Medical Decision Making最新文献

{"title":"Directed Acyclic Graphs in Decision-Analytic Modeling: Bridging Causal Inference and Effective Model Design in Medical Decision Making.","authors":"Stijntje W Dijk, Maurice Korf, Jeremy A Labrecque, Ankur Pandya, Bart S Ferket, Lára R Hallsson, John B Wong, Uwe Siebert, M G Myriam Hunink","doi":"10.1177/0272989X241310898","DOIUrl":"https://doi.org/10.1177/0272989X241310898","url":null,"abstract":"<p><strong>Highlights: </strong>Our commentary proposes the application of directed acyclic graphs (DAGs) in the design of decision-analytic models, offering researchers a valuable and structured tool to enhance transparency and accuracy by bridging the gap between causal inference and model design in medical decision making.The practical examples in this article showcase the transformative effect DAGs can have on model structure, parameter selection, and the resulting conclusions on effectiveness and cost-effectiveness.This methodological article invites a broader conversation on decision-modeling choices grounded in causal assumptions.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"272989X241310898"},"PeriodicalIF":3.1,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143025164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Sequential Calibration Approach to Address Challenges of Repeated Calibration of a COVID-19 Model.","authors":"Eva A Enns, Zongbo Li, Shannon B McKearnan, Szu-Yu Zoe Kao, Erinn C Sanstead, Alisha Baines Simon, Pamela J Mink, Stefan Gildemeister, Karen M Kuntz","doi":"10.1177/0272989X241292012","DOIUrl":"10.1177/0272989X241292012","url":null,"abstract":"<p><strong>Background: </strong>Mathematical models served a critical role in COVID-19 decision making throughout the pandemic. Model calibration is an essential, but often computationally burdensome, step in model development that provides estimates for difficult-to-measure parameters and establishes an up-to-date modeling platform for scenario analysis. In the evolving COVID-19 pandemic, frequent recalibration was necessary to provide ongoing support to decision makers. In this study, we address the computational challenges of frequent recalibration with a new calibration approach.</p><p><strong>Methods: </strong>We calibrated and recalibrated an age-stratified dynamic compartmental model of COVID-19 in Minnesota to statewide COVID-19 cumulative mortality and prevalent age-specific hospitalizations from March 22, 2020 through August 20, 2021. This period was divided into 10 calibration periods, reflecting significant changes in policies, messaging, and/or epidemiological conditions in Minnesota. When recalibrating the model from one period to the next, we employed a sequential calibration approach that leveraged calibration results from previous periods and adjusted only parameters most relevant to the calibration target data of the new calibration period to improve computational efficiency. We compared computational burden and performance of the sequential calibration approach to a more traditional calibration method, in which all parameters were readjusted with each recalibration.</p><p><strong>Results: </strong>Both calibration methods identified parameter sets closely reproducing prevalent hospitalizations and cumulative deaths over time. By the last calibration period, both approaches converged to similar parameter values. However, the sequential calibration approach identified parameter sets that more tightly fit calibration targets and required substantially less computation time than traditional calibration.</p><p><strong>Conclusions: </strong>Sequential calibration is an efficient approach to maintaining up-to-date models with evolving, time-varying parameters and potentially identifies better-fitting parameter sets than traditional calibration.</p><p><strong>Highlights: </strong>This study used a sequential calibration approach, which takes advantage of previous calibration results to reduce the number of parameters to be estimated in each round of calibration, improving computational efficiency and algorithm convergence to best-fitting parameter values.Both sequential and traditional calibration approaches were able to identify parameter sets that closely reproduced calibration targets. However, the sequential calibration approach generated parameter sets that yielded tighter fits and was less computationally burdensome.Sequential calibration is an efficient approach to maintaining up-to-date models with evolving, time-varying parameters.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"3-16"},"PeriodicalIF":3.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142631439","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":"Awareness of Disease Incurability Moderates the Association between Patients' Health Status and Their Treatment Preferences.","authors":"Louisa Camille Poco, Ishwarya Balasubramanian, Isha Chaudhry, Chetna Malhotra","doi":"10.1177/0272989X241293716","DOIUrl":"10.1177/0272989X241293716","url":null,"abstract":"<p><strong>Background: </strong>With advancing illness, some patients with heart failure (HF) opt to receive life-extending treatments despite their high costs, while others choose to forgo these treatments, emphasizing cost containment. We examined the association between patients' health status and their preferences for treatment cost containment versus life extension and whether their patients' awareness of disease incurability moderated this association.