Medical Decision Making最新文献

{"title":"Patients' Attitude toward Less Frequent Surveillance of Low-Risk Pancreatic Cysts: A Multicenter European Cohort Study.","authors":"Marloes L J A Sprij, Inge M C M de Kok, Daan D Nieboer, Gabriele Capurso, Jihane Meziani, Mattheus C B Wielenga, Mirjam C M van der Ende, Marianne E Smits, Riccardo Casadei, Matthijs P Schwartz, Frederike G I van Vilsteren, Chantal Hoge, Rutger Quispel, Pieter Honkoop, Laurens A van der Waaij, Gemma Rossi, Adriaan C I T L Tan, Marco J Bruno, Djuna L Cahen","doi":"10.1177/0272989X251352750","DOIUrl":"https://doi.org/10.1177/0272989X251352750","url":null,"abstract":"<p><p>BackgroundRecent studies show that low-risk pancreatic cysts may require less frequent monitoring. Future guidelines will likely adapt their recommendations accordingly. Our goal was to explore the willingness of individuals with a low-risk pancreatic cyst to undergo less frequent surveillance and to identify associated characteristics with such willingness.MethodsThis is a side study of the international PACYFIC study, which prospectively collects data on cyst surveillance, including questionnaires to assess participants' attitude toward surveillance. Individuals with low-risk cysts at baseline, without given standardized information by the study protocol, were enrolled. Their responses to the baseline question, \"Would you prefer less frequent surveillance? Yes/No,\" were correlated with baseline characteristics using multivariable logistic regression, namely, age, country of residence, symptoms, medical and family history, time since first cyst detection, and Hospital Anxiety Depression Scale score.ResultsOf the 215 participants included from the Netherlands (<i>n</i> = 185) and Italy (<i>n</i> = 30), only 47 (22%) were willing to undergo less surveillance. Characteristics positively associated with this willingness were older age (odds ratio [OR] 1.87 per 10 y, 95% confidence interval [CI]: 1.15-3.04) and Italian residency (OR 16.35, 95% CI: 5.65-47.31). A medical history of cancer was negatively associated (OR 0.28, 95% CI: 0.09-0.90). No other associations were observed.ConclusionMost participants appear unwilling to undergo less frequent cyst surveillance. Older age and residing in Italy were associated with a greater willingness toward less rigorous surveillance, while a history of cancer did the opposite. Identifying which individuals are hesitant to undergo less frequent surveillance may help clinicians tailor their counseling and can support implementation of future guideline with fewer surveillance recommendations.HighlightsMost low-risk individuals were reluctant toward less frequent pancreatic cyst surveillance.Older age and residency in Italy were associated with a higher willingness.A medical history of cancer was associated with an unwillingness.Standardized patient information may increase the willingness, but such information has yet to be developed.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"272989X251352750"},"PeriodicalIF":3.1,"publicationDate":"2025-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144769157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling the Impact of Multicancer Early Detection Tests: A Review of Natural History of Disease Models.","authors":"Olena Mandrik, Sophie Whyte, Natalia Kunst, Annabel Rayner, Melissa Harden, Sofia Dias, Katherine Payne, Stephen Palmer, Marta O Soares","doi":"10.1177/0272989X251351639","DOIUrl":"https://doi.org/10.1177/0272989X251351639","url":null,"abstract":"<p><p>IntroductionThe potential for multicancer early detection (MCED) tests to detect cancer at earlier stages is currently being evaluated in screening clinical trials. Once trial evidence becomes available, modeling will be necessary to predict the effects on final outcomes (benefits and harms), account for heterogeneity in determining clinical and cost-effectiveness, and explore alternative screening program specifications. The natural history of disease (NHD) component will use statistical, mathematical, or calibration methods. This work aims to identify, review, and critically appraise the existing literature for alternative modeling approaches proposed for MCED that include an NHD component.MethodsModeling approaches for MCED screening that include an NHD component were identified from the literature, reviewed, and critically appraised. Purposively selected (non-MCED) cancer-screening models were also reviewed. The appraisal focused on the scope, data sources, evaluation approaches, and the structure and parameterization of the models.ResultsFive different MCED models incorporating an NHD component were identified and reviewed, alongside 4 additional (non-MCED) models. The critical appraisal highlighted several features of this literature. In the absence of trial evidence, MCED effects are based on predictions derived from test accuracy. These predictions rely on simplifying assumptions with unknown impacts, such as the stage-shift assumption used to estimate mortality impacts from predicted stage shifts. None of the MCED models fully characterized uncertainty in