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Pineda-Antunez C, Seguin C, van Duuren LA, Knudsen AB, Davidi B, de Lima PN, Rutter C, Kuntz KM, Lansdorp-Vogelaar I, Collier N, Ozik J, Alarid-Escudero F. Emulator-based Bayesian calibration of the CISNET colorectal cancer models. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.02.27.23286525. [PMID: 36909607 PMCID: PMC10002763 DOI: 10.1101/2023.02.27.23286525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
Abstract
Purpose To calibrate Cancer Intervention and Surveillance Modeling Network (CISNET) 's SimCRC, MISCAN-Colon, and CRC-SPIN simulation models of the natural history colorectal cancer (CRC) with an emulator-based Bayesian algorithm and internally validate the model-predicted outcomes to calibration targets. Methods We used Latin hypercube sampling to sample up to 50,000 parameter sets for each CISNET-CRC model and generated the corresponding outputs. We trained multilayer perceptron artificial neural networks (ANN) as emulators using the input and output samples for each CISNET-CRC model. We selected ANN structures with corresponding hyperparameters (i.e., number of hidden layers, nodes, activation functions, epochs, and optimizer) that minimize the predicted mean square error on the validation sample. We implemented the ANN emulators in a probabilistic programming language and calibrated the input parameters with Hamiltonian Monte Carlo-based algorithms to obtain the joint posterior distributions of the CISNET-CRC models' parameters. We internally validated each calibrated emulator by comparing the model-predicted posterior outputs against the calibration targets. Results The optimal ANN for SimCRC had four hidden layers and 360 hidden nodes, MISCAN-Colon had 4 hidden layers and 114 hidden nodes, and CRC-SPIN had one hidden layer and 140 hidden nodes. The total time for training and calibrating the emulators was 7.3, 4.0, and 0.66 hours for SimCRC, MISCAN-Colon, and CRC-SPIN, respectively. The mean of the model-predicted outputs fell within the 95% confidence intervals of the calibration targets in 98 of 110 for SimCRC, 65 of 93 for MISCAN, and 31 of 41 targets for CRC-SPIN. Conclusions Using ANN emulators is a practical solution to reduce the computational burden and complexity for Bayesian calibration of individual-level simulation models used for policy analysis, like the CISNET CRC models. In this work, we present a step-by-step guide to constructing emulators for calibrating three realistic CRC individual-level models using a Bayesian approach.
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Affiliation(s)
- Carlos Pineda-Antunez
- The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, University of Washington, Seattle, WA, United States
| | - Claudia Seguin
- Institute for Technology Assessment, Massachusetts General Hospital, Boston, MA, United States
| | - Luuk A van Duuren
- Department of Public Health, Erasmus MC Medical Center Rotterdam, The Netherlands
| | - Amy B Knudsen
- Institute for Technology Assessment, Massachusetts General Hospital, Boston, MA, United States
| | - Barak Davidi
- Institute for Technology Assessment, Massachusetts General Hospital, Boston, MA, United States
| | | | - Carolyn Rutter
- Fred Hutchinson Cancer Research Center, Hutchinson Institute for Cancer Outcomes Research, Biostatistics Program, Public Health Sciences Division, Seattle WA
| | - Karen M Kuntz
- Division of Health Policy & Management, University of Minnesota School of Public Health, Minneapolis, MN, United States
| | | | - Nicholson Collier
- Decision and Infrastructure Sciences Division, Argonne National Laboratory, Lemont, IL, United States
- Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL, United States
| | - Jonathan Ozik
- Decision and Infrastructure Sciences Division, Argonne National Laboratory, Lemont, IL, United States
- Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL, United States
| | - Fernando Alarid-Escudero
- Department of Health Policy, School of Medicine, Stanford University, CA, US
- Center for Health Policy, Freeman Spogli Institute, Stanford University, CA, US
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Calibrating spatiotemporal models of microbial communities to microscopy data: A review. PLoS Comput Biol 2022; 18:e1010533. [PMID: 36227846 PMCID: PMC9560168 DOI: 10.1371/journal.pcbi.1010533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
Spatiotemporal models that account for heterogeneity within microbial communities rely on single-cell data for calibration and validation. Such data, commonly collected via microscopy and flow cytometry, have been made more accessible by recent advances in microfluidics platforms and data processing pipelines. However, validating models against such data poses significant challenges. Validation practices vary widely between modelling studies; systematic and rigorous methods have not been widely adopted. Similar challenges are faced by the (macrobial) ecology community, in which systematic calibration approaches are often employed to improve quantitative predictions from computational models. Here, we review single-cell observation techniques that are being applied to study microbial communities and the calibration strategies that are being employed for accompanying spatiotemporal models. To facilitate future calibration efforts, we have compiled a list of summary statistics relevant for quantifying spatiotemporal patterns in microbial communities. Finally, we highlight some recently developed techniques that hold promise for improved model calibration, including algorithmic guidance of summary statistic selection and machine learning approaches for efficient model simulation.
