Medical Decision Making最新文献

{"title":"Accurate EVSI Estimation for Nonlinear Models Using the Gaussian Approximation Method.","authors":"Linke Li, Hawre Jalal, Anna Heath","doi":"10.1177/0272989X241264287","DOIUrl":"https://doi.org/10.1177/0272989X241264287","url":null,"abstract":"<p><strong>Background: </strong>The expected value of sample information (EVSI) measures the expected benefits that could be obtained by collecting additional data. Estimating EVSI using the traditional nested Monte Carlo method is computationally expensive, but the recently developed Gaussian approximation (GA) approach can efficiently estimate EVSI across different sample sizes. However, the conventional GA may result in biased EVSI estimates if the decision models are highly nonlinear. This bias may lead to suboptimal study designs when GA is used to optimize the value of different studies. Therefore, we extend the conventional GA approach to improve its performance for nonlinear decision models.</p><p><strong>Methods: </strong>Our method provides accurate EVSI estimates by approximating the conditional expectation of the benefit based on 2 steps. First, a Taylor series approximation is applied to estimate the conditional expectation of the benefit as a function of the conditional moments of the parameters of interest using a spline, which is fitted to the samples of the parameters and the corresponding benefits. Next, the conditional moments of parameters are approximated by the conventional GA and Fisher information. The proposed approach is applied to several data collection exercises involving non-Gaussian parameters and nonlinear decision models. Its performance is compared with the nested Monte Carlo method, the conventional GA approach, and the nonparametric regression-based method for EVSI calculation.</p><p><strong>Results: </strong>The proposed approach provides accurate EVSI estimates across different sample sizes when the parameters of interest are non-Gaussian and the decision models are nonlinear. The computational cost of the proposed method is similar to that of other novel methods.</p><p><strong>Conclusions: </strong>The proposed approach can estimate EVSI across sample sizes accurately and efficiently, which may support researchers in determining an economically optimal study design using EVSI.</p><p><strong>Highlights: </strong>The Gaussian approximation method efficiently estimates the expected value of sample information (EVSI) for clinical trials with varying sample sizes, but it may introduce bias when health economic models have a nonlinear structure.We introduce the spline-based Taylor series approximation method and combine it with the original Gaussian approximation to correct the nonlinearity-induced bias in EVSI estimation.Our approach can provide more precise EVSI estimates for complex decision models without sacrificing computational efficiency, which can enhance the resource allocation strategies from the cost-effective perspective.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141856992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying Decisional Needs for Adult Tracheostomy and Prolonged Mechanical Ventilation Decision Making to Inform Shared Decision-Making Interventions.","authors":"Anuj B Mehta, Steven Lockhart, Allison V Lange, Daniel D Matlock, Ivor S Douglas, Megan A Morris","doi":"10.1177/0272989X241266246","DOIUrl":"10.1177/0272989X241266246","url":null,"abstract":"<p><strong>Background: </strong>Decision making for adult tracheostomy and prolonged mechanical ventilation is emotionally complex. Expectations of surrogate decision makers and physicians rarely align. Little is known about what surrogates need to make goal-concordant decisions. Currently, little is known about the decisional needs of surrogates and providers, impeding efforts to improve the decision-making process.</p><p><strong>Methods: </strong>Using a thematic analysis approach, we performed a qualitative study with semistructured interviews with surrogates of adult patients receiving mechanical ventilation (MV) being considered for tracheostomy and physicians routinely caring for patients receiving MV. Recruitment was stopped when thematic saturation was reached. We describe the decision-making process, identify core decisional needs, and map the process and needs for possible elements of a future shared decision-making tool.</p><p><strong>Results: </strong>Forty-three participants (23 surrogates and 20 physicians) completed interviews. Hope, Lack of Knowledge Data, and Uncertainty emerged as the 3 main themes that described the decision-making process and were interconnected with one another and, at times, opposed each other. Core decisional needs included information about patient wishes, past activity/medical history, short- and long-term outcomes, and meaningful recovery. The themes were the lens through which the decisional needs were weighed. Decision making existed as a balance between surrogate emotions and understanding and physician recommendations.