Medical Decision Making最新文献

{"title":"Feedback Loop Failure Modes in Medical Diagnosis: How Biases Can Emerge and Be Reinforced.","authors":"Rachael C Aikens, Jonathan H Chen, Michael Baiocchi, Julia F Simard","doi":"10.1177/0272989X241248612","DOIUrl":"10.1177/0272989X241248612","url":null,"abstract":"<p><strong>Background: </strong>Medical diagnosis in practice connects to research through continuous feedback loops: Studies of diagnosed cases shape our understanding of disease, which shapes future diagnostic practice. Without accounting for an imperfect and complex diagnostic process in which some cases are more likely to be diagnosed correctly (or diagnosed at all), the feedback loop can inadvertently exacerbate future diagnostic errors and biases.</p><p><strong>Framework: </strong>A feedback loop failure occurs if misleading evidence about disease etiology encourages systematic errors that self-perpetuate, compromising future diagnoses and patient care. This article defines scenarios for feedback loop failure in medical diagnosis.</p><p><strong>Design: </strong>Through simulated cases, we characterize how disease incidence, presentation, and risk factors can be misunderstood when observational data are summarized naive to biases arising from diagnostic error. A fourth simulation extends to a progressive disease.</p><p><strong>Results: </strong>When severe cases of a disease are diagnosed more readily, less severe cases go undiagnosed, increasingly leading to underestimation of the prevalence and heterogeneity of the disease presentation. Observed differences in incidence and symptoms between demographic groups may be driven by differences in risk, presentation, the diagnostic process itself, or a combination of these. We suggested how perceptions about risk factors and representativeness may drive the likelihood of diagnosis. Differing diagnosis rates between patient groups can feed back to increasingly greater diagnostic errors and disparities in the timing of diagnosis and treatment.</p><p><strong>Conclusions: </strong>A feedback loop between past data and future medical practice may seem obviously beneficial. However, under plausible scenarios, poorly implemented feedback loops can degrade care. Direct summaries from observational data based on diagnosed individuals may be misleading, especially concerning those symptoms and risk factors that influence the diagnostic process itself.</p><p><strong>Highlights: </strong>Current evidence about a disease can (and should) influence the diagnostic process. A feedback loop failure may occur if biased \"evidence\" encourages diagnostic errors, leading to future errors in the evidence base.When diagnostic accuracy varies for mild versus severe cases or between demographic groups, incorrect conclusions about disease prevalence and presentation will result without specifically accounting for such variability.Use of demographic characteristics in the diagnostic process should be done with careful justification, in particular avoiding potential cognitive biases and overcorrection.</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/PMC11281873/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140913150","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":"Risk-Adapted Breast Screening for Women at Low Predicted Risk of Breast Cancer: An Online Discrete Choice Experiment.","authors":"Charlotte Kelley Jones, Suzanne Scott, Nora Pashayan, Stephen Morris, Yasmina Okan, Jo Waller","doi":"10.1177/0272989X241254828","DOIUrl":"10.1177/0272989X241254828","url":null,"abstract":"<p><strong>Background: </strong>A risk-stratified breast screening program could offer low-risk women less screening than is currently offered by the National Health Service. The acceptability of this approach may be enhanced if it corresponds to UK women's screening preferences and values.</p><p><strong>Objectives: </strong>To elicit and quantify preferences for low-risk screening options.</p><p><strong>Methods: </strong>Women aged 40 to 70 y with no history of breast cancer took part in an online discrete choice experiment. We generated 32 hypothetical low-risk screening programs defined by 5 attributes (start age, end age, screening interval, risk of dying from breast cancer, and risk of overdiagnosis), the levels of which were systematically varied between the programs. Respondents were presented with 8 choice sets and asked to choose between 2 screening alternatives or no screening. Preference data were analyzed using conditional logit regression models. The relative importance of attributes and the mean predicted probability of choosing each program were estimated.</p><p><strong>Results: </strong>Participants (<i>N</i> = 502) preferred all screening programs over no screening. An older starting age of screening, younger end age of screening, longer intervals between screening, and increased risk of dying had a negative impact on support for screening programs (<i>P</i> < 0.01). Although the risk of overdiagnosis was of low relative importance, a decreased risk of this harm had a small positive impact on screening choices. The mean predicted probabilities that risk-adapted screening programs would be supported relative to current guidelines were low (range, 0.18 to 0.52).