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Zhong H, Brandeau ML, Yazdi GE, Wang J, Nolen S, Hagan L, Thompson WW, Assoumou SA, Linas BP, Salomon JA. Metamodeling for Policy Simulations with Multivariate Outcomes. Med Decis Making 2022; 42:872-884. [PMID: 35735216 PMCID: PMC9452454 DOI: 10.1177/0272989x221105079] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
PURPOSE Metamodels are simplified approximations of more complex models that can be used as surrogates for the original models. Challenges in using metamodels for policy analysis arise when there are multiple correlated outputs of interest. We develop a framework for metamodeling with policy simulations to accommodate multivariate outcomes. METHODS We combine 2 algorithm adaptation methods-multitarget stacking and regression chain with maximum correlation-with different base learners including linear regression (LR), elastic net (EE) with second-order terms, Gaussian process regression (GPR), random forests (RFs), and neural networks. We optimize integrated models using variable selection and hyperparameter tuning. We compare the accuracy, efficiency, and interpretability of different approaches. As an example application, we develop metamodels to emulate a microsimulation model of testing and treatment strategies for hepatitis C in correctional settings. RESULTS Output variables from the simulation model were correlated (average ρ = 0.58). Without multioutput algorithm adaptation methods, in-sample fit (measured by R2) ranged from 0.881 for LR to 0.987 for GPR. The multioutput algorithm adaptation method increased R2 by an average 0.002 across base learners. Variable selection and hyperparameter tuning increased R2 by 0.009. Simpler models such as LR, EE, and RF required minimal training and prediction time. LR and EE had advantages in model interpretability, and we considered methods for improving the interpretability of other models. CONCLUSIONS In our example application, the choice of base learner had the largest impact on R2; multioutput algorithm adaptation and variable selection and hyperparameter tuning had a modest impact. Although advantages and disadvantages of specific learning algorithms may vary across different modeling applications, our framework for metamodeling in policy analyses with multivariate outcomes has broad applicability to decision analysis in health and medicine.