the NHD or examined uncertainty in the stage-shift assumption.ConclusionThere is currently no modeling approach for MCEDs that can integrate clinical study evidence. In support of policy, it is important that efforts are made to develop models that make the best use of data from the large and costly clinical studies being designed and implemented across the globe.HighlightsIn the absence of trial evidence, published estimates of the effects of multicancer early detection (MCED) tests are based on predictions derived from test accuracy.These predictions rely on simplifying assumptions, such as the stage-shift assumption used to estimate mortality effects from predicted stage shifts. The effects of such simplifying assumptions are mostly unknown.None of the existing MCED models fully characterize uncertainty in the natural history of disease; none examine uncertainty in the stage-shift assumption.Currently, there is no modeling approach that can integrate clinical study evidence.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"272989X251351639"},"PeriodicalIF":3.1,"publicationDate":"2025-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144769156","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 Acceptance of Overall Survival Extrapolation Methods in Solid Tumor Treatments by Health Technology Assessment Agencies in England, France, and Australia between 2017 and 2022.","authors":"Jean-Baptiste Trouiller, Arthur Quenéchdu, Mondher Toumi, Laurent Boyer, Philippe Laramée","doi":"10.1177/0272989X251351635","DOIUrl":"https://doi.org/10.1177/0272989X251351635","url":null,"abstract":"<p><p>BackgroundSurvival extrapolation is used to predict patients' overall survival beyond clinical trial follow-up. It can significantly affect the results of a cost-effectiveness analysis and subsequent pricing and reimbursement decisions. However, selecting an appropriate model involves subjectivity, leading to discrepancies between methods submitted by manufacturers and those accepted by health technology assessment (HTA) agencies. This review describes the acceptance and criticisms of overall survival extrapolation methods by HTA agencies in England, France, and Australia.MethodsElectronic searches conducted on September 4, 2022, identified HTA evaluations for oncology therapies indicated for the treatment of solid tumors from the National Institute for Health and Care Excellence (NICE) in England, the Haute Autorité de Santé (HAS) in France, and the Pharmaceutical Benefits Advisory Committee (PBAC) in Australia, published between August 2017 and August 2022. Information on the overall survival extrapolation model submitted by the manufacturer was extracted. The acceptance decision of the HTA agency and the key criticisms were also recorded.ResultsA total of 140 HTA evaluations were identified. The initial overall survival extrapolation method was accepted in 21% of cases. The most frequently cited criticisms related to a lack of or inappropriate incorporation of treatment effect waning over time (31%). Other criticisms included choice of parametric distribution, in which multiple distributions were often considered plausible (24%); immaturity of survival data (15%); and concerns about the proportional hazards assumption, which lacked clinical validity (8%).ConclusionThis review highlights the low acceptance of survival extrapolation methods and the areas of discordance between manufacturers and HTA agencies in England, France, and Australia. Low acceptance rates of survival extrapolation methods by HTA bodies can affect pricing and reimbursement decisions of new therapies, delaying patient access.HighlightsThis review found that the survival extrapolation methods initially submitted by companies were accepted in only 21% of cases.The most common areas of discordance between companies and HTA agencies were inappropriate modeling of treatment effect over time, choice of parametric distribution, immaturity of survival data, and concerns about the proportional hazards assumption.There is a need for more consistent guidance on the selection of an appropriate extrapolation method to limit the inherent subjectivity surrounding survival curve selection.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"272989X251351635"},"PeriodicalIF":3.1,"publicationDate":"2025-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144765737","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":"Decision Frameworks for Assessing Cost-Effectiveness Given Previous Nonoptimal Decisions.","authors":"Doug Coyle, David Glynn, Jeremy D Goldhaber-Fiebert, Edward C F Wilson","doi":"10.1177/0272989X251340941","DOIUrl":"10.1177/0272989X251340941","url":null,"abstract":"<p><p>IntroductionEconomic evaluations identify the best course of action by a decision maker with respect to the level of health within the overall population. Traditionally, they identify 1 optimal treatment choice. In many jurisdictions, multiple technologies can be covered for the same heterogeneous patient population, which limits the applicability of this framework for directly determining whether a new technology