</p><p><strong>Conclusions: </strong>A deintensified screening pathway for women at low risk of breast cancer, especially one that recommends a later screening start age, would run counter to women's breast screening preferences. Further research is needed to enhance the acceptability of offering less screening to those at low risk of breast cancer.</p><p><strong>Highlights: </strong>Risk-based breast screening may involve the deintensification of screening for women at low risk of breast cancer.Low-risk screening pathways run counter to women's screening preferences and values.Longer screening intervals may be preferable to a later start age.Work is needed to enhance the acceptability of a low-risk screening pathway.</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/PMC11283735/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141201041","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":"<i>Medical Decision Making</i> and <i>MDM Policy & Practice Reviewers</i>, 2023.","authors":"","doi":"10.1177/0272989X241258539","DOIUrl":"10.1177/0272989X241258539","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":"141158718","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 Impact of Model Assumptions on Personalized Lung Cancer Screening Recommendations.","authors":"Kevin Ten Haaf, Koen de Nijs, Giulia Simoni, Andres Alban, Pianpian Cao, Zhuolu Sun, Jean Yong, Jihyoun Jeon, Iakovos Toumazis, Summer S Han, G Scott Gazelle, Chung Ying Kong, Sylvia K Plevritis, Rafael Meza, Harry J de Koning","doi":"10.1177/0272989X241249182","DOIUrl":"10.1177/0272989X241249182","url":null,"abstract":"<p><strong>Background: </strong>Recommendations regarding personalized lung cancer screening are being informed by natural-history modeling. Therefore, understanding how differences in model assumptions affect model-based personalized screening recommendations is essential.</p><p><strong>Design: </strong>Five Cancer Intervention and Surveillance Modeling Network (CISNET) models were evaluated. Lung cancer incidence, mortality, and stage distributions were compared across 4 theoretical scenarios to assess model assumptions regarding 1) sojourn times, 2) stage-specific sensitivities, and 3) screening-induced lung cancer mortality reductions. Analyses were stratified by sex and smoking behavior.</p><p><strong>Results: </strong>Most cancers had sojourn times <5 y (model range [MR]; lowest to highest value across models: 83.5%-98.7% of cancers). However, cancer aggressiveness still varied across models, as demonstrated by differences in proportions of cancers with sojourn times <2 y (MR: 42.5%-64.6%) and 2 to 4 y (MR: 28.8%-43.6%). Stage-specific sensitivity varied, particularly for stage I (MR: 31.3%-91.5%). Screening reduced stage IV incidence in most models for 1 y postscreening; increased sensitivity prolonged this period to 2 to 5 y. Screening-induced lung cancer mortality reductions among lung cancers detected at screening ranged widely (MR: 14.6%-48.9%), demonstrating variations in modeled treatment effectiveness of screen-detected cases. All models assumed longer sojourn times and greater screening-induced lung cancer mortality reductions for women. Models assuming differences in cancer epidemiology by smoking behaviors assumed shorter sojourn times and lower screening-induced lung cancer mortality reductions for heavy smokers.</p><p><strong>Conclusions: </strong>Model-based personalized screening recommendations are primarily driven by assumptions regarding sojourn times (favoring longer intervals for groups more likely to develop less aggressive cancers), sensitivity (higher sensitivities favoring longer intervals), and screening-induced mortality reductions (greater reductions favoring shorter intervals).</p><p><strong>Implications: </strong>Models suggest longer screening intervals may be feasible and benefits may be greater for women and light smokers.</p><p><strong>Highlights: </strong>Natural-history models are increasingly used to inform lung cancer screening, but causes for variations between models are difficult to assess.This is the first evaluation of these causes and their impact on personalized screening recommendations through easily interpretable metrics.Models vary regarding sojourn times, stage-specific sensitivities, and screening-induced lung cancer mortality reductions.Model outcomes were similar in predicting greater screening benefits for women and potentially light smokers. Longer screening intervals may be feasible for women and light smokers.</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/PMC11281869/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140913153","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":"The Spillover Effects of Extending Liver Transplantation to Patients with Colorectal Liver Metastases: A Discrete Event Simulation Analysis.","authors":"Hanna Meidell Sjule, Caroline N Vinter, Svein Dueland, Pål-Dag Line, Emily A Burger, Gudrun Marie Waaler Bjørnelv","doi":"10.1177/0272989X241249154","DOIUrl":"10.1177/0272989X241249154","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Liver transplantation is an alternative treatment for patients with nonresectable colorectal cancer liver-only metastases (CRLM); however, the potential effects on wait-list time and life expectancy to other patients on the transplant waiting list have not been considered. We explored the potential effects of expanding liver transplantation eligibility to include patients with CRLM on wait-list time and life expectancy in Norway.