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Affiliation(s)
- Huaiyang Zhong
- Department of Management Science and Engineering, Stanford University, Stanford, CA, USA
| | - Margaret L Brandeau
- Department of Management Science and Engineering, Stanford University, Stanford, CA, USA
| | - Golnaz Eftekhari Yazdi
- Section of Infectious Diseases, Department of Medicine, Boston Medical Center, Boston, MA, USA
| | - Jianing Wang
- Section of Infectious Diseases, Department of Medicine, Boston Medical Center, Boston, MA, USA
| | - Shayla Nolen
- Section of Infectious Diseases, Department of Medicine, Boston Medical Center, Boston, MA, USA
| | | | - William W Thompson
- Division of Viral Hepatitis, Center for Disease Control and Prevention, Atlanta, GA, USA
| | - Sabrina A Assoumou
- Section of Infectious Diseases, Department of Medicine, Boston Medical Center, Boston, MA, USA
| | - Benjamin P Linas
- Section of Infectious Diseases, Department of Medicine, Boston Medical Center, Boston, MA, USA
| | - Joshua A Salomon
- Center for Health Policy and Center for Primary Care and Outcomes Research, Stanford University, Stanford, CA, USA
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Malloy GSP, Brandeau ML. When Is Mass Prophylaxis Cost-Effective for Epidemic Control? A Comparison of Decision Approaches. Med Decis Making 2022; 42:1052-1063. [PMID: 35591754 DOI: 10.1177/0272989x221098409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND For certain communicable disease outbreaks, mass prophylaxis of uninfected individuals can curtail new infections. When an outbreak emerges, decision makers could benefit from methods to quickly determine whether mass prophylaxis is cost-effective. We consider 2 approaches: a simple decision model and machine learning meta-models. The motivating example is plague in Madagascar. METHODS We use a susceptible-exposed-infectious-removed (SEIR) epidemic model to derive a decision rule based on the fraction of the population infected, effective reproduction ratio, infection fatality rate, quality-adjusted life-year loss associated with death, prophylaxis effectiveness and cost, time horizon, and willingness-to-pay threshold. We also develop machine learning meta-models of a detailed model of plague in Madagascar using logistic regression, random forest, and neural network models. In numerical experiments, we compare results using the decision rule and the meta-models to results obtained using the simulation model. We vary the initial fraction of the population infected, the effective reproduction ratio, the intervention start date and duration, and the cost of prophylaxis. LIMITATIONS We assume homogeneous mixing and no negative side effects due to antibiotic prophylaxis. RESULTS The simple decision rule matched the SEIR model outcome in 85.4% of scenarios. Using data for a 2017 plague outbreak in Madagascar, the decision rule correctly indicated that mass prophylaxis was not cost-effective. The meta-models were significantly more accurate, with an accuracy of 92.8% for logistic regression, 95.8% for the neural network model, and 96.9% for the random forest model. CONCLUSIONS A simple decision rule using minimal information about an outbreak can accurately evaluate the cost-effectiveness of mass prophylaxis for outbreak mitigation. Meta-models of a complex disease simulation can achieve higher accuracy but with greater computational and data requirements and less interpretability. HIGHLIGHTS We use a susceptible-exposed-infectious-removed model and net monetary benefit to derive a simple decision rule to evaluate the cost-effectiveness of mass prophylaxis.We use the example of plague in Madagascar to compare the performance of the analytically derived decision rule to that of machine learning meta-models trained on a stochastic dynamic transmission model.We assess the accuracy of each approach for different combinations of disease dynamics and intervention scenarios.The machine learning meta-models are more accurate predictors of mass prophylaxis cost-effectiveness. However, the simple decision rule is also accurate and may be a preferred substitute in low-resource settings.
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Affiliation(s)
- Giovanni S P Malloy
- Department of Management Science and Engineering, Stanford University, Stanford, CA, USA
| | - Margaret L Brandeau
- Department of Management Science and Engineering, Stanford University, Stanford, CA, USA
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Pandya A, Yu YJ, Ge Y, Nagel E, Kwong RY, Bakar RA, Grizzard JD, Merkler AE, Ntusi N, Petersen SE, Rashedi N, Schwitter J, Selvanayagam JB, White JA, Carr J, Raman SV, Simonetti OP, Bucciarelli-Ducci C, Sierra-Galan LM, Ferrari VA, Bhatia M, Kelle S. Evidence-based cardiovascular magnetic resonance cost-effectiveness calculator for the detection of significant coronary artery disease. J Cardiovasc Magn Reson 2022; 24:1. [PMID: 34986851 PMCID: PMC8734365 DOI: 10.1186/s12968-021-00833-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 11/30/2021] [Indexed: 01/30/2023] Open
Abstract
BACKGROUND Although prior reports have evaluated the clinical and cost impacts of cardiovascular magnetic resonance (CMR) for low-to-intermediate-risk patients with suspected significant coronary artery disease (CAD), the cost-effectiveness of CMR compared to relevant comparators remains poorly understood. We aimed to summarize the cost-effectiveness literature on CMR for CAD and create a cost-effectiveness calculator, useable worldwide, to approximate the cost-per-quality-adjusted-life-year (QALY) of CMR and relevant comparators with context-specific patient-level and system-level inputs. METHODS We searched the Tufts Cost-Effectiveness Analysis Registry and PubMed for cost-per-QALY or cost-per-life-year-saved studies of CMR to detect significant CAD. We also developed a linear regression meta-model (CMR Cost-Effectiveness Calculator) based on a larger CMR cost-effectiveness simulation model that can approximate CMR lifetime discount cost, QALY, and cost effectiveness compared to relevant comparators [such as single-photon emission computed tomography (SPECT), coronary computed tomography angiography (CCTA)] or invasive coronary angiography. RESULTS CMR was cost-effective for evaluation of significant CAD (either health-improving and cost saving or having a cost-per-QALY or cost-per-life-year result lower than the cost-effectiveness threshold) versus its relevant comparator in 10 out of 15 studies, with 3 studies reporting uncertain cost effectiveness, and 2 studies showing CCTA was optimal. Our cost-effectiveness calculator showed that CCTA was not cost-effective in the US compared to CMR when the most recent publications on imaging performance were included in the model. CONCLUSIONS Based on current world-wide evidence in the literature, CMR usually represents a cost-effective option compared to relevant comparators to assess for significant CAD.