</p><p><strong>Conclusions: </strong>Tracheostomy and prolonged MV decision making is complex. Hope and Uncertainty were conceptual themes that often battled with one another. Lack of Knowledge & Data plagued both surrogates and physicians. Multiple tangible factors were identified that affected surrogate decision making and physician recommendations.</p><p><strong>Implications: </strong>Understanding this complex decision-making process has the potential to improve the information provided to surrogates and, potentially, increase the goal-concordant care and alignment of surrogate and physician expectations.</p><p><strong>Highlights: </strong>Decision making for tracheostomy and prolonged mechanical ventilation is a complex interactive process between surrogate decision makers and providers.Qualitative themes of Hope, Uncertainty, and Lack of Knowledge & Data shared by both providers and surrogates were identified and described the decision-making process.Concrete decisional needs of patient wishes, past activity/medical history, short- and long-term outcomes, and meaningful recovery affected each of the larger themes and represented key information from which surrogates and providers based decisions and recommendations.The qualitative themes and decisional needs identified provide a roadmap to design a shared decision-making intervention to improve adult tracheostomy and prolonged mec","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141856993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling Radiologists' Assessments to Explore Pairing Strategies for Optimized Double Reading of Screening Mammograms.","authors":"Jessie J J Gommers, Craig K Abbey, Fredrik Strand, Sian Taylor-Phillips, David J Jenkinson, Marthe Larsen, Solveig Hofvind, Mireille J M Broeders, Ioannis Sechopoulos","doi":"10.1177/0272989X241264572","DOIUrl":"https://doi.org/10.1177/0272989X241264572","url":null,"abstract":"<p><strong>Purpose: </strong>To develop a model that simulates radiologist assessments and use it to explore whether pairing readers based on their individual performance characteristics could optimize screening performance.</p><p><strong>Methods: </strong>Logistic regression models were designed and used to model individual radiologist assessments. For model evaluation, model-predicted individual performance metrics and paired disagreement rates were compared against the observed data using Pearson correlation coefficients. The logistic regression models were subsequently used to simulate different screening programs with reader pairing based on individual true-positive rates (TPR) and/or false-positive rates (FPR). For this, retrospective results from breast cancer screening programs employing double reading in Sweden, England, and Norway were used. Outcomes of random pairing were compared against those composed of readers with similar and opposite TPRs/FPRs, with positive assessments defined by either reader flagging an examination as abnormal.</p><p><strong>Results: </strong>The analysis data sets consisted of 936,621 (Sweden), 435,281 (England), and 1,820,053 (Norway) examinations. There was good agreement between the model-predicted and observed radiologists' TPR and FPR (<i>r</i> ≥ 0.969). Model-predicted negative-case disagreement rates showed high correlations (<i>r</i> ≥ 0.709), whereas positive-case disagreement rates had lower correlation levels due to sparse data (<i>r</i> ≥ 0.532). Pairing radiologists with similar FPR characteristics (Sweden: 4.50% [95% confidence interval: 4.46%-4.54%], England: 5.51% [5.47%-5.56%], Norway: 8.03% [7.99%-8.07%]) resulted in significantly lower FPR than with random pairing (Sweden: 4.74% [4.70%-4.78%], England: 5.76% [5.71%-5.80%], Norway: 8.30% [8.26%-8.34%]), reducing examinations sent to consensus/arbitration while the TPR did not change significantly. Other pairing strategies resulted in equal or worse performance than random pairing.</p><p><strong>Conclusions: </strong>Logistic regression models accurately predicted screening mammography assessments and helped explore different radiologist pairing strategies. Pairing readers with similar modeled FPR characteristics reduced the number of examinations unnecessarily sent to consensus/arbitration without significantly compromising the TPR.</p><p><strong>Highlights: </strong>A logistic-regression model can be derived that accurately predicts individual and paired reader performance during mammography screening reading.Pairing screening mammography radiologists with similar false-positive characteristics reduced false-positive rates with no significant loss in true positives and may reduce the number of examinations unnecessarily sent to consensus/arbitration.