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We developed a discrete event simulation model to reflect the Norwegian liver transplantation waiting list process and included 2 groups: 1) patients currently eligible for liver transplantation and 2) CRLM patients. Under 2 alternative CRLM-patient transplant eligibility criteria, we simulated 2 strategies: 1) inclusion of only currently eligible patients (CRLM patients received standard-of-care palliative chemotherapy) and 2) expanding waiting list eligibility to include CRLM patients under 2 eligibility criteria. Model outcomes included median waiting list time, life expectancy, and total life-years.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;For every additional CRLM patient listed per year, the overall median wait-list time, initially 52 d, increased by 8% to 11%. Adding 2 additional CRLM patients under the most restrictive eligibility criteria increased the CRLM patients' average life expectancy by 10.64 y and decreased the average life expectancy for currently eligible patients by 0.05 y. Under these assumptions, there was a net gain of 149.61 life-years over a 10-y programmatic period, which continued to increase under scenarios of adding 10 CRLM patients to the wait-list. Health gains were lower under less restrictive CRLM eligibility criteria. For example, adding 4 additional CRLM patients under the less restrictive eligibility criteria increased the CRLM patients' average life expectancy by 5.64 y and decreased the average life expectancy for currently eligible patients by 0.12 y. Under these assumptions, there was a net gain of 96.36 life-years over a 10-y programmatic period, which continued to increase up to 7 CRLM patients.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Our model-based analysis enabled the consideration of the potential effects of enlisting Norwegian CRLM patients for liver transplantation on wait-list time and life expectancy. Enlisting CRLM patients is expected to increase the total health effects, which supports the implementation of liver transplantation for CRLM patients in Norway.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Highlights: &lt;/strong&gt;Given the Norwegian donor liver availability, adding patients with nonresectable colorectal cancer liver-only metastases (CRLM) to the liver transplantation waiting list had an overall modest, but varying, impact on total waiting list time.Survival gains for selected CRLM patients treated with liver transplantation would likely outweigh the losses incurred to patients listed currently.To improve the total life-years gained in the 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/PMC11283734/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141201046","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":"Emulator-Based Bayesian Calibration of the CISNET Colorectal Cancer Models.","authors":"Carlos Pineda-Antunez, Claudia Seguin, Luuk A van Duuren, Amy B Knudsen, Barak Davidi, Pedro Nascimento de Lima, Carolyn Rutter, Karen M Kuntz, Iris Lansdorp-Vogelaar, Nicholson Collier, Jonathan Ozik, Fernando Alarid-Escudero","doi":"10.1177/0272989X241255618","DOIUrl":"10.1177/0272989X241255618","url":null,"abstract":"<p><strong>Purpose: </strong>To calibrate Cancer Intervention and Surveillance Modeling Network (CISNET)'s SimCRC, MISCAN-Colon, and CRC-SPIN simulation models of the natural history colorectal cancer (CRC) with an emulator-based Bayesian algorithm and internally validate the model-predicted outcomes to calibration targets.</p><p><strong>Methods: </strong>We used Latin hypercube sampling to sample up to 50,000 parameter sets for each CISNET-CRC model and generated the corresponding outputs. We trained multilayer perceptron artificial neural networks (ANNs) as emulators using the input and output samples for each CISNET-CRC model. We selected ANN structures with corresponding hyperparameters (i.e., number of hidden layers, nodes, activation functions, epochs, and optimizer) that minimize the predicted mean square error on the validation sample. We implemented the ANN emulators in a probabilistic programming language and calibrated the input parameters with Hamiltonian Monte Carlo-based algorithms to obtain the joint posterior distributions of the CISNET-CRC models' parameters. We internally validated each calibrated emulator by comparing the model-predicted posterior outputs against the calibration targets.</p><p><strong>Results: </strong>The optimal ANN for SimCRC had 4 hidden layers and 360 hidden nodes, MISCAN-Colon had 4 hidden layers and 114 hidden nodes, and CRC-SPIN had 1 hidden layer and 140 hidden nodes. The total time for training and calibrating the emulators was 7.3, 4.0, and 0.66 h for SimCRC, MISCAN-Colon, and CRC-SPIN, respectively. The mean of the model-predicted outputs fell within the 95% confidence intervals of the calibration targets in 98 of 110 for SimCRC, 65 of 93 for MISCAN, and 31 of 41 targets for CRC-SPIN.</p><p><strong>Conclusions: </strong>Using ANN emulators is a practical solution to reduce the computational burden and complexity for Bayesian calibration of individual-level simulation models used for policy analysis, such as the CISNET CRC models. In this work, we present a step-by-step guide to constructing emulators for calibrating 3 realistic CRC individual-level models using a Bayesian approach.