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Affiliation(s)
- Ankur Pandya
- Department of Health Policy and Management, Harvard T.H. Chan School of Public Health, 718 Huntington Ave, 2nd Floor, Boston, MA, 02115, USA.
| | - Yuan-Jui Yu
- National Taiwan University Hospital, Taipei, Taiwan
| | - Yin Ge
- Cardiovascular Division of the Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Eike Nagel
- Institute for Experimental and Translational Cardiovascular Imaging, DZHK (German Centre for Cardiovascular Research) Centre for Cardiovascular Imaging, Partner Site RheinMain, University Hospital Frankfurt/Main, Frankfurt am Main, Germany
| | - Raymond Y Kwong
- Cardiovascular Division of the Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Rafidah Abu Bakar
- Department of Cardiology, National Heart Institute, Kuala Lumpur, Malaysia
| | - John D Grizzard
- Department of Radiology, Virginia Commonwealth University Medical Center, Main Hospital, Richmond, VA, USA
| | - Alexander E Merkler
- Department of Neurology, Weill Cornell Medicine/NewYork-Presbyterian Hospital, New York, NY, USA
| | - Ntobeko Ntusi
- Department of Medicine, University of Cape Town & Groote Schuur Hospital, Cape Town, South Africa
| | - Steffen E Petersen
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, UK
| | - Nina Rashedi
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Juerg Schwitter
- Division of Cardiology, Cardiovascular Department, CMR Center University Hospital, Lausanne, Switzerland
- Faculty of Biology and Medicine, University of Lausanne, UniL, Lausanne, Switzerland
| | - Joseph B Selvanayagam
- Department of Medicine, School of Medicine and Public Health, Flinders University, Adelaide, Australia
- Department of Heart Health, South Australian Health and Medical Research Institute, Adelaide, Australia
| | - James A White
- Division of Cardiology, Department of Cardiac Sciences, Stephenson Cardiac Imaging Centre, University of Calgary, Calgary, Canada
| | - James Carr
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Subha V Raman
- Krannert Institute of Cardiology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Orlando P Simonetti
- Departments of Internal Medicine and Radiology, The Ohio State University, Columbus, OH, USA
| | - Chiara Bucciarelli-Ducci
- Royal Brompton and Harefield Hospitals, Guys' and St Thomas NHS Hospitals and School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Lilia M Sierra-Galan
- Cardiovascular Division, Department of Cardiology, American British Cowdray Medical Center, Mexico City, Mexico
| | - Victor A Ferrari
- Cardiovascular Division and Penn Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania Medical Center, Philadelphia, PA, USA
| | - Mona Bhatia
- Department of Imaging, Fortis Escorts Heart Institute, New Delhi, India
| | - Sebastian Kelle
- Department of Internal Medicine and Cardiology, Charité - Universitätsmedizin Berlin, Campus Virchow Klinikum, Berlin, Germany
- Department of Internal Medicine and Cardiology, DZHK (German Centre for Cardiovascular Research), Partner Site Berlin, German Heart Institute Berlin (DHZB), Berlin, Germany
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Weyant C, Brandeau ML. Personalization of Medical Treatment Decisions: Simplifying Complex Models while Maintaining Patient Health Outcomes. Med Decis Making 2021; 42:450-460. [PMID: 34416832 PMCID: PMC8858337 DOI: 10.1177/0272989x211037921] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
BACKGROUND Personalizing medical treatments based on patient-specific risks and preferences can improve patient health. However, models to support personalized treatment decisions are often complex and difficult to interpret, limiting their clinical application. METHODS We present a new method, using machine learning to create meta-models, for simplifying complex models for personalizing medical treatment decisions. We consider simple interpretable models, interpretable ensemble models, and noninterpretable ensemble models. We use variable selection with a penalty for patient-specific risks and/or preferences that are difficult, risky, or costly to obtain. We interpret the meta-models to the extent permitted by their model architectures. We