should be covered. This article explores the impact of different decision frameworks within this context.MethodsThree alternate decision frameworks were considered: the traditional normative framework in which only the optimal technology will be covered (normative); a commonly adopted framework in which the new technology is recommended for reimbursement only if it is optimal, with coverage of other technologies remaining as before (current); and a framework that assesses specifically whether coverage of the new technology is optimal, incorporating previous reimbursement decisions and the market share of current technologies (positivist). The implications of the frameworks were assessed using a simulated probabilistic Markov model for a chronic progressive condition.ResultsResults illustrate how the different frameworks can lead to different reimbursement recommendations. This in turn produces differences in population health effects and the resultant price reductions required for covering the new technology.ConclusionBy covering only the optimal treatment option, decision makers can maximize the level of health across a population. If decision makers are unwilling to defund technologies, however, the second best option of adopting the positivist framework has the greatest relevance with respect to deciding whether a new technology should be covered.HighlightsTraditionally, economic evaluations focus on identifying the optimal treatment choice.This paper considers three alternative decision frameworks, within the context of multiple technologies being covered for the same heterogeneous patient population.This paper highlight that if decision makers are unwilling to defund therapies, current approaches to assessing cost effectiveness may be non-optimal.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"703-713"},"PeriodicalIF":3.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12260196/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144276466","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":"So You've Got a High AUC, Now What? An Overview of Important Considerations when Bringing Machine-Learning Models from Computer to Bedside.","authors":"Jiawen Deng, Mohamed E Elghobashy, Kathleen Zang, Shubh K Patel, Eddie Guo, Kiyan Heybati","doi":"10.1177/0272989X251343082","DOIUrl":"10.1177/0272989X251343082","url":null,"abstract":"<p><p>Machine-learning (ML) models have the potential to transform health care by enabling more personalized and data-driven clinical decision making. However, their successful implementation in clinical practice requires careful consideration of factors beyond predictive accuracy. We provide an overview of essential considerations for developing clinically applicable ML models, including methods for assessing and improving calibration, selecting appropriate decision thresholds, enhancing model explainability, identifying and mitigating bias, as well as methods for robust validation. We also discuss strategies for improving accessibility to ML models and performing real-world testing.HighlightsThis tutorial provides clinicians with a comprehensive guide to implementing machine-learning classification models in clinical practice.Key areas covered include model calibration, threshold selection, explainability, bias mitigation, validation, and real-world testing, all of which are essential for the clinical deployment of machine-learning models.Following these guidance can help clinicians bridge the gap between machine-learning model development and real-world application and enhance patient care outcomes.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"640-653"},"PeriodicalIF":3.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12260203/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144175400","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":"Facilitators and Barriers of the Use of Prognostic Models for Clinical Decision Making in Acute Neurologic Care: A Systematic Review.","authors":"Ellen X Y Hu, Evelien S van Hoorn, Isabel R A Retel Helmrich, Susanne Muehlschlegel, Judith A C Rietjens, Hester F Lingsma","doi":"10.1177/0272989X251343027","DOIUrl":"10.1177/0272989X251343027","url":null,"abstract":"<p><p>BackgroundPrognostic models are crucial for predicting patient outcomes and aiding clinical decision making. Despite their availability in acute neurologic care, their use in clinical practice is limited, with insufficient reflection on reasons for this scarce implementation.PurposeTo summarize facilitators and barriers among clinicians affecting the use of prognostic models in acute neurologic care.Data SourcesSystematic searches were conducted in Embase, Medline ALL, Web of Science Core Collection, and Cochrane Central Register of Controlled Trials from inception until February 2024.Study SelectionEligible studies included those providing clinicians' perspectives on the use of prognostic models in acute neurologic care.Data ExtractionData were extracted concerning study characteristics, study aim, data collection and analysis, prognostic models, participant characteristics, facilitators, and