Medical Decision Making最新文献

{"title":"Understanding Delayed Diabetes Diagnosis: An Agent-Based Model of Health-Seeking Behavior.","authors":"Firouzeh Rosa Taghikhah, Araz Jabbari, Kevin C Desouza, Arunima Malik, Hadi A Khorshidi","doi":"10.1177/0272989X251326908","DOIUrl":"10.1177/0272989X251326908","url":null,"abstract":"<p><p>BackgroundDiabetes is a rapidly growing global health issue, with the hidden burden of undiagnosed cases leading to severe complications and escalating health care costs.MethodsThis study investigated the potential of integrated behavioral frameworks to predict health-seeking behaviors and improve diabetes diagnosis timelines through the development of an agent-based model. Focusing on Narromine and Gilgandra in New South Wales, Australia, the model captured the integrative influence of 3 social theories-theory of planned behavior (TPB), health belief model (HBM), and goal framing theory (GFT)-on health care decisions across behavioral and nonbehavioral variables, providing a robust analysis of temporal diagnostic patterns, health care utilization, and costs.ResultsOur comparative experiments indicated that this multitheory framework improved predictive accuracy by 15% to 30% compared with single-theory models, effectively capturing the interplay of planned, belief-driven, and context-based health behaviors. Spatial-temporal analysis highlighted key regional and demographic variations in diagnosis behaviors. While early, planned medical visits were prevalent in regions with better access (Gilgandra), areas with limited infrastructure saw a reliance on hospital-based diagnoses (Narromine). Health care cost analysis demonstrated a nonlinear expenditure pattern, suggesting that these theories defy conventional linear cost trends. Scenario analysis demonstrated the impact of targeted interventions. Gender-specific awareness initiatives in Gilgandra reduced late-diagnosis rates among men by approximately 15%, while enhanced access to care in Narromine decreased hospital-based late diagnoses from a baseline of 80% to around 60%.ConclusionsThis study contributes an empirically grounded, policy-oriented decision support tool to inform targeted interventions, offering novel insights to improve diabetes management.HighlightsWe explored the delay in diabetes diagnosis, particularly within remote Australian communities, through looking into the health care-seeking behavior of individuals displaying diabetes symptoms.We developed an innovative agent-based model to craft a dynamic decision support tool for policy makers by providing unique insights into the health behaviors of diabetes patients.Our study contributes significantly to the understanding of public health management with particular concerns around diabetes, as well as equips the New South Wales Ministry of Health with impactful insights into the consequences of their decisions.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"399-425"},"PeriodicalIF":3.1,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11992636/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143781313","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":"Optimizing Face Validity and Clinical Relevance of a Mathematical Population Cancer Epidemiology Model Using a Novel Advisory Group Approach.","authors":"Louise Davies, Sara Fernandes-Taylor, Natalia Arroyo, Yichi Zhang, Oguzhan Alagoz, David O Francis","doi":"10.1177/0272989X251327595","DOIUrl":"10.1177/0272989X251327595","url":null,"abstract":"<p><p>BackgroundCancer simulation models can answer research and policy questions when prospective evidence is incomplete or not feasible. However, such models require incorporating unmeasureable inputs for which there is often not strong evidence, and model utility is limited if assumptions lack face validity or if the model is not clinically relevant. We systematically incorporated formal advisory input to mitigate these challenges as we developed a microsimulation model of papillary thyroid cancer (PApillary Thyroid CArcinoma Microsimulation model [PATCAM]).MethodsWe used a participatory action research approach incorporating focus group techniques and using principles of bidirectional learning.ResultsWe assembled a formal standing advisory group with representation by perspective (medical, patient, and payor), geography, and local practice culture to understand current and historical clinical beliefs and practices about thyroid cancer diagnosis and treatment. The group provided input on critical modeling assumptions and decisions: 1) the role of nodule size in biopsy decisions, 2) trends in provider biopsy behavior, 3) specialty propensity to biopsy, 4) population prevalence of thyroid cancer over time, 5) proportion of malignant tumors showing regression, and 6) cancer epidemiology and diagnostic practices by sex and age. Advisory group questions and concerns about model development will inform future research questions and strategies to communicate and disseminate model results.ConclusionsWe successfully used our advisory group to provide critical inputs on unmeasurable assumptions, increasing the face validity of our model. The use of a standing advisory group improved model transparency and contributed to future research plans and dissemination of model results so they can have maximum impact when guiding clinical decisions and policy.HighlightsUnfamiliarity