Development and Validation of PREDICT-DM: A New Microsimulation Model to Project and Evaluate Complications and Treatments of Type 2 Diabetes Mellitus

Kazemian, Pooyan; Wexler, Deborah J.; Fields, Naomi F; Zheng, Amy; Walensky, Rochelle P.

doi:10.1089/dia.2018.0393

Cited by 12 publications

(11 citation statements)

References 66 publications

(71 reference statements)

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“…All qualitative studies, literature reviews, abstracts only, non-English written papers and methodological papers were excluded. For instance, we excluded Kazemian et al’s study [ 13 ] titled “development and validation of PREDICT-DM: a new microsimulation model to project and evaluate complications and treatment of type 2 diabetes mellitus” because the paper focused on the modelling process, i.e., the design, development, and validation of the model for the United States population.…”

Section: Methodsmentioning

confidence: 99%

Application of decision analytical models to diabetes in low- and middle-income countries: a systematic review

Tagoe

Nonvignon

Robert

et al. 2022

BMC Health Serv Res

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Background Decision analytical models (DAMs) are used to develop an evidence base for impact and health economic evaluations, including evaluating interventions to improve diabetes care and health services—an increasingly important area in low- and middle-income countries (LMICs), where the disease burden is high, health systems are weak, and resources are constrained. This study examines how DAMs–in particular, Markov, system dynamic, agent-based, discrete event simulation, and hybrid models–have been applied to investigate non-pharmacological population-based (NP) interventions and how to advance their adoption in diabetes research in LMICs. Methods We systematically searched peer-reviewed articles published in English from inception to 8th August 2022 in PubMed, Cochrane, and the reference list of reviewed articles. Articles were summarised and appraised based on publication details, model design and processes, modelled interventions, and model limitations using the Health Economic Evaluation Reporting Standards (CHEERs) checklist. Results Twenty-three articles were fully screened, and 17 met the inclusion criteria of this qualitative review. The majority of the included studies were Markov cohort (7, 41%) and microsimulation models (7, 41%) simulating non-pharmacological population-based diabetes interventions among Asian sub-populations (9, 53%). Eleven (65%) of the reviewed studies evaluated the cost-effectiveness of interventions, reporting the evaluation perspective and the time horizon used to track cost and effect. Few studies (6,35%) reported how they validated models against local data. Conclusions Although DAMs have been increasingly applied in LMICs to evaluate interventions to control diabetes, there is a need to advance the use of DAMs to evaluate NP diabetes policy interventions in LMICs, particularly DAMs that use local research data. Moreover, the reporting of input data, calibration and validation that underlies DAMs of diabetes in LMICs needs to be more transparent and credible.

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Section: Methodsmentioning

confidence: 99%

Application of decision analytical models to diabetes in low- and middle-income countries: a systematic review

Tagoe

Nonvignon

Robert

et al. 2022

BMC Health Serv Res

View full text Add to dashboard Cite

show abstract

“…All qualitative studies, literature reviews, abstract only, non-English written papers and methodological papers were excluded. For instance, we excluded Kazemian et al's study (25) titled "development and validation of PREDICT-DM: a new microsimulation model to project and evaluate complications and treatment of type 2 diabetes mellitus" because the paper focused on the modelling process, i.e., the design, development and validation of the model for the US population.…”

Section: Exclusion Criteriamentioning

confidence: 99%

“…E.g., Verburg and Overmars[25] Enrichment Elements from two or more modelling approaches are combined and to form a new model. Interaction between the two models occurs at a xed time step.E.g., Chaim and Streit [26]IntegrationInterfacedTwo or more modelling approaches from different paradigms are combined while relaxing paradigm restrictions.…”

mentioning

confidence: 99%

Application of decision analytical models to diabetes in low- and middle-income countries: A systematic review