barriers. Risk of bias was assessed using the Qualsyst tool.Data SynthesisFindings were structured around the Unified Theory of Acceptance and Use of Technology framework. Identified facilitators included improved communication with patients and surrogate decision makers (<i>n</i> = 9), reassurance of clinical judgment (<i>n</i> = 6) perceived improved patient outcomes (<i>n</i> = 4), standardization of care (<i>n</i> = 4), resource optimization (<i>n</i> = 3), and extension of clinical knowledge (<i>n</i> = 3). Barriers included perceived misinterpretation during risk communication (<i>n</i> = 3), mistrust in data (<i>n</i> = 3), perceived reduction of clinicians' autonomy (<i>n</i> = 3), and ethical considerations (<i>n</i> = 2). In total, 15 studies were included, with all but 1 demonstrating good methodological quality. None were excluded due to poor quality ratings.LimitationsThis review identifies limitations, including study heterogeneity, exclusion of gray literature, and the scarcity of evaluations on model implementation.ConclusionsUnderstanding facilitators and barriers may enhance prognostic model development and implementation. Bridging the gap between development and clinical use requires improved collaboration among researchers, clinicians, patients, and surrogate decision makers.HighlightsThis is the first systematic review to summarize published facilitators and barriers affecting the use of prognostic models in acute neurologic care from the clinicians' perspective.Commonly reported barriers and facilitators were consistent with several domains of the Unified Theory of Acceptance and Use of Technology model, including effort expectancy, social influence, and facilitating conditions, with the focus on the performance expectancy domain.Future implementation research including collaboration with researchers from different fields, clinicians, patients, and their surrogate decision makers may be highly valuable for future model development and implementation.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"753-770"},"PeriodicalIF":3.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12260205/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144530739","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 Impact of Machine Learning Mortality Risk Prediction on Clinician Prognostic Accuracy and Decision Support: A Randomized Vignette Study.","authors":"Ravi B Parikh, William J Ferrell, Anthony Girard, Jenna White, Sophia Fang, Justin E Bekelman, Marilyn M Schapira","doi":"10.1177/0272989X251349489","DOIUrl":"10.1177/0272989X251349489","url":null,"abstract":"&lt;p&gt;&lt;p&gt;BackgroundMachine learning (ML) algorithms may improve the prognosis for serious illnesses such as cancer, identifying patients who may benefit from earlier palliative care (PC) or advance care planning (ACP). We evaluated the impact of various presentation strategies of a hypothetical ML algorithm on clinician prognostic accuracy and decision making.MethodsThis was a randomized clinical vignette survey study among medical oncologists who treat metastatic non-small-cell lung cancer (mNSCLC). Between March and June 2023, clinicians were shown 3 vignettes of patients presenting with mNSCLC. The vignettes varied by prognostic risk, as defined from the Lung Cancer Prognostic Index (LCPI). Clinicians estimated life expectancy in months and made recommendations about PC and ACP. Clinicians were then shown the same vignette with a hypothetical survival estimate from a black-box ML algorithm; clinicians were randomized to receive the ML prediction using absolute and/or reference-dependent prognostic estimates. The primary outcome was prognostic accuracy relative to the LCPI.ResultsAmong 51 clinicians with complete responses, the median years in practice was 7 (interquartile range 3.5-19), 14 (27.5%) were female, 23 (45.1%) practiced in a community oncology setting, and baseline accuracy was 54.9% (95% confidence interval [CI] 47.0-62.8) across all vignettes. ML presentation improved accuracy (mean change relative to baseline 20.9%, 95% CI 13.9-27.9, &lt;i&gt;P&lt;/i&gt; &lt; 0.001). ML outputs using an absolute presentation strategy alone (mean change 27.4%, 95% 16.8-38.1, &lt;i&gt;P&lt;/i&gt; &lt; 0.001) or with reference dependence (mean change 33.4%, 95% 23.9-42.8, &lt;i&gt;P&lt;/i&gt; &lt; 0.001) improved accuracy, but reference dependence alone did not (mean change 2.0% [95% CI -11.1 to 15.0], &lt;i&gt;P&lt;/i&gt; = 0.77). ML presentation did not change the rates of recommending ACP nor PC referral (mean change 1.3% and 0.7%, respectively).LimitationsThe singular use case of prognosis in mNSCLC, low initial response rate.ConclusionsML-based assessments may improve prognostic accuracy but not result in changed decision making.ImplicationsML prognostic algorithms prioritizing explainability and absolute prognoses may have greater impact on clinician decision making.Trial Registration: CT.gov: NCT06463977HighlightsWhile machine learning (ML) algorithms may accurately predict mortality, the impact of prognostic ML on clinicians' prognostic accuracy and decision making and optimal presentation strategies for ML outputs are unclear.In this multicenter randomized survey study among vignettes of patients with advanced cancer, prognostic accuracy improved by 20.9% when clinicians reviewed vignettes with a hypothetical ML mortality risk prediction, with absolute risk presentation strategies resulting in greater accuracy gains than reference-dependent presentations alone.However, ML presentation did not change the rates of recommending advance care planning or palliative care referral (1.3% and 0.7%, respectiv","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"690-702"},"PeriodicalIF":3.