</p><p><strong>Highlights: </strong>We use artificial neural networks (ANNs) to build emulators that surrogate complex individual-based models to reduce the computational burden in the Bayesian calibration process.ANNs showed good performance in emulating the CISNET-CRC microsimulation models, despite having many input parameters and outputs.Using ANN emulators is a practical solution to reduce the computational burden and complexity for Bayesian calibration of individual-level simulation models used for policy analysis.This work aims to support health decision scientists who want to quantify the uncertainty of calibrated parameters of computationally intensive simulation models under a Bayesian framework.</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/PMC11281870/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141301977","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":"The Health Impact of Waiting for Elective Procedures in the NHS in England: A Modeling Framework Applied to Coronary Artery Bypass Graft and Total Hip Replacement.","authors":"Naomi Kate Gibbs, Susan Griffin, Nils Gutacker, Adrián Villaseñor, Simon Walker","doi":"10.1177/0272989X241256639","DOIUrl":"10.1177/0272989X241256639","url":null,"abstract":"<p><strong>Introduction: </strong>The aim of this study is to demonstrate a practical framework that can be applied to estimate the health impact of changes in waiting times across a range of elective procedures in the National Health Service (NHS) in England. We apply this framework by modeling 2 procedures: coronary artery bypass graft (CABG) and total hip replacement (THR).</p><p><strong>Methods: </strong>We built a Markov model capturing health pre- and postprocedure, including the possibility of exiting preprocedure to acute NHS care or self-funded private care. We estimate the change in quality-adjusted life-years (QALYs) over a lifetime horizon for 10 subgroups defined by sex and Index of Multiple Deprivation quintile groups and for 7 alternative scenarios. We include 18 wk as a baseline waiting time consistent with current NHS policy. The model was populated with data from routinely collected data sets where possible (Hospital Episode Statistics, Patient-Reported Outcome Measures, and Office for National Statistics Mortality records), supplemented by the academic literature.</p><p><strong>Results: </strong>Compared with 18 wk, increasing the wait time to 36 wk resulted in a mean discounted QALY loss in the range of 0.034 to 0.043 for CABG and 0.193 to 0.291 for THR. The QALY impact of longer NHS waits was greater for those living in more deprived areas, partly as fewer patients switch to private care.</p><p><strong>Discussion/conclusion: </strong>The proposed framework was applied to 2 different procedures and patient populations. If applied to an expanded group of procedures, it could provide decision makers with information to inform prioritization of waiting lists. There are a number of limitations in routine data on waiting for elective procedures, primarily the lack of information on people still waiting.</p><p><strong>Highlights: </strong>We present a modeling framework that allows for an estimation of the health impact (measured in quality-adjusted life-years) of waiting for elective procedures in the NHS in England.We apply our model to waiting for coronary artery bypass graft (CABG) and total hip replacement (THR). Increasing the wait for THR results in a larger health loss than an equivalent increase in wait for CABG.This model could potentially be used to estimate the impact across an expanded group of procedures to inform prioritization of activities to reduce waiting times.</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/PMC11283740/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141297182","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":3.6,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141181071","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":"Danish Women Make Decisions about Participation in Breast Cancer Screening prior to Invitation Information: An Online Survey Using Experimental Methods","authors":"Eeva-Liisa Røssell, Hilary Louise Bekker, Mara A. Schonberg, Ivar Sønbø Kristiansen, Signe Borgquist, Henrik Støvring","doi":"10.1177/0272989x241248142","DOIUrl":"https://doi.org/10.1177/0272989x241248142","url":null,"abstract":"IntroductionAt mammography screening invitation, the Danish Health Authority recommends women aged 50 to 69 y make an informed decision about whether to be screened. Previous studies have shown that women have very positive attitudes about screening participation. Therefore, we hypothesized that Danish women may already have decided to participate in breast cancer screening prior to receiving their screening invitation at age 50 y.MethodsWe invited a random sample of 2,952 Danish women aged 44 to 49 y (prescreening age) to complete an online questionnaire about barriers to informed screening decision making using the official digital mailbox system in Denmark. We asked participants about their screening intentions using 3 different questions to which women were randomized: screening presented 1) as an opportunity, 2) as a choice, and 3) as an opportunity plus a question about women’s stage of decision making. All women completed questions about background characteristics, intended participation in the screening program, use and impact of screening information, and preferences for the decision-making process. Data were linked to sociodemographic register data.ResultsA total