with simulation modeling poses a threat to its acceptability and adoption. The effectiveness of these models is contingent on end-users' willingness to accept and adopt model results. The effectiveness of the models is further limited if they lack face validity to potential users or do not have clinical relevance.Several approaches to overcoming validity challenges have been advanced, such as collaborative modeling, which involves developing multiple models independently using common data sources. However, when only a single model exists, another approach is needed. We used an Advisory Group and \"participatory modeling,\" which has been used in other settings but has not been previously reported in cancer modeling. We describe the methods used for and results of incorporating a formal advisory group into the development of a cancer microsimulation model.The use of a formal, standing advisory group (as opposed to one-off focus groups or interviews) strengthened our model by rigorously vetting modeling assumptions and model inputs with subject matter experts. The formal, ongoing structur","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"385-398"},"PeriodicalIF":3.1,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12120966/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143755555","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":"Methodological Approaches for Incorporating Marginalized Populations into HPV Vaccine Modeling: A Systematic Review.","authors":"Jennifer C Spencer, Juan Yanguela, Lisa P Spees, Olufeyisayo O Odebunmi, Anna A Ilyasova, Caitlin B Biddell, Kristen Hassmiller Lich, Sarah D Mills, Colleen R Higgins, Sachiko Ozawa, Stephanie B Wheeler","doi":"10.1177/0272989X251325509","DOIUrl":"10.1177/0272989X251325509","url":null,"abstract":"<p><p><b>Background.</b> Delineation of historically marginalized populations in decision models can identify strategies to improve equity but requires assumptions in both model structure and stratification of input data. <b>Purpose.</b> We sought to characterize alternative methodological approaches for incorporating marginalized populations into human papillomavirus (HPV) vaccine decision-support models. <b>Data Sources.</b> We conducted a systematic search of PubMed, CINAHL, Scopus, and Embase from January 2006 through June 2022. <b>Study Selection.</b> We identified simulation models of HPV vaccination that refine any model input to specifically reflect a marginalized population. <b>Data Extraction.</b> We extracted data on key methodological decisions across modeling approaches to incorporate marginalized populations, including stratification of inputs, model structure, attribution of prevaccine disparities, calibration, validation, and sensitivity analyses. <b>Data Synthesis.</b> We identified 30 models that stratified inputs by sexual behavior (i.e., men who have sex with men), HIV infection status, race, ethnicity, income, rurality, or combinations of these. We identified 5 common approaches used to incorporate marginalized groups. These included models based primarily on differences in sexual behavior (k = 6), HPV cancer incidence (k = 10), cancer screening and care access (k = 4), and HPV natural history (through either direct incorporation of data [k = 10] or calibration [k = 5]). Few models evaluated sensitivity around their conceptualization of the marginalized group, and only 5 models validated outcomes for the marginalized group. <b>Limitations.</b> Evaluated studies reflected a variety of settings and research questions, making it difficult to evaluate the implications of differences across modeling approaches. <b>Conclusions.</b> Modelers should be explicit about the assumptions and theory driving their model structure and input parameters specific to key marginalized populations, such as the causes of prevaccination differences in outcomes. More emphasis is needed on model validation and rigorous sensitivity analysis.HighlightsWe identified 30 unique HPV vaccination models that incorporated marginalized populations, including populations living with HIV, low-income or rural populations, and individuals of a marginalized race, ethnicity, or sexual behavior.Methods for incorporating these populations, as well as the assumptions inherent in the modeling structure and parameter selections, varied substantially, with models explicitly or implicitly attributing prevaccine differences to alternative combinations of biological, behavioral, and societal mechanisms.Modelers seeking to incorporate marginalized populations should be transparent about assumptions underlying model structure and data and examine these assumptions in sensitivity analysis when possible.