetes-related intervention profiles using predefined baseline characteristics and a standard utility value set for diabetes-related complications. LYs and QALYs were reported. Simulations were repeated using lower and upper limits of the 95% confidence intervals of utility inputs. Changes in LYs and QALYs from tested interventions were compared across models. Additional analyses were conducted postchallenge to investigate drivers of cross-model differences.ResultsSubstantial cross-model variability in incremental LYs and QALYs was observed, particularly for HbA1c and body mass index (BMI) intervention profiles. For a 0.5%-point permanent HbA1c reduction, LY gains ranged from 0.050 to 0.750. For a 1-unit permanent BMI reduction, incremental QALYs varied from a small decrease in QALYs (-0.024) to an increase of 0.203. Changes in utility values of health states had a much smaller impact (to the hundredth of a decimal place) on incremental QALYs. Microsimulation models were found to generate a mean of 3.41 more LYs than cohort simulation models (P = 0.049).ConclusionsVariations in utility values contribute to a lesser extent than uncertainty captured as structural uncertainty. These findings reinforce the importance of assessing structural uncertainty thoroughly because the choice of model (or models) can influence study results, which can serve as evidence for resource allocation decisions.HighlightsThe findings indicate substantial cross-model variability in QALY predictions for a standardized set of simulation scenarios and is considerably larger than within model variability to alternative health state utility values (e.g., lower and upper limits of the 95% confidence intervals of utility inputs).There is a need to understand and assess structural uncertainty, as the choice of model to inform resource allocation decisions can matter more than the choice of health state utility values.

Project description:BackgroundPublic reporting of provider performance can assist consumers in their choice of providers and stimulate providers to improve quality. Reporting of quality measures is supported by advocates of health care reform across the political spectrum.ObjectiveTo assess the availability, credibility and applicability of existing public reports of hospital and physician quality, with comparisons across geographic areas.ApproachInformation pertaining to 263 public reports in 21 geographic areas was collected through reviews of websites and telephone and in-person interviews, and used to construct indicators of public reporting status. Interview data collected in 14 of these areas were used to assess recent changes in reporting and their implications.ParticipantsInterviewees included staff of state and local associations, health plan representatives and leaders of local health care alliances.ResultsThere were more reports of hospital performance (161) than of physician performance (103) in the study areas. More reports included measures derived from claims data (mean, 7.2 hospital reports and 3.3 physician reports per area) than from medical records data. Typically, reports on physician performance contained measures of chronic illness treatment constructed at the medical group level, with diabetes measures the most common (mean number per non-health plan report, 2.3). Patient experience measures were available in more hospital reports (mean number of reports, 1.2) than physician reports (mean, 0.7). Despite the availability of national hospital reports and reports sponsored by national health plans, from a consumer standpoint the status of public reporting depended greatly on where one lived and health plan membership.ConclusionsCurrent public reports, and especially reports of physician quality of care, have significant limitations from both consumer and provider perspectives. The present approach to reporting is being challenged by the development of new information sources for consumers, and consumer and provider demands for more current information.

Project description:Healthy aging is associated with deficits in performance on episodic memory tasks. Popular verbal theories of the mechanisms underlying this decrement have primarily focused on inferred changes in associative memory. However, performance on any task is the result of interactions between different neurocognitive mechanisms, such as perceptuomotor, memory, and decision-making processes. As a result, age-related differences in performance could arise from multiple processes, which could lead to incomplete or incorrect conclusions about the sources of aging effects. In addition, standard statistical comparisons of group-level summary statistics, such as mean accuracy, may not provide sufficient information to allow detailed mechanistic explanations of age-related change. We argue that these and other drawbacks of relying exclusively on verbal theories can hamper replicability, transparency, and scientific progress in aging research and psychological science more generally, and that computational modeling is a tool that can address many of these limitations. Computational models make mathematically transparent claims about how latent processes give rise to observed behavior and decompose an individual's performance into model parameters governing hypothesized mechanisms. In this work, we present a short memory task designed for and analyzed with mechanistic model-based approaches. We provide an example of a computational model and fit the model to data from young and older adults with hierarchical Bayesian techniques in order to (a) detect differences in latent cognitive processes between young and older adults (as well as individual participants), (b) quantitatively compare models to assess different processes that could underlie performance, and (c) simulate data to make predictions for future experiments based on model mechanisms. We argue that computational modeling is a powerful tool to examine age differences in latent processes, make theories more transparent, and facilitate discovery in cognitive aging research. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Dataset Information

Computer Modeling of Diabetes and Its Transparency: A Report on the Eighth Mount Hood Challenge.

Publications

Computer Modeling of Diabetes and Its Transparency: A Report on the Eighth Mount Hood Challenge.

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