of 790 (26.8%) women participated in the study. Herein, 97% (95% confidence interval: 96%–98%) reported that they wanted to participate in breast cancer screening when invited at age 50 y. When presented with the choice compared with the opportunity framing, more women rejected screening. When asked about their stage of decision making, most (87%) had already made a decision about screening participation and were unlikely to change their mind.ConclusionIn our study, almost all women of prescreening age wanted to participate in breast cancer screening, suggesting that providing information at the time of screening invitation may be too late to support informed decision making.HighlightsAlmost all women of prescreening age (44–49 y) in our study wanted to participate in the Danish national mammography screening program starting at age 50 y. Early decision making represents a barrier for informed decision making as women in this study had intentions to participate in breast cancer screening prior to receiving an official screening invitation, and therefore, providing information at the time of screening invitation may be too late to support informed decision making. Very few women rejected screening participation; however, more women rejected screening when the information was framed as an active choice between having or declining breast cancer screening (continue with usual care) compared with presenting only the option of screening with no description of the alternative. Two-thirds of women reading the screening information in this study had unchanged attitudes toward screening after reading the presented information.","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140839499","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":"A Comparison of Additional Benefit Assessment Methods for Time-to-Event Endpoints Using Hazard Ratio Point Estimates or Confidence Interval Limits by Means of a Simulation Study.","authors":"Christopher A Büsch, Marietta Kirchner, Rouven Behnisch, Meinhard Kieser","doi":"10.1177/0272989X241239928","DOIUrl":"10.1177/0272989X241239928","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;For time-to-event endpoints, three additional benefit assessment methods have been developed aiming at an unbiased knowledge about the magnitude of clinical benefit of newly approved treatments. The American Society of Clinical Oncology (ASCO) defines a continuous score using the hazard ratio point estimate (HR-PE). The European Society for Medical Oncology (ESMO) and the German Institute for Quality and Efficiency in Health Care (IQWiG) developed methods with an ordinal outcome using lower and upper limits of the 95% HR confidence interval (HR-CI), respectively. We describe all three frameworks for additional benefit assessment aiming at a fair comparison across different stakeholders. Furthermore, we determine which ASCO score is consistent with which ESMO/IQWiG category.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;In a comprehensive simulation study with different failure time distributions and treatment effects, we compare all methods using Spearman's correlation and descriptive measures. For determination of ASCO values consistent with categories of ESMO/IQWiG, maximizing weighted Cohen's Kappa approach was used.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Our research depicts a high positive relationship between ASCO/IQWiG and a low positive relationship between ASCO/ESMO. An ASCO score smaller than 17, 17 to 20, 20 to 24, and greater than 24 corresponds to ESMO categories. Using ASCO values of 21 and 38 as cutoffs represents IQWiG categories.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Limitations: &lt;/strong&gt;We investigated the statistical aspects of the methods and hence implemented slightly reduced versions of all methods.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;IQWiG and ASCO are more conservative than ESMO, which often awards the maximal category independent of the true effect and is at risk of overcompensating with various failure time distributions. ASCO has similar characteristics as IQWiG. Delayed treatment effects and underpowered/overpowered studies influence all methods in some degree. Nevertheless, ESMO is the most liberal one.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Highlights: &lt;/strong&gt;For the additional benefit assessment, the American Society of Clinical Oncology (ASCO) uses the hazard ratio point estimate (HR-PE) for their continuous score. In contrast, the European Society for Medical Oncology (ESMO) and the German Institute for Quality and Efficiency in Health Care (IQWiG) use the lower and upper 95% HR confidence interval (HR-CI) to specific thresholds, respectively. ESMO generously assigns maximal scores, while IQWiG is more conservative.This research provides the first comparison between IQWiG and ASCO and describes all three frameworks for additional benefit assessment aiming for a fair comparison across different stakeholders. Furthermore, thresholds for ASCO consistent with ESMO and IQWiG categories are determined, enabling a comparison of the methods in practice in a fair manner.IQWiG and ASCO are the more conservative methods, while ESMO awards high percentages of ","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11102642/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140899374","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}