Reporting Quality of Discrete Event Simulations in Healthcare—Results From a Generic Reporting Checklist

Zhang, Xiange; Lhachimi, Stefan K.; Rogowski, W.

doi:10.1016/j.jval.2020.01.005

Cited by 13 publications

(13 citation statements)

References 34 publications

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“…The majority (19) of the 30 identification methods assessed model quality and transparency of reporting [2,15,[20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35]46]. Three methods specifically aimed at determining the model validity [36][37][38] and eight at assessing the quality of evidence used to develop the model [14,[39][40][41][42][43][44][45] (Table 1).…”

Section: Uncertainty Identification Methodsmentioning

confidence: 99%

Comprehensive Review of Methods to Assess Uncertainty in Health Economic Evaluations

Otten¹,

Grimm²,

Ramaekers³

et al. 2023

PharmacoEconomics

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Uncertainty assessment is a cornerstone in model-based health economic evaluations (HEEs) that inform reimbursement decisions. No comprehensive overview of available uncertainty assessment methods currently exists. We aimed to review methods for uncertainty assessment for use in model-based HEEs, by conducting a snowballing review. We categorised all methods according to their stage of use relating to uncertainty assessment (identification, analysis, communication). Additionally, we classified identification methods according to sources of uncertainty, and subdivided analysis and communication methods according to their purpose. The review identified a total of 80 uncertainty methods: 30 identification, 28 analysis, and 22 communication methods. Uncertainty identification methods exist to address uncertainty from different sources. Most identification methods were developed with the objective to assess related concepts such as validity, model quality, and relevance. Almost all uncertainty analysis and communication methods required uncertainty to be quantified and inclusion of uncertainties in probabilistic analysis. Our review can help analysts and decision makers in selecting uncertainty assessment methods according to their aim and purpose of the assessment. We noted a need for further clarification of terminology and guidance on the use of (combinations of) methods to identify uncertainty and related concepts such as validity and quality.A key finding is that uncertainty assessment relies heavily on quantification, which may necessitate increased use of expert elicitation and/or the development of methods to assess unquantified uncertainty.

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Section: Uncertainty Identification Methodsmentioning

confidence: 99%

Comprehensive Review of Methods to Assess Uncertainty in Health Economic Evaluations

Otten¹,

Grimm²,

Ramaekers³

et al. 2023

PharmacoEconomics

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“…We undertook a cost–utility analysis over a 10‐year time horizon and employed a discrete event simulation (DES) model. A DES model is suitable when the timing of key events is important to capture, where different treatment sequences or switching therapies is involved 10 . The model combined data from various sources, including population‐level pharmaceutical claims on drug use, published long‐term therapy retention rates, patient‐level hospital cost data, genomic databases on prevalence of variants, and systematic reviews (Table 1).…”

Section: Methodsmentioning

confidence: 99%

“…A DES model is suitable when the timing of key events is important to capture, where different treatment sequences or switching therapies is involved. 10 The model combined data from various sources, including population-level pharmaceutical claims on drug use, published long-term therapy retention rates, patient-level hospital cost data, genomic databases on prevalence of variants, and systematic reviews (Table 1). The model aggregated the costs and benefits of genomic sequencing and personalized treatment versus usual care for 5000 simulated patients.…”

Section: Overviewmentioning

confidence: 99%

Early cost–utility analysis of genetically guided therapy for patients with drug‐resistant epilepsy

et al. 2022

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Objective Existing gene panels were developed to understand the etiology of epilepsy, and further benefits will arise from an effective pharmacogenomics panel for personalizing therapy and achieving seizure control. Our study assessed the cost‐effectiveness of a pharmacogenomics panel for patients with drug‐resistant epilepsy, compared with usual care. Methods A cost–utility analysis was employed using a discrete event simulation model. The microsimulation model aggregated the costs and benefits of genetically guided treatment versus usual care for 5000 simulated patients. The 10‐year model combined data from various sources including genomic databases on prevalence of variants, population‐level pharmaceutical claims on antiseizure medications, published long‐term therapy retention rates, patient‐level cost data, and systematic reviews. Incremental cost per quality‐adjusted life‐year (QALY) gained was computed. Deterministic and probabilistic sensitivity analyses were undertaken to address uncertainty in model parameters. Results The mean cost of the genetically guided treatment option was AU$98 199 compared with AU$95 386 for usual care. Corresponding mean QALYs were 4.67 compared with 4.28 for genetically guided and usual care strategies, respectively. The incremental cost per QALY gained was AU$7381. In probabilistic sensitivity analyses, the incremental cost per QALY gained was AU$6321 (95% uncertainty interval = AU$3604–AU$9621), with a 100% likelihood of being cost‐effective in the Australian health care system. The most influential drivers of the findings were the monthly health care costs associated with reduced seizures, costs when seizures continued, and the quality‐of‐life estimates under genetically guided and usual care strategies. Significance This early economic evaluation of a pharmacogenomics panel to guide treatment for drug‐resistant epilepsy could potentially be cost‐effective in the Australian health care system. Clinical trial evidence is necessary to confirm these findings.

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“…Survey papers were analyzed alongside review papers as they were very few, and they focused on specific DES applications. Specific to the review papers included, only five studies focus entirely on DES as a unique review topic, and the rest aim to analyze healthcare improvements through diverse operations research techniques, DES being one of the approaches mentioned [21,[30][31][32][33].…”

Section: Review Methodologymentioning

confidence: 99%

Discrete-Event Simulation Modeling in Healthcare: A Comprehensive Review

Vázquez-Serrano

Peimbert-García

Cárdenas–Barrón

2021

IJERPH

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Discrete-event simulation (DES) is a stochastic modeling approach widely used to address dynamic and complex systems, such as healthcare. In this review, academic databases were systematically searched to identify 231 papers focused on DES modeling in healthcare. These studies were sorted by year, approach, healthcare setting, outcome, provenance, and software use. Among the surveys, conceptual/theoretical studies, reviews, and case studies, it was found that almost two-thirds of the theoretical articles discuss models that include DES along with other analytical techniques, such as optimization and lean/six sigma, and one-third of the applications were carried out in more than one healthcare setting, with emergency departments being the most popular. Moreover, half of the applications seek to improve time- and efficiency-related metrics, and one-third of all papers use hybrid models. Finally, the most popular DES software is Arena and Simul8. Overall, there is an increasing trend towards using DES in healthcare to address issues at an operational level, yet less than 10% of DES applications present actual implementations following the modeling stage. Thus, future research should focus on the implementation of the models to assess their impact on healthcare processes, patients, and, possibly, their clinical value. Other areas are DES studies that emphasize their methodological formulation, as well as the development of frameworks for hybrid models.

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Reporting Quality of Discrete Event Simulations in Healthcare—Results From a Generic Reporting Checklist

Cited by 13 publications

References 34 publications

Comprehensive Review of Methods to Assess Uncertainty in Health Economic Evaluations

Comprehensive Review of Methods to Assess Uncertainty in Health Economic Evaluations

Early cost–utility analysis of genetically guided therapy for patients with drug‐resistant epilepsy

Discrete-Event Simulation Modeling in Healthcare: A Comprehensive Review

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