illustrate our method by applying it to simplify a previously developed model for personalized selection of antipsychotic drugs for patients with schizophrenia. RESULTS The best simplified interpretable, interpretable ensemble, and noninterpretable ensemble models contained at most half the number of patient-specific risks and preferences compared with the original model. The simplified models achieved 60.5% (95% credible interval [crI]: 55.2-65.4), 60.8% (95% crI: 55.5-65.7), and 83.8% (95% crI: 80.8-86.6), respectively, of the net health benefit of the original model (quality-adjusted life-years gained). Important variables in all models were similar and made intuitive sense. Computation time for the meta-models was orders of magnitude less than for the original model. LIMITATIONS The simplified models share the limitations of the original model (e.g., potential biases). CONCLUSIONS Our meta-modeling method is disease- and model- agnostic and can be used to simplify complex models for personalization, allowing for variable selection in addition to improved model interpretability and computational performance. Simplified models may be more likely to be adopted in clinical settings and can help improve equity in patient outcomes.
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Affiliation(s)
- Christopher Weyant
- Department of Management Science and Engineering, Stanford University, Stanford, CA, USA
| | - Margaret L Brandeau
- Department of Management Science and Engineering, Stanford University, Stanford, CA, USA
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Jalal H, Trikalinos TA, Alarid-Escudero F. BayCANN: Streamlining Bayesian Calibration With Artificial Neural Network Metamodeling. Front Physiol 2021; 12:662314. [PMID: 34113262 PMCID: PMC8185956 DOI: 10.3389/fphys.2021.662314] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 04/20/2021] [Indexed: 12/03/2022] Open
Abstract
Purpose: Bayesian calibration is generally superior to standard direct-search algorithms in that it estimates the full joint posterior distribution of the calibrated parameters. However, there are many barriers to using Bayesian calibration in health decision sciences stemming from the need to program complex models in probabilistic programming languages and the associated computational burden of applying Bayesian calibration. In this paper, we propose to use artificial neural networks (ANN) as one practical solution to these challenges. Methods: Bayesian Calibration using Artificial Neural Networks (BayCANN) involves (1) training an ANN metamodel on a sample of model inputs and outputs, and (2) then calibrating the trained ANN metamodel instead of the full model in a probabilistic programming language to obtain the posterior joint distribution of the calibrated parameters. We illustrate BayCANN using a colorectal cancer natural history model. We conduct a confirmatory simulation analysis by first obtaining parameter estimates from the literature and then using them to generate adenoma prevalence and cancer incidence targets. We compare the performance of BayCANN in recovering these "true" parameter values against performing a Bayesian calibration directly on the simulation model using an incremental mixture importance sampling (IMIS) algorithm. Results: We were able to apply BayCANN using only a dataset of the model inputs and outputs and minor modification of BayCANN's code. In this example, BayCANN was slightly more accurate in recovering the true posterior parameter estimates compared to IMIS. Obtaining the dataset of samples, and running BayCANN took 15 min compared to the IMIS which took 80 min. In applications involving computationally more expensive simulations (e.g., microsimulations), BayCANN may offer higher relative speed gains. Conclusions: BayCANN only uses a dataset of model inputs and outputs to obtain the calibrated joint parameter distributions. Thus, it can be adapted to models of various levels of complexity with minor or no change to its structure. In addition, BayCANN's efficiency can be especially useful in computationally expensive models. To facilitate BayCANN's wider adoption, we provide BayCANN's open-source implementation in R and Stan.