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"358-369"},"PeriodicalIF":3.1,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11992634/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143634897","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":"Segmenting the Population and Estimating Transition Probabilities Using Data on Health and Health-Related Social Service Needs from the US Health and Retirement Study.","authors":"Lize Duminy","doi":"10.1177/0272989X251320887","DOIUrl":"10.1177/0272989X251320887","url":null,"abstract":"<p><p>BackgroundSimulation modeling is a promising tool to help policy makers and providers make evidence-based decisions when evaluating integrated care programs. The functionality of such models, however, depends on 2 prerequisites: 1) the analytical segmentation of populations to capture both health and health-related social service (HASS) needs and 2) the precise estimation of transition probabilities among the various states of need.MethodsWe took a validated instrument for segmenting the population by HASS needs and adapted it to the Health and Retirement Study, a nationally representative survey dataset from the US population older than 50 y. We then estimated the transition probabilities across all 10 need states and death using multistate modeling. A need state was defined as a combination of any of the 5 ordinal global impression segments and a complicating factor status.ResultsKaplan-Meier survival curves, log-rank tests, and c-indices were used to assess predictive validity in relation to mortality. The Markov traces, using the estimated transition probability to replicate 2 closed cohorts, resembled the proportion of individuals per health state across subsequent waves well enough to indicate adequate fit of the estimated transition probabilities.ConclusionsThis article provides a population segmentation approach that incorporates HASS needs for the US population and 1-y transition probabilities across HASS need states and death. This is the first application of HASS segmentation that can estimate transitions between all 10 HASS need states, facilitating novel analysis of policy decisions related to integrated care.ImplicationsOur results will be used as input for a simulation model that performs scenario analysis on the long-term effects of various integrated care policies on population health.HighlightsWe took a validated tool for segmenting the population according to health and health-related social service (HASS) needs and adapted it to the Health and Retirement Study, a nationally representative survey dataset from the US population over the age of 50 y.We estimated the 1-y transition probabilities across all 10 HASS segments and death.This is the first application of a version of this HASS segmentation tool that includes HASSs in the various need states when estimating transition probabilities.Our results will be used as input for a simulation model that performs scenario analysis on the long-term effects of various integrated care policies on population health.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"286-301"},"PeriodicalIF":3.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143484476","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":"Health Utilities in People with Hepatitis C Virus Infection: A Study Using Real-World Population-Level Data.","authors":"Yasmin A Saeed, Nicholas Mitsakakis, Jordan J Feld, Murray D Krahn, Jeffrey C Kwong, William W L Wong","doi":"10.1177/0272989X251319342","DOIUrl":"10.1177/0272989X251319342","url":null,"abstract":"<p><p>BackgroundHepatitis C virus (HCV) infection is associated with reduced quality of life and health utility. It is unclear whether this is primarily due to HCV infection itself or commonly co-occurring patient characteristics such as low income and mental health issues. This study aims to estimate and separate the effects of HCV infection on health utility from the effects of clinical and sociodemographic factors using real-world population-level data.MethodsWe conducted a cross-sectional retrospective cohort study to estimate health utilities in people with and without HCV infection in Ontario, Canada, from 2000 to 2014 using linked survey data from the Canadian Community Health Survey and health administrative data. Utilities were derived from the Health Utilities Index Mark 3 instrument. We used propensity score matching and multivariable linear regression to examine the impact of HCV infection on utility scores while adjusting for clinical and sociodemographic factors.ResultsThere were 7,102 individuals with hepatitis C status and health utility data available (506 HCV-positive, 6,596 HCV-negative). Factors associated with marginalization were more prevalent in the HCV-positive cohort (e.g., household income <$20,000: 36% versus 15%). Propensity score matching resulted in 454 matched pairs of HCV-positive and HCV-negative individuals. HCV-positive individuals had substantially lower unadjusted utilities than HCV-negative individuals did (mean ± standard error: 0.662 ± 0.016 versus 0.734 ± 0.015). The regression model showed that HCV positivity (coefficient: -0.066), age, comorbidity, mental health history, and household income had large impacts on health utility.ConclusionsHCV infection is associated with low health utility even after controlling for clinical and sociodemographic variables. Individuals with HCV infection may benefit from additional social services and supports alongside antiviral therapy to improve their quality of life.HighlightsHepatitis C virus (HCV) infection is associated with reduced quality of life and health utility. There is debate in the literature on whether this is primarily due to HCV infection itself or commonly co-occurring patient characteristics such as low income and mental health issues.We showed that individuals with HCV infection have substantially lower health utilities than uninfected individuals do even after controlling for clinical and sociodemographic variables, based on a large, real-world population-level dataset. Socioeconomically marginalized individuals with HCV infection had particularly low health utilities.In addition to improving access to HCV testing and treatment, it may be beneficial to provide social services such as mental health and financial supports to improve the quality of life and health utility of people living with HCV.