Medical Decision Making最新文献

{"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":"https://doi.org/10.1177/0272989X251319808","url":null,"abstract":"<p><strong>Background: </strong>In 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.</p><p><strong>Methods: </strong>To 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.</p><p><strong>Results: </strong>The 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.</p><p><strong>Limitations: </strong>The magnitude of improvements in efficiency and reduction in bias may be model specific.</p><p><strong>Conclusion: </strong>Changing time representation in microsimulation may offer computational advantages.</p><p><strong>Highlights: </strong>Short 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":"272989X251319808"},"PeriodicalIF":3.1,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","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":"","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":"https://doi.org/10.1177/0272989X251320887","url":null,"abstract":"<p><strong>Background: </strong>Simulation 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.</p><p><strong>Methods: </strong>We 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.</p><p><strong>Results: </strong>Kaplan-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.</p><p><strong>Conclusions: </strong>This 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.</p><p><strong>Implications: </strong>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><p><strong>Highlights: </strong>We 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":"272989X251320887"},"PeriodicalIF":3.1,"publicationDate":"2025-02-24","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":"Leveling up: Treating Uptake as Endogenous May Increase the Value of Screening Programs.","authors":"Jose A Robles-Zurita, Neil Hawkins, Janet Bouttell","doi":"10.1177/0272989X251319794","DOIUrl":"https://doi.org/10.1177/0272989X251319794","url":null,"abstract":"<p><strong>Background: </strong>We aimed to illustrate that health economists should consider individual heterogeneity when solving the problem of finding the optimal combination of sensitivity and specificity that maximizes the average health utility of a target population in a screening program.</p><p><strong>Methods: </strong>A theoretical framework compares the solution under standard economics of diagnoses to the optimal combination under an endogenous uptake analysis, where screening participation is given by heterogenous health preferences. An applied example used calibrated parameters with real data from the bowel cancer screening program in the United Kingdom. Scenario analyses show how the results would change with parameter values, if disease risk and health utilities were not independent and if screening uptake were not completely determined by health preferences.</p><p><strong>Results: </strong>A general theoretical result states that the endogenous uptake analysis leads to a weakly higher true- and false-positive rate than would be optimal under the standard approach. In the same way, the endogenous solution would lead to a lower uptake rate. The base-case scenario of the applied example illustrates that a screening program using the endogenous solution would generate 21.1% more quality-adjusted life-years than when using the standard solution. The scenario analyses show when the endogenous analysis is most valued and that the general result applies for a wide range of situations when theoretical assumptions are relaxed.</p><p><strong>Limitations: </strong>The results obtained are valid under the assumptions made. Analysts should evaluate if those could hold in the applied screening context.</p><p><strong>Conclusions: </strong>Individual heterogeneity and uptake decisions are relevant factors to consider in the problem of finding an optimal combination of sensitivity and specificity for a screening test.</p><p><strong>Highlights: </strong>The value of screening programs can be higher if heterogeneity of preferences in the target population is considered.The optimal operation of a screening test depends on health utilities of the target population and on the heterogeneity of these health utilities.Under heterogeneity of health utilities, the optimal operation of a screening test does not maximize screening uptake.A general theoretical result states that the endogenous uptake analysis leads to a weakly higher true- and false-positive rate than would be optimal under a standard approach; this is true for a wide range of situations.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"272989X251319794"},"PeriodicalIF":3.1,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143484436","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":"https://doi.org/10.1177/0272989X251319342","url":null,"abstract":"<p><strong>Background: </strong>Hepatitis 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.</p><p><strong>Methods: </strong>We 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.</p><p><strong>Results: </strong>There 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.</p><p><strong>Conclusions: </strong>HCV 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.</p><p><strong>Highlights: </strong>Hepatitis 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":"272989X251319342"},"PeriodicalIF":3.1,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","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":"","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":"https://doi.org/10.1177/0272989X251319338","url":null,"abstract":"<p><strong>Background: </strong>A decision model for patients with advanced melanoma to estimate outcomes of a wide range of treatment sequences is lacking.</p><p><strong>Objectives: </strong>To 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.</p><p><strong>Methods: </strong>A 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.