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141793892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Net Monetary Benefit Lines Augmented with Value-of-Information Measures to Present the Results of Economic Evaluations under Uncertainty.","authors":"Reza Yaesoubi, Natalia Kunst","doi":"10.1177/0272989X241262343","DOIUrl":"https://doi.org/10.1177/0272989X241262343","url":null,"abstract":"<p><strong>Background: </strong>Methods to present the result of cost-effectiveness analyses under parameter uncertainty include cost-effectiveness planes (CEPs), cost-effectiveness acceptability curves/frontier (CEACs/CEAF), expected loss curves (ELCs), and net monetary benefit (NMB) lines. We describe how NMB lines can be augmented to present NMB values that could be achieved by reducing or resolving parameter uncertainty. We evaluated the ability of these methods to correctly 1) identify the alternative with the highest expected NMB and 2) communicate the magnitude of parameter and decision uncertainty.</p><p><strong>Methods: </strong>We considered 4 hypothetical decision problems representing scenarios with high variance or correlated cost and effect estimates and alternatives with similar cost-effectiveness ratios. We used these decision problems to demonstrate the limitations of existing methods and the potential of augmented NMB lines to resolve these issues.</p><p><strong>Results: </strong>CEPs and CEACs/CEAF could falsely imply the lack of sufficient evidence to identify the optimal option if cost and effect estimates have high variance, are correlated across alternatives, or when alternatives have similar cost-effectiveness ratios. The augmented NMB lines and ELCs can correctly identify the option with the highest expected NMB and communicate the potential benefit of resolving uncertainties. Like ELCs, the augmented NMB lines provide information about the value of resolving parameter uncertainties, but augmented NMB lines may be easier to interpret for decision makers.</p><p><strong>Conclusions: </strong>Our analysis supports recommending the augment NMB lines as an important method to present the results of economic evaluation studies under parameter uncertainty.</p><p><strong>Highlights: </strong>The results of cost-effectiveness analyses (CEAs) when the cost and effect estimates of alternatives are uncertain are commonly presented using cost-effectiveness planes (CEPs), cost-effectiveness acceptability curves/frontier (CEACs/CEAF), and expected loss curves (ELCs).Although currently not often used, net monetary benefit (NMB) lines could present the results of cost-effectiveness to identify the alternative with the highest expected NMB values given the current level of uncertainty. Furthermore, NMB lines can be augmented to 1) show metrics of value of information, which measure the value of additional research to reduce or eliminate the decision uncertainty, and 2) display the confidence intervals along the NMB lines to ensure that NMB values are estimated accurately using a sufficiently large number of parameter samples.Using several decision problems, we demonstrate the limitation of existing methods to present the results of CEAs under parameter uncertainty and how augmented NMB lines could resolve these issues.Our analysis supports recommending augmented NMB lines as an important method to present the results of CEA under uncertain","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141762145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Methods to Quantify the Importance of Parameters for Model Updating and Distributional Adaptation.","authors":"David Glynn, Susan Griffin, Nils Gutacker, Simon Walker","doi":"10.1177/0272989X241262037","DOIUrl":"https://doi.org/10.1177/0272989X241262037","url":null,"abstract":"<p><strong>Purpose: </strong>Decision models are time-consuming to develop; therefore, adapting previously developed models for new purposes may be advantageous. We provide methods to prioritize efforts to 1) update parameter values in existing models and 2) adapt existing models for distributional cost-effectiveness analysis (DCEA).</p><p><strong>Methods: </strong>Methods exist to assess the influence of different input parameters on the results of a decision models, including value of information (VOI) and 1-way sensitivity analysis (OWSA). We apply 1) VOI to prioritize searches for additional information to update parameter values and 2) OWSA to prioritize searches for parameters that may vary by socioeconomic characteristics. We highlight the assumptions required and propose metrics that quantify the extent to which parameters in a model have been updated or adapted. We provide R code to quickly carry out the analysis given inputs from a probabilistic sensitivity analysis (PSA) and demonstrate our methods using an oncology case study.