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"332-343"},"PeriodicalIF":3.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11894892/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143476923","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":"Directed Acyclic Graphs in Decision-Analytic Modeling: Bridging Causal Inference and Effective Model Design in Medical Decision Making.","authors":"Stijntje W Dijk, Maurice Korf, Jeremy A Labrecque, Ankur Pandya, Bart S Ferket, Lára R Hallsson, John B Wong, Uwe Siebert, M G Myriam Hunink","doi":"10.1177/0272989X241310898","DOIUrl":"10.1177/0272989X241310898","url":null,"abstract":"<p><p>Decision-analytic models (DAMs) are essentially informative yet complex tools for solving questions in medical decision making. When their complexity grows, the need for causal inference techniques becomes evident as causal relationships between variables become unclear. In this methodological commentary, we argue that graphical representations of assumptions on such relationships, directed acyclic graphs (DAGs), can enhance the transparency of decision models and aid in parameter selection and estimation through visually specifying backdoor paths (i.e., potential biases in parameter estimates) and visually clarifying structural modeling choices of frontdoor paths (i.e., the effect of the model structure on the outcome). This commentary discusses the benefit of integrating DAGs and DAMs in medical decision making and in particular health economics with 2 applications: the first examines statin use for prevention of cardiovascular disease, and the second considers mindfulness-based interventions for students' stress. Despite the potential application of DAGs in the decision science framework, challenges remain, including simplicity, defining the scope of a DAG, unmeasured confounding, noncausal aspects, and limited data availability or quality. Broader adoption of DAGs in decision science requires full-model applications and further debate.HighlightsOur commentary proposes the application of directed acyclic graphs (DAGs) in the design of decision-analytic models, offering researchers a valuable and structured tool to enhance transparency and accuracy by bridging the gap between causal inference and model design in medical decision making.The practical examples in this article showcase the transformative effect DAGs can have on model structure, parameter selection, and the resulting conclusions on effectiveness and cost-effectiveness.This methodological article invites a broader conversation on decision-modeling choices grounded in causal assumptions.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"223-231"},"PeriodicalIF":3.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11894903/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143025164","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":"Recalibrating an Established Microsimulation Model to Capture Trends and Projections of Colorectal Cancer Incidence and Mortality.","authors":"Jie-Bin Lew, Qingwei Luo, Joachim Worthington, Han Ge, Emily He, Julia Steinberg, Michael Caruana, Dianne L O'Connell, Eleonora Feletto, Karen Canfell","doi":"10.1177/0272989X251314050","DOIUrl":"10.1177/0272989X251314050","url":null,"abstract":"<p><p>BackgroundChanging colorectal cancer (CRC) incidence rates, including recent increases for people younger than 50 y, need to be considered in planning for future cancer control and screening initiatives. Reliable estimates of the impact of changing CRC trends on the National Bowel Cancer Screening Program (NBCSP) are essential for programmatic planning in Australia. An existing microsimulation model of CRC, <i>Policy1-Bowel</i>, was updated to reproduce Australian CRC trends data and provide updated projections of CRC- and screening-related outcomes to inform clinical practice guidelines for the prevention of CRC.Methods<i>Policy1-Bowel</i> was recalibrated to reproduce statistical age-period-cohort model trends and projections of CRC incidence for 1995-2045 in the absence of the NBCSP as well as published data on CRC incidence trends, stage distribution, and survival in 1995-2020 in Australia. The recalibrated <i>Policy1-Bowel</i> predictions were validated by comparison with published Australian CRC mortality trends for 1995-2015 and statistical projections to 2040. Metamodels were developed to aid the calibration process and significantly reduce the computational burden.Results<i>Policy1-Bowel</i> was recalibrated, and best-fit parameter sets were identified for lesion incidence, CRC stage progression rates, detection rates, and survival rates by age, sex, bowel location, cancer stage, and birth year. The recalibrated model was validated and successfully reproduced observed CRC mortality rates for 1995-2015 and statistical projections for 2016-2030.ConclusionThe recalibrated <i>Policy1-Bowel</i> model captures significant additional detail on the future incidence and mortality burden of CRC in Australia. This is particularly relevant as