</p><p><strong>Results: </strong>The 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.</p><p><strong>Conclusion: </strong>The 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.</p><p><strong>Highlights: </strong>The 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":"272989X251319338"},"PeriodicalIF":3.1,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","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":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Effect of Patient Decision Aid Attributes on Patient Outcomes: A Network Meta-Analysis of a Systematic Review.","authors":"Dawn Stacey, Meg Carley, Janet Gunderson, Shu-Ching Hsieh, Shannon E Kelly, Krystina B Lewis, Maureen Smith, Robert J Volk, George Wells","doi":"10.1177/0272989X251318640","DOIUrl":"https://doi.org/10.1177/0272989X251318640","url":null,"abstract":"<p><strong>Background: </strong>Patient decision aids (PtDAs) are effective interventions to help people participate in health care decisions. Although there are quality standards, PtDAs are complex interventions with variability in their attributes.</p><p><strong>Purpose: </strong>To determine and compare the effects of PtDA attributes (e.g., content elements, delivery timing, development) on primary outcomes for adults facing health care decisions.</p><p><strong>Data sources: </strong>A systematic review of randomized controlled trials (RCTs) comparing PtDAs to usual care.</p><p><strong>Study selection: </strong>Eligible RCTs measured at least 1 primary outcome: informed values choice, knowledge, accurate risk perception, decisional conflict subscales, and undecided.</p><p><strong>Data analysis: </strong>A network meta-analysis evaluated direct and indirect effects of PtDA attributes on primary outcomes.</p><p><strong>Data synthesis: </strong>Of 209 RCTs, 149 reported eligible outcomes. There was no difference in outcomes for PtDAs using implicit compared with explicit values clarification. Compared with PtDAs with probabilities, PtDAs without probabilities were associated with poorer patient knowledge (mean difference [MD] -3.86; 95% credible interval [CrI] -7.67, -0.03); there were no difference for other outcomes. There was no difference in outcomes when PtDAs presented information in ways that decrease cognitive demand and mixed results when PtDAs used strategies to enhance communication. Compared with PtDAs delivered in preparation for consultations, PtDAs used during consultations were associated with poorer knowledge (MD -4.34; 95% CrI -7.24, -1.43) and patients feeling more uninformed (MD 5.07; 95% CrI 1.06, 9.11). Involving patients in PtDA development was associated with greater knowledge (MD 6.56; 95% CrI 1.10, 12.03) compared with involving health care professionals alone.</p><p><strong>Limitations: </strong>There were no direct comparisons between PtDAs with/without attributes.</p><p><strong>Conclusions: </strong>Improvements in knowledge were influenced by some PtDA content elements, using PtDA content before the consultation, and involving patients in development. There were few or no differences on other outcomes.</p><p><strong>Highlights: </strong>This is the first known network meta-analysis conducted to determine the contributions of the different attributes of patient decision aids (PtDAs) on patient outcomes.There was no difference in outcomes when PtDAs used implicit compared with explicit values clarification.There were greater improvements in knowledge when PtDAs included information on probabilities, PtDAs were used in preparation for the consultation or development included patients on the research team.There was no difference in outcomes when PtDAs presented information in ways that decrease cognitive demand and mixed results when PtDAs used strategies to enhance communication.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"272989X251318640"},"PeriodicalIF":3.1,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143450735","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":"Expected Value of Sample Information Calculations for Risk Prediction Model Validation.","authors":"Mohsen Sadatsafavi, Andrew J Vickers, Tae Yoon Lee, Paul Gustafson, Laure Wynants","doi":"10.1177/0272989X251314010","DOIUrl":"https://doi.org/10.1177/0272989X251314010","url":null,"abstract":"<p><strong>Background: </strong>The purpose of external validation of a risk prediction model is to evaluate its performance before recommending it for use in a new population. Sample size calculations for such validation studies are currently based on classical inferential statistics around metrics of discrimination, calibration, and net benefit (NB). For NB as a measure of clinical utility, the relevance of inferential statistics is doubtful. Value-of-information methodology enables quantifying the value of collecting validation data in terms of expected gain in clinical utility.</p><p><strong>Methods: </strong>We define the validation expected value of sample information (EVSI) as the expected gain in NB by procuring a validation sample of a given size. We propose 3 algorithms for EVSI computation and compare their face validity and computation time in simulation studies. In a case study, we use the non-US subset of a clinical trial to create a risk prediction model for short-term mortality after myocardial infarction and calculate validation EVSI at a range of sample sizes for the US population.</p><p><strong>Results: </strong>Computation methods generated similar EVSI values in simulation studies, although they differed in numerical accuracy and computation times. At 2% risk threshold, procuring 1,000 observations for external validation, had an EVSI of 0.00101 in true-positive units or 0.04938 in false-positive units. Scaled by heart attack incidence in the United States, the population EVSI was 806 in true positives gained, or 39,500 in false positives averted, annually. Validation studies with >4,000 observations had diminishing returns, as the EVSIs were approaching their maximum possible value.