</p><p><strong>Results: </strong>In our case study, updating 2 of 21 probabilistic model parameters addressed 71.5% of the total VOI and updating 3 addressed approximately 100% of the uncertainty. Our proposed approach suggests that these are the 3 parameters that should be prioritized. For model adaptation for DCEA, 46.3% of the total OWSA variation came from a single parameter, while the top 10 input parameters were found to account for more than 95% of the total variation, suggesting efforts should be aimed toward these.</p><p><strong>Conclusions: </strong>These methods offer a systematic approach to guide research efforts in updating models with new data or adapting models to undertake DCEA. The case study demonstrated only very small gains from updating more than 3 parameters or adapting more than 10 parameters.</p><p><strong>Highlights: </strong>It can require considerable analyst time to search for evidence to update a model or to adapt a model to take account of equity concerns.In this article, we provide a quantitative method to prioritze parameters to 1) update existing models to reflect potential new evidence and 2) adapt existing models to estimate distributional outcomes.We define metrics that quantify the extent to which the parameters in a model have been updated or adapted.We provide R code that can quickly rank parameter importance and calculate quality metrics using only the results of a standard probabilistic sensitivity analysis.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141762144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning Methods to Estimate Individualized Treatment Effects for Use in Health Technology Assessment.","authors":"Yingying Zhang, Noemi Kreif, Vijay S Gc, Andrea Manca","doi":"10.1177/0272989X241263356","DOIUrl":"10.1177/0272989X241263356","url":null,"abstract":"<p><strong>Background: </strong>Recent developments in causal inference and machine learning (ML) allow for the estimation of individualized treatment effects (ITEs), which reveal whether treatment effectiveness varies according to patients' observed covariates. ITEs can be used to stratify health policy decisions according to individual characteristics and potentially achieve greater population health. Little is known about the appropriateness of available ML methods for use in health technology assessment.</p><p><strong>Methods: </strong>In this scoping review, we evaluate ML methods available for estimating ITEs, aiming to help practitioners assess their suitability in health technology assessment. We present a taxonomy of ML approaches, categorized by key challenges in health technology assessment using observational data, including handling time-varying confounding and time-to event data and quantifying uncertainty.</p><p><strong>Results: </strong>We found a wide range of algorithms for simpler settings with baseline confounding and continuous or binary outcomes. Not many ML algorithms can handle time-varying or unobserved confounding, and at the time of writing, no ML algorithm was capable of estimating ITEs for time-to-event outcomes while accounting for time-varying confounding. Many of the ML algorithms that estimate ITEs in longitudinal settings do not formally quantify uncertainty around the point estimates.</p><p><strong>Limitations: </strong>This scoping review may not cover all relevant ML methods and algorithms as they are continuously evolving.</p><p><strong>Conclusions: </strong>Existing ML methods available for ITE estimation are limited in handling important challenges posed by observational data when used for cost-effectiveness analysis, such as time-to-event outcomes, time-varying and hidden confounding, or the need to estimate sampling uncertainty around the estimates.</p><p><strong>Implications: </strong>ML methods are promising but need further development before they can be used to estimate ITEs for health technology assessments.</p><p><strong>Highlights: </strong>Estimating individualized treatment effects (ITEs) using observational data and machine learning (ML) can support personalized treatment advice and help deliver more customized information on the effectiveness and cost-effectiveness of health technologies.ML methods for ITE estimation are mostly designed for handling confounding at baseline but not time-varying or unobserved confounding. The few models that account for time-varying confounding are designed for continuous or binary outcomes, not time-to-event outcomes.Not all ML methods for estimating ITEs can quantify the uncertainty of their predictions.Future work on developing ML that addresses the concerns summarized in this review is needed before these methods can be widely used in clinical and health technology assessment-like decision making.