younger cohorts with higher CRC incidence rates approach screening ages to inform decision making for these groups. The metamodeling approach allows fast recalibration and makes regular updates to incorporate new evidence feasible.HighlightsIn Australia, colorectal cancer incidence rates are increasing for people younger than 50 y but decreasing for people older than 50 y, and colorectal cancer survival is improving as new treatment technologies emerge.To evaluate the future health and economic impact of screening and inform policy, modeling must include detailed trends and projections of colorectal cancer incidence, mortality, and diagnosis stage.We used novel techniques including integrative age-period cohort projections and metamodel calibration to update <i>Policy1-Bowel</i>, a detailed microsimulation of colorectal cancer and screening in Australia.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"257-275"},"PeriodicalIF":3.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143366553","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":"Development of a Microsimulation Model to Project the Future Prevalence of Childhood Cancer in Ontario, Canada.","authors":"Alexandra Moskalewicz, Sumit Gupta, Paul C Nathan, Petros Pechlivanoglou","doi":"10.1177/0272989X251314031","DOIUrl":"10.1177/0272989X251314031","url":null,"abstract":"<p><p>BackgroundEstimates of the future prevalence of childhood cancer are informative for health system planning but are underutilized. We describe the development of a pediatric oncology microsimulation model for prevalence (POSIM-Prev) and illustrate its application to produce projections of incidence, survival, and limited-duration prevalence of childhood cancer in Ontario, Canada, until 2040.MethodsPOSIM-Prev is a population-based, open-cohort, discrete-time microsimulation model. The model population was updated annually from 1970 to 2040 to account for births, deaths, net migration, and incident cases of childhood cancer. Prevalent individuals were followed until death, emigration, or the last year of simulation. Median population-based outcomes with 95% credible intervals (CrIs) were generated using Monte Carlo simulation. The methodology to derive model inputs included generalized additive modeling of cancer incidence, parametric survival modeling, and stochastic population forecasting. Individual-level data from provincial cancer registries for years 1970 to 2019 informed cancer-related model inputs and internal validation.ResultsThe number of children (aged 0-14 y) diagnosed with cancer in Ontario is projected to rise from 414 (95% CrI: 353-486) in 2020 to 561 (95% CrI: 481-653) in 2039. The 5-y overall survival rate for 2030-2034 is estimated to reach 90% (95% CrI: 88%-92%). By 2040, 24,088 (95% CrI: 22,764-25,648) individuals with a history of childhood cancer (diagnosed in Ontario or elsewhere) are projected to reside in the province. The model accurately reproduced historical trends in incidence, survival, and prevalence when validated.ConclusionsThe rising incidence and prevalence of childhood cancer will create increased demand for both acute cancer care and long-term follow-up services in Ontario. The POSIM-Prev model can be used to support long-range health system planning and future health technology assessments in jurisdictions that have access to similar model inputs.HighlightsThis article describes the development of a population-based, discrete-time microsimulation model that can simulate incident and prevalent cases of childhood cancer in Ontario, Canada, until 2040.Use of an open cohort framework allowed for estimation of the potential impact of net migration on childhood cancer prevalence.In addition to supporting long-term health system planning, this model can be used in future health technology assessments, by providing a demographic profile of incident and prevalent cases for model conceptualization and budget impact purposes.This modeling framework is adaptable to other jurisdictions and disease areas where individual-level data for incidence and survival are available.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"245-256"},"PeriodicalIF":3.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143191036","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":"Changing Time Representation in Microsimulation Models.","authors":"Eric Kai-Chung Wong, Wanrudee Isaranuwatchai, Joanna E M Sale, Andrea C Tricco, Sharon E Straus, David M J Naimark","doi":"10.1177/0272989X251319808","DOIUrl":"10.1177/0272989X251319808","url":null,"abstract":"<p><p>BackgroundIn microsimulation models of diseases with an early, acute phase requiring short cycle lengths followed by a chronic phase, fixed short cycles may lead to computational inefficiency. Examples include epidemic or resource constraint models with early short cycles where long-term economic consequences are of interest for individuals surviving the epidemic or ultimately obtaining the resource. In this article, we demonstrate methods to improve efficiency in such scenarios. Furthermore, we show that care must be taken when applying these methods to epidemic or resource constraint models to avoid bias.MethodsTo demonstrate efficiency, we compared the model runtime among 3 versions of a microsimulation model: with short fixed cycles for all states (FCL), with dynamic cycle length (DCL) defined locally for