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12233153/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144561782","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":"Postpartum Sterilization after a Preterm Delivery Is Not Associated with Decision Regret.","authors":"Marika Toscano, Sarah J Betstadt, Sara Spielman, Gayathri Guru Murthy, Brooke A Levandowski","doi":"10.1177/0272989X251341478","DOIUrl":"10.1177/0272989X251341478","url":null,"abstract":"<p><p>BackgroundAlthough sterilization is one of the most effective methods of birth control, some physicians may hesitate to perform postpartum sterilizations on patients after preterm birth, as preterm labor and delivery may preclude adequate counseling.MethodsThis is a cross-sectional study conducted at a single, tertiary care, academic institution of adult pregnant patients who experienced a spontaneous or iatrogenic preterm delivery between March 15, 2011, and May 10, 2014 and underwent postpartum female surgical sterilization within 12 wk of delivery. A validated Decision Regret Scale was administered 7 to 11 y later. Univariate and bivariate analyses were conducted. Unadjusted and multivariate logistic regression analyses identified factors associated with moderate to severe decision regret.ResultsMost participants (75.5%) with a preterm delivery reported no or mild regret associated with their sterilization. Circumstances surrounding the sterilization decision were positive, as 85.7% reported having enough information, 81.6% reported enough emotional support, and 75.5% reported adequate decision time. Adjusting for maternal and gestational age at delivery plus other covariates, only those reporting they had adequate time to make their sterilization decision remained significantly associated with no or mild regret (odds ratio: 0.002, 95% confidence interval: <0.001-0.61).DiscussionStudy results indicated high confidence in the sterilization decision, which was not affected by maternal age at delivery or the fact that the individual had a preterm delivery, emphasizing the importance of individualized counseling and support for patients during the decision-making process.ConclusionProviding adequate time for patients to decide on postpartum surgical sterilization was the most important factor for decreased sterilization regret.ImplicationsThe decision for sterilization should be made using a patient-centered, shared decision-making framework.HighlightsAmong patients with a preterm delivery who underwent postpartum surgical sterilization, maternal age at delivery was not associated with increased decision regret.Providing adequate time for patients to decide on postpartum surgical sterilization was the most important factor for decreased sterilization regret among patients with a preterm delivery.We must trust the patient knows they are making the right decision for themselves in that moment, even if this is at the time of a preterm delivery.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"654-664"},"PeriodicalIF":3.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12270760/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144340575","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":"Impact of Limited Sample Size and Follow-up on Partitioned Survival and Multistate Modeling-Based Health Economic Models: A Simulation Study.","authors":"Jaclyn M Beca, Kelvin K W Chan, David M J Naimark, Petros Pechlivanoglou","doi":"10.1177/0272989X251342596","DOIUrl":"10.1177/0272989X251342596","url":null,"abstract":"<p><p>BackgroundEconomic models often require extrapolation of clinical time-to-event data for multiple events. Two modeling approaches in oncology that incorporate time dependency include partitioned survival models (PSM) and semi-Markov decision models estimated using multistate modeling (MSM). The objective of this simulation study was to assess the performance of PSM and MSM across datasets with varying sample size and degrees of censoring.MethodsWe generated disease trajectories of progression and death for multiple hypothetical populations with advanced cancers. These populations served as the sampling pool for simulated trial cohorts with multiple sample sizes and various levels of follow-up. We estimated MSM and PSM by fitting survival models to these simulated datasets with different approaches to incorporating general population mortality (GPM) and selected best-fitting models using statistical criteria. Mean survival was compared