Tagoe

Nonvignon

Meer

et al. 2022

Preprint

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Background: Decision analytical models (DAMs) are used to develop an evidence base that is used in impact and health economic evaluations, including to evaluate interventions to improve diabetes care and health service—an increasingly important area in low- and middle-income countries (LMICs), where the disease burden is high, health systems are weak, and resources are constrained. Compared with large-scale real-life randomised control trials and cohort studies, DAMs can produce evidence of the health and economic benefits of interventions in an affordable and timely manner, while overcoming ethical issues, by using mathematical and logical relationships to abstract vital aspects of reality/systems for analysis.This study examines how DAMs–in particular, Markov, system dynamic, agent-based, discrete event simulation and hybrid models–have been applied to investigate non-pharmaceutical (NP) policy interventions and how to advance their adoption to diabetes research in LMICs. Methods: We conducted a systematic search of peer-reviewed articles published in English between 2000 and 2020 in PubMed, Cochrane and the reference list of reviewed articles. Articles were summarised and appraised based on publication details, model design and processes, modelled interventions, and model limitations. Results: Thirteen studies were included (six Markov, six microsimulation and one agent-based model), most of which modelled interventions in Asian sub-population. Except for one study (which examined tax increment), all studies assessed interventions within the health sector. Seven studies reported health and economic outcomes and the rest reported health outcomes only. Twelve studies reported on uncertainty analysis. Almost half of the studies did not report on model validation, whereas two studies combined multiple validation techniques to increase confidence in models. Four studies modelled policy interventions among diabetes patients; the rest concentrated on at-risk populations. All studies reported limitations with obtaining sufficient data for modelling. Conclusions: This review summarises the application of DAMs to NP diabetes interventions in LMICs. Our purpose is to identify gaps in their adoption and advance their appropriate use for assessing NP policies intervention aimed at controlling diabetes. Our study recommends that LMICs should leverage the usefulness of DAMs to support economic evaluation of population-wide NP policy interventions, particularly fiscal policies, to control diabetes.

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“…28 Similar to methods used in validating other models, we chose root-mean-square error (RMSE, for cumulative risks and time-varying prevalence estimates) and mean absolute percentage error (MAPE, for mortality rates) to evaluate the goodness of fit between STOP model results and data sources. [28][29][30][31][32][33][34] We applied the coefficient of variation of RMSE (CV-RMSE) as a relative measure of error.…”

Section: Analytical Overviewmentioning

confidence: 99%

Novel microsimulation model of tobacco use behaviours and outcomes: calibration and validation in a US population

et al. 2020

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Background and objectiveSimulation models can project effects of tobacco use and cessation and inform tobacco control policies. Most existing tobacco models do not explicitly include relapse, a key component of the natural history of tobacco use. Our objective was to develop, calibrate and validate a novel individual-level microsimulation model that would explicitly include smoking relapse and project cigarette smoking behaviours and associated mortality risks.MethodsWe developed the Simulation of Tobacco and Nicotine Outcomes and Policy (STOP) model, in which individuals transition monthly between tobacco use states (current/former/never) depending on rates of initiation, cessation and relapse. Simulated individuals face tobacco use-stratified mortality risks. For US women and men, we conducted cross-validation with a Cancer Intervention and Surveillance Modeling Network (CISNET) model. We then incorporated smoking relapse and calibrated cessation rates to reflect the difference between a transient quit attempt and sustained abstinence. We performed external validation with the National Health Interview Survey (NHIS) and the linked National Death Index. Comparisons were based on root-mean-square error (RMSE).ResultsIn cross-validation, STOP-generated projections of current/former/never smoking prevalence fit CISNET-projected data well (coefficient of variation (CV)-RMSE≤15%). After incorporating smoking relapse, multiplying the CISNET-reported cessation rates for women/men by 7.75/7.25, to reflect the ratio of quit attempts to sustained abstinence, resulted in the best approximation to CISNET-reported smoking prevalence (CV-RMSE 2%/3%). In external validation using these new multipliers, STOP-generated cumulative mortality curves for 20-year-old current smokers and never smokers each had CV-RMSE ≤1% compared with NHIS. In simulating those surveyed by NHIS in 1997, the STOP-projected prevalence of current/former/never smokers annually (1998–2009) was similar to that reported by NHIS (CV-RMSE 12%).ConclusionsThe STOP model, with relapse included, performed well when validated to US smoking prevalence and mortality. STOP provides a flexible framework for policy-relevant analysis of tobacco and nicotine product use.

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Development and Validation of PREDICT-DM: A New Microsimulation Model to Project and Evaluate Complications and Treatments of Type 2 Diabetes Mellitus

Cited by 12 publications

References 66 publications

Application of decision analytical models to diabetes in low- and middle-income countries: a systematic review

Application of decision analytical models to diabetes in low- and middle-income countries: a systematic review

Application of decision analytical models to diabetes in low- and middle-income countries: A systematic review

Novel microsimulation model of tobacco use behaviours and outcomes: calibration and validation in a US population

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