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141762143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Making Drug Approval Decisions in the Face of Uncertainty: Cumulative Evidence versus Value of Information.","authors":"Stijntje W Dijk, Eline Krijkamp, Natalia Kunst, Jeremy A Labrecque, Cary P Gross, Aradhana Pandit, Chia-Ping Lu, Loes E Visser, John B Wong, M G Myriam Hunink","doi":"10.1177/0272989X241255047","DOIUrl":"10.1177/0272989X241255047","url":null,"abstract":"<p><strong>Background: </strong>The COVID-19 pandemic underscored the criticality and complexity of decision making for novel treatment approval and further research. Our study aims to assess potential decision-making methodologies, an evaluation vital for refining future public health crisis responses.</p><p><strong>Methods: </strong>We compared 4 decision-making approaches to drug approval and research: the Food and Drug Administration's policy decisions, cumulative meta-analysis, a prospective value-of-information (VOI) approach (using information available at the time of decision), and a reference standard (retrospective VOI analysis using information available in hindsight). Possible decisions were to reject, accept, provide emergency use authorization, or allow access to new therapies only in research settings. We used monoclonal antibodies provided to hospitalized COVID-19 patients as a case study, examining the evidence from September 2020 to December 2021 and focusing on each method's capacity to optimize health outcomes and resource allocation.</p><p><strong>Results: </strong>Our findings indicate a notable discrepancy between policy decisions and the reference standard retrospective VOI approach with expected losses up to $269 billion USD, suggesting suboptimal resource use during the wait for emergency use authorization. Relying solely on cumulative meta-analysis for decision making results in the largest expected loss, while the policy approach showed a loss up to $16 billion and the prospective VOI approach presented the least loss (up to $2 billion).</p><p><strong>Conclusion: </strong>Our research suggests that incorporating VOI analysis may be particularly useful for research prioritization and treatment implementation decisions during pandemics. While the prospective VOI approach was favored in this case study, further studies should validate the ideal decision-making method across various contexts. This study's findings not only enhance our understanding of decision-making strategies during a health crisis but also provide a potential framework for future pandemic responses.</p><p><strong>Highlights: </strong>This study reviews discrepancies between a reference standard (retrospective VOI, using hindsight information) and 3 conceivable real-time approaches to research-treatment decisions during a pandemic, suggesting suboptimal use of resources.Of all prospective decision-making approaches considered, VOI closely mirrored the reference standard, yielding the least expected value loss across our study timeline.This study illustrates the possible benefit of VOI results and the need for evidence accumulation accompanied by modeling in health technology assessment for emerging therapies.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11283736/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141201040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Thinking Fast, Slow, and Forever: Daniel Kahneman Obituary.","authors":"Donald A Redelmeier","doi":"10.1177/0272989X241256121","DOIUrl":"https://doi.org/10.1177/0272989X241256121","url":null,"abstract":"","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141762147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stability of Willingness to Pay: Does Time and Treatment Allocation in a Randomized Controlled Trial Influence Willingness to Pay?","authors":"Marjon van der Pol, Verity Watson, Dwayne Boyers","doi":"10.1177/0272989X241249654","DOIUrl":"10.1177/0272989X241249654","url":null,"abstract":"<p><strong>Background: </strong>Willingness-to-pay (WTP) estimates are useful to policy makers only if they are generalizable beyond the moment when they are collected. To understand the \"shelf life\" of preference estimates, preference stability needs be tested over substantial periods of time.