with \"true\" population values to assess error.ResultsWith near complete follow-up, both PSMs and MSMs accurately estimated mean population survival, while smaller samples and shorter follow-up times were associated with a larger error across approaches and clinical scenarios, especially for more distant clinical endpoints. MSMs were slightly more often not estimable when informed by studies with small sample sizes or short follow-up, due to low numbers at risk for the downstream transition. However, when estimable, the MSM models more commonly produced a smaller error in mean survival than the PSMs did.ConclusionsCaution should be taken with all modeling approaches when the underlying data are very limited, particularly PSMs, due to the large errors produced. When estimable and for selections based on statistical criteria, MSMs performed similar to or better than PSMs in estimating mean survival with limited data.HighlightsCaution should be taken with all modeling approaches when underlying data are very limited.Partitioned survival models (PSMs) can lead to significant errors, particularly with limited follow-up. Incorporating general population mortality (GPM) via internal additive hazards improved estimates of mean survival, but the effects were modest.When estimable, decision models based on multistate modeling (MSM) produced similar or smaller error in mean survival compared with PSM, but small samples or limited deaths after progression produce additional challenges for fitting MSMs; more research is needed to improve estimation of MSMs and similar state transition-based modeling methods with limited data.Future studies are needed to assess the applicability of these findings to comparative analyses estimating incremental survival benefits.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"714-725"},"PeriodicalIF":3.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12260197/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144499005","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":"Mapping and Linking between the EQ-5D-5L and the PROMIS Domains in the United States.","authors":"Xiaodan Tang, Ron D Hays, David Cella, Sarah Acaster, Benjamin David Schalet, Asia Sikora Kessler, Montserrat Vera Llonch, Janel Hanmer","doi":"10.1177/0272989X251340990","DOIUrl":"10.1177/0272989X251340990","url":null,"abstract":"<p><p>ObjectivesThe EQ-5D-5L and Patient-Reported Outcomes Measurement Information System (PROMIS®) preference score (PROPr) are preference-based measures. This study compares mapping and linking approaches to align the PROPr and the PROMIS domains included in PROPr plus Anxiety with EQ-5D-5L item responses and preference scores.MethodsA general population sample of 983 adults completed the online survey. Regression-based mapping methods and item response theory (IRT) linking methods were used to align scores. Mapping was used to predict EQ-5D-5L item responses or preference scores using PROMIS domain scores. Equating strategies were applied to address regression to the mean. The linking approach estimated item parameters of EQ-5D-5L based on the PROMIS score metric and generated bidirectional crosswalks between EQ-5D-5L item responses and relevant PROMIS domain scores.ResultsEQ-5D-5L item responses were significantly accounted for by PROMIS domains of Anxiety, Depression, Fatigue, Pain Interference, Physical Function, Social Roles, and Sleep Disturbance. EQ-5D-5L preference scores were accounted for by the same PROMIS domains, excluding Anxiety and Fatigue, and by the PROPr preference scores. IRT-linking crosswalks were generated between EQ-5D-5L item responses and PROMIS domains of Physical Function, Pain, and Depression. Small differences were found between observed and predicted scores for all 3 methods. The direct mapping approach (directly predicting EQ-5D-5L scores) with the equipercentile equating strategy proved superior to the linking method due to improved prediction accuracy and comparable score range coverage.ConclusionsThe PROPr and the PROMIS domains included in the PROMIS-29+2 predict EQ-5D-5L preference scores or item responses. Both methods can generate acceptably precise EQ-5D-5L preference scores, with the direct mapping approach using the equating strategy offering better precision. We summarized recommended score conversion tables based on available and desired scores.HighlightsThis study compares mapping (score prediction) and IRT-based linking approaches to align the PROPr and the PROMIS domains with EQ-5D-5L item responses and preference scores.Researchers, clinicians, and stakeholders can use this study's regression formulas and score crosswalks to convert scores between PROMIS and EQ-5D-5L.Mapping can generate more precise scores, while linking offers greater flexibility in score estimation when fewer PROMIS domain scores are collected.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"740-752"},"PeriodicalIF":3.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12260195/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144286947","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}