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Affiliation(s)
- Hawre Jalal
- Department of Health Policy and Management, University of Pittsburgh, Graduate School of Public Health, Pittsburgh, PA, United States
| | - Thomas A. Trikalinos
- Departments of Health Services, Policy & Practice and Biostatistics, Brown University, Providence, RI, United States
| | - Fernando Alarid-Escudero
- Division of Public Administration, Center for Research and Teaching in Economics (CIDE), Aguascalientes, Mexico
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Koffijberg H, Degeling K, IJzerman MJ, Coupé VMH, Greuter MJE. Using Metamodeling to Identify the Optimal Strategy for Colorectal Cancer Screening. VALUE IN HEALTH : THE JOURNAL OF THE INTERNATIONAL SOCIETY FOR PHARMACOECONOMICS AND OUTCOMES RESEARCH 2021; 24:206-215. [PMID: 33518027 DOI: 10.1016/j.jval.2020.08.2099] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2019] [Revised: 08/07/2020] [Accepted: 08/18/2020] [Indexed: 06/12/2023]
Abstract
OBJECTIVES Metamodeling can address computational challenges within decision-analytic modeling studies evaluating many strategies. This article illustrates the value of metamodeling for evaluating colorectal cancer screening strategies while accounting for colonoscopy capacity constraints. METHODS In a traditional approach, the best screening strategy was identified from a limited subset of strategies evaluated with the validated Adenoma and Serrated pathway to Colorectal CAncer model. In a metamodeling approach, metamodels were fitted to this limited subset to evaluate all potentially plausible strategies and determine the best overall screening strategy. Approaches were compared based on the best screening strategy in life-years gained compared with no screening. Metamodel runtime and accuracy was assessed. RESULTS The metamodeling approach evaluated >40 000 strategies in <1 minute with high accuracy after 1 adaptive sampling step (mean absolute error: 0.0002 life-years) using 300 samples in total (generation time: 8 days). Findings indicated that health outcomes could be improved without requiring additional colonoscopy capacity. Obtaining similar insights using the traditional approach could require at least 1000 samples (generation time: 28 days). Suggested benefits from screening at ages <40 years require adequate validation of the underlying Adenoma and Serrated pathway to Colorectal CAncer model before making policy recommendations. CONCLUSIONS Metamodeling allows rapid assessment of a vast set of strategies, which may lead to identification of more favorable strategies compared to a traditional approach. Nevertheless, metamodel validation and identifying extrapolation beyond the support of the original decision-analytic model are critical to the interpretation of results. The screening strategies identified with metamodeling support ongoing discussions on decreasing the starting age of colorectal cancer screening.
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Affiliation(s)
- Hendrik Koffijberg
- Health Technology and Services Research Department, Technical Medical Centre, University of Twente, Enschede, The Netherlands.
| | - Koen Degeling
- Health Technology and Services Research Department, Technical Medical Centre, University of Twente, Enschede, The Netherlands
| | - Maarten J IJzerman
- Health Technology and Services Research Department, Technical Medical Centre, University of Twente, Enschede, The Netherlands; Centre for Cancer Research and Centre for Health Policy, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Australia; Melbourne School of Population and Global Health, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Australia; Department of Cancer Research, Peter MacCallum Cancer Centre, Melbourne, Australia
| | - Veerle M H Coupé
- Decision Modeling Center, Department of Epidemiology and Data Science, Amsterdam UMC - location VUmc, Amsterdam, the Netherlands
| | - Marjolein J E Greuter
- Decision Modeling Center, Department of Epidemiology and Data Science, Amsterdam UMC - location VUmc, Amsterdam, the Netherlands
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