</p><p><strong>Methods: </strong>We tested the stability of WTP for preventative dental care (scale and polish) using a payment-card contingent valuation question administered to 909 randomized controlled trial participants at 4 time points: baseline (prerandomization) and at annual intervals for 3 years. Trial participants were regular attenders at National Health Service dental practices. Participants were randomly offered different frequencies (intensities) of scale polish (no scale and polish, 1 scale and polish per year, 2 scale and polishes per year). We also examined whether treatment allocation to these different treatment intensities influenced the stability of WTP. Interval regression methods were used to test for changes in WTP over time while controlling for changes in 2 determinants of WTP. Individual-level changes were also examined as well as the WTP function over time.</p><p><strong>Results: </strong>We found that at the aggregate level, mean WTP values were stable over time. The results were similar by trial arm. Individuals allocated to the arm with the highest scale and polish intensity (2 per year) had a slight increase in WTP toward the latter part of the trial. There was considerable variation at the individual level. The WTP function was stable over time.</p><p><strong>Conclusions: </strong>The payment-card contingent valuation method can produce stable WTP values in health over time. Future research should explore the generalizability of these results in other populations, for less familiar health care services, and using alternative elicitation methods.</p><p><strong>Highlights: </strong>Stated preferences are commonly used to value health care.Willingness-to-pay (WTP) estimates are useful only if they have a \"shelf life.\"Little is known about the stability of WTP for health care.We test the stability of WTP for dental care over 3 y.Our results show that the contingent valuation method can produce stable WTP values.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11282685/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140913151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cost-effectiveness Analysis of Colorectal Cancer Screening Strategies Using Active Learning and Monte Carlo Simulation.","authors":"Amirhossein Fouladi, Amin Asadi, Eric A Sherer, Mahboubeh Madadi","doi":"10.1177/0272989X241258224","DOIUrl":"10.1177/0272989X241258224","url":null,"abstract":"<p><strong>Introduction: </strong>Detection of colorectal cancer (CRC) in the early stages through available screening tests increases the patient's survival chances. Multimodal screening policies can benefit patients by providing more diverse screening options and balancing the risks and benefits of screening tests. We investigate the cost-effectiveness of a wide variety of multimodal CRC screening policies.</p><p><strong>Methods: </strong>We developed a Monte Carlo simulation framework to model CRC dynamics. We proposed an innovative calibration process using machine learning models to estimate age- and size-specific adenomatous polyps' progression and regression rates. The proposed approach significantly expedites the model parameter space search.</p><p><strong>Results: </strong>Two multimodal proposed policies (i.e., 1] colonoscopy at 50 y and fecal occult blood test annually between 60 and 75 y and 2] colonoscopy at 50 and 60 y and fecal immunochemical test annually between 70 and 75 y) are identified as efficient frontier policies. Both policies are cost-effective at a willingness to pay of $50,000. Sensitivity analyses were performed to assess the sensitivity of results to a change in screening test costs as well as adherence behavior. The sensitivity analysis results suggest that the proposed policies are mostly robust to the considered changes in screening test costs, as there is a significant overlap between the efficient frontier policies of the baseline and the sensitivity analysis cases. However, the efficient frontier policies were more sensitive to changes in adherence behavior.</p><p><strong>Conclusion: </strong>Generally, combining stool-based tests with visual tests will benefit patients with higher life expectancy and a lower expected cost compared with unimodal screening policies. Colonoscopy at younger ages (when the colonoscopy complication risk is lower) and stool-based tests at older ages are shown to be more effective.</p><p><strong>Highlights: </strong>We propose a detailed Markov model to capture the colorectal cancer (CRC) dynamics. The proposed Markov model presents the detailed dynamics of adenomas progression to CRC.We use more than 44,000 colonoscopy reports and available data in the literature to calibrate the proposed Markov model using an innovative approach that leverages machine learning models to expedite the calibration process.We investigate the cost-effectiveness of a wide variety of multimodal CRC screening policies and compare their performances with the current in-practice policies.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11325561/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141441046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}