</p><p><strong>Conclusion: </strong>Value-of-information methodology quantifies the return on investment from conducting an external validation study and can provide a value-based perspective when designing such studies.</p><p><strong>Highlights: </strong>In external validation studies of risk prediction models, the finite size of the validation sample leads to uncertain conclusions about the performance of the model. This uncertainty has hitherto been approached from a classical inferential perspective (e.g., confidence interval around the c-statistic).Correspondingly, sample size calculations for validation studies have been based on classical inferential statistics. For measures of clinical utility such as net benefit, the relevance of this approach is doubtful.This article defines the expected value of sample information (EVSI) for model validation and suggests algorithms for its computation. Validation EVSI quantifies the return on investment from conducting a validation study.Value-based approaches rooted in decision theory can complement contemporary study design and sample size calculation methods in predictive analytics.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"272989X251314010"},"PeriodicalIF":3.1,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143442599","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":"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":"https://doi.org/10.1177/0272989X251314050","url":null,"abstract":"<p><strong>Background: </strong>Changing 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.</p><p><strong>Methods: </strong><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.</p><p><strong>Results: </strong><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.</p><p><strong>Conclusion: </strong>The 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.</p><p><strong>Highlights: </strong>In 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":"272989X251314050"},"PeriodicalIF":3.1,"publicationDate":"2025-02-06","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":"https://doi.org/10.1177/0272989X251314031","url":null,"abstract":"<p><strong>Background: </strong>Estimates 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.</p><p><strong>Methods: </strong>POSIM-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.</p><p><strong>Results: </strong>The 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.</p><p><strong>Conclusions: </strong>The 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.</p><p><strong>Highlights: </strong>This 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":"272989X251314031"},"PeriodicalIF":3.1,"publicationDate":"2025-02-04","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":"Changes in Risk Tolerance for Ovarian Cancer Prevention Strategies during the COVID-19 Pandemic: Results of a Discrete Choice Experiment.","authors":"Brian L Egleston, Mary B Daly, Kaitlyn Lew, Lisa Bealin, Alexander D Husband, Jill E Stopfer, Pawel Przybysz, Olga Tchuvatkina, Yu-Ning Wong, Judy E Garber, Timothy R Rebbeck","doi":"10.1177/0272989X241302829","DOIUrl":"10.1177/0272989X241302829","url":null,"abstract":"<p><strong>Background: </strong>Prior to COVID-19, little was known about how risks associated with such a pandemic would compete with and influence patient decision making regarding cancer risk reducing medical decision making. We investigated how the pandemic affected preferences for medical risk-reducing strategies among women at elevated risk of breast or ovarian cancer.</p><p><strong>Methods: </strong>We conducted a discrete choice experiment. Women about to undergo genetic testing and counseling at 2 medical centers participated. Enrollment occurred between 2019 and 2022, allowing us to investigate changes in preferences from before the pandemic to after the pandemic. Women chose from permuted scenarios that specified type of surgery, age of menopause, quality of menopausal symptoms, and risk of ovarian cancer, heart disease, or osteoporosis.</p><p><strong>Results: </strong>A total of 355 women, with a median age of 36 y, participated. In 2019, women were less likely to choose prevention scenarios with higher ovarian cancer risk (odds ratio [OR] = 0.42 per 10-point increase in risk, 95% confidence interval [CI] 0.22-0.61). In June 2020, the effect of higher ovarian cancer risk scenarios on choice was attenuated (OR = 0.86, 95% CI 0.68-1.04), with the effect becoming more salient again by July 2021 (OR = 0.59, 95% CI 0.52-0.67) (<i>P</i> = 0.039 for test of temporal interaction). No other attribute demonstrated a temporal trend.</p><p><strong>Conclusion: </strong>The risks associated with the COVID-19 pandemic may have attenuated the impact of risk of ovarian cancer on choice of risk-reducing prevention strategies for ovarian cancer. The maximum attenuation occurred at the beginning of the pandemic when access to risk-reducing surgery was most restricted. Our findings highlight how individuals evaluate competing health risks and adjust their uptake of cancer prevention strategies when faced with a future pandemic or similar global crisis.</p><p><strong>Highlights: </strong>In this discrete choice experiment, women were much less likely to choose prevention scenarios that had higher ovarian cancer risk prior to the COVID-19 pandemic than after the pandemic.The attenuation of preferences may have persisted through 2022.COVID-19 may have altered the relative importance of factors that motivate women to undergo risk-reducing surgeries.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"168-176"},"PeriodicalIF":3.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142899970","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}