each state, and with DCL features plus a discrete-event-like hybrid component. To demonstrate bias mitigation, we compared discounted lifetime costs for 3 versions of a resource constraint model: with a fixed horizon where simulation stops, with a fixed entry horizon beyond which new individuals could not enter the model, and with a fixed entry horizon plus a mechanism to maintain a constant level of competition for the resource after the horizon.ResultsThe 3 versions of the microsimulation model had average runtimes of 515 (95% credible interval [CI]: 477 to 545; FCL), 2.70 (95% CI: 1.48 to 2.92; DCL), and 1.45 (95% CI: 1.26 to 2.61; DCL-pseudo discrete event simulation) seconds, respectively. The first 2 resource constraint versions underestimated costs relative to the constant competition version: $20,055 (95% CI: $19,000 to $21,120), $27,030 (95% CI: $24,680 to $29,412), and $33,424 (95% CI: $27,510 to $44,484), respectively.LimitationsThe magnitude of improvements in efficiency and reduction in bias may be model specific.ConclusionChanging time representation in microsimulation may offer computational advantages.HighlightsShort cycle lengths may be required to model the acute phase of an illness but lead to computational inefficiency in a subsequent chronic phase in microsimulation models.A solution is to create state-specific cycle lengths so that cycle lengths change dynamically as the simulation progresses.Computational efficiency can be enhanced further by using a hybrid model containing discrete-event-simulation-like features.Hybrid models can efficiently handle events subsequent to exit from an epidemic or resource constraint model provided steps are taken to mitigate potential bias.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"276-285"},"PeriodicalIF":3.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11894904/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143494530","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":"Development of a Decision Model to Estimate the Outcomes of Treatment Sequences in Advanced Melanoma.","authors":"Saskia de Groot, Hedwig M Blommestein, Brenda Leeneman, Carin A Uyl-de Groot, John B A G Haanen, Michel W J M Wouters, Maureen J B Aarts, Franchette W P J van den Berkmortel, Willeke A M Blokx, Marye J Boers-Sonderen, Alfons J M van den Eertwegh, Jan Willem B de Groot, Geke A P Hospers, Ellen Kapiteijn, Olivier J van Not, Astrid A M van der Veldt, Karijn P M Suijkerbuijk, Pieter H M van Baal","doi":"10.1177/0272989X251319338","DOIUrl":"10.1177/0272989X251319338","url":null,"abstract":"<p><p>BackgroundA decision model for patients with advanced melanoma to estimate outcomes of a wide range of treatment sequences is lacking.ObjectivesTo develop a decision model for advanced melanoma to estimate outcomes of treatment sequences in clinical practice with the aim of supporting decision making. The article focuses on methodology and long-term health benefits.MethodsA semi-Markov model with a lifetime horizon was developed. Transitions describing disease progression, time to next treatment, and mortality were estimated from real-world data (RWD) as a function of time since starting treatment or disease progression and patient characteristics. Transitions were estimated separately for melanoma with and without a BRAF mutation and for patients with favorable and intermediate prognostic factors. All transitions can be adjusted using relative effectiveness of treatments derived from a network meta-analysis of randomized controlled trials (RCTs). The duration of treatment effect can be adjusted to obtain outcomes under different assumptions.ResultsThe model distinguishes 3 lines of systemic treatment for melanoma with a BRAF mutation and 2 lines of systemic treatment for melanoma without a BRAF mutation. Life expectancy ranged from 7.8 to 12.0 years in patients with favorable prognostic factors and from 5.1 to 8.7 years in patients with intermediate prognostic factors when treated with sequences consisting of targeted therapies and immunotherapies. Scenario analyses illustrate how estimates of life expectancy depend on the duration of treatment effect.ConclusionThe model is flexible because it can accommodate different treatments and treatment sequences, and the duration of treatment effects and the transitions influenced by treatment can be adjusted. We show how using RWD and data from RCTs can harness advantages of both data sources, guiding the development of future decision models.HighlightsThe model is flexible because it can accommodate different treatments and treatment sequences, and the duration of treatment effects as well as the transitions that are influenced by treatment can be adjusted.The long-term health benefits of treatment sequences depend on the place of different therapies within a treatment sequence.Assumptions about the duration of relative treatment effects influence the estimates of long-term health benefits.We show how the use of real-world data and data from randomized controlled trials harness the advantages of both data sources, guiding the development of future decision models.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"302-317"},"PeriodicalIF":3.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11894896/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143476920","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}