</p><p><strong>Methods: </strong>In a prospective cohort of patients (<i>N</i> = 231) with advanced HF in Singapore, we assessed patients' awareness of disease incurability, health status, and treatment preferences every 4 mo for up to 4 y (up to 13 surveys). Using random effects multinomial logistic regression models, we assessed whether patients' awareness of disease incurability moderated the association between their health status and treatment preferences.</p><p><strong>Results: </strong>About half of the patients in our study lacked awareness of HF's incurability. Results from regression analyses showed that patients with better health status, as indicated by lower distress scores (odds ratio [OR] [95% confidence interval {CI}]: 0.862 [0.754, 0.985]) and greater physical well-being (1.12 [1.03, 1.21]); and who lacked awareness of their disease's incurability were more likely to prefer higher cost containment/minimal life extension treatments compared with lower cost containment/maximal life extension.</p><p><strong>Conclusions: </strong>This study underscores the significance of patients' awareness in disease incurability in shaping the relationship between their health status and treatment preferences. Our findings emphasize the need to incorporate illness education during goals-of-care conversations with patients and the importance of revisiting these conversations frequently to accommodate changing treatment preferences.</p><p><strong>Highlights: </strong>The health status of patients with advanced heart failure was associated with their treatment preferences.Patients whose health status improved and who lacked awareness of their disease's incurability were more likely to prefer higher cost containment/minimal life extension treatments.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"74-85"},"PeriodicalIF":3.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142631360","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":"Unclear Trajectory and Uncertain Benefit: Creating a Lexicon for Clinical Uncertainty in Patients with Critical or Advanced Illness Using a Delphi Consensus Process.","authors":"Samuel K McGowan, Maria-Jose Corrales-Martinez, Teva Brender, Alexander K Smith, Shannen Kim, Krista L Harrison, Hunter Mills, Albert Lee, David Bamman, Julien Cobert","doi":"10.1177/0272989X241293446","DOIUrl":"10.1177/0272989X241293446","url":null,"abstract":"<p><strong>Background: </strong>Clinical uncertainty is associated with increased resource utilization, worsened health-related quality of life for patients, and provider burnout, particularly during critical illness. Existing data are limited, because determining uncertainty from notes typically requires manual, qualitative review. We sought to develop a consensus list of descriptors of clinical uncertainty and then, using a thematic analysis approach, describe how respondents consider their use in intensive care unit (ICU) notes, such that future work can extract uncertainty data at scale.</p><p><strong>Design: </strong>We conducted a Delphi consensus study with physicians across multiple institutions nationally who care for critically ill patients or patients with advanced illnesses. Participants were given a definition for clinical uncertainty and collaborated through multiple rounds to determine which words represent uncertainty in clinician notes. We also administered surveys that included open-ended questions to participants about clinical uncertainty. Following derivation of a consensus list, we analyzed participant responses using thematic analysis to understand the role of uncertainty in clinical documentation.</p><p><strong>Results: </strong>Nineteen physicians participated in at least 2 of the Delphi rounds. Consensus was achieved for 44 words or phrases over 5 rounds of the Delphi process. Clinicians described comfort with using uncertainty terms and used them in a variety of ways: documenting and processing the diagnostic thinking process, enlisting help, identifying incomplete information, and practicing transparency to reflect uncertainty that was present.</p><p><strong>Conclusions: </strong>Using a consensus process, we created an uncertainty lexicon that can be used for uncertainty data extraction from the medical record. We demonstrate that physicians, particularly in the ICU, are comfortable with uncertainty and document uncertainty terms frequently to convey the complexity and ambiguity that is pervasive in critical illness.</p><p><strong>Highlights: </strong>Question: What words do physicians caring for critically ill patients use to document clinical uncertainty, and why?Findings: A consensus list of 44 words or phrases was identified by a group of experts. Physicians expressed comfort with using these words in the electronic health record.Meaning: Physicians are comfortable with uncertainty words and document them frequently to convey the complexity and ambiguity that is pervasive in critical illness.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"34-44"},"PeriodicalIF":3.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11645224/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142669758","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":"Using QALYs as an Outcome for Assessing Global Prediction Accuracy in Diabetes Simulation Models.","authors":"Helen A Dakin, Ni Gao, José Leal, Rury R Holman, An Tran-Duy, Philip Clarke","doi":"10.1177/0272989X241285866","DOIUrl":"10.1177/0272989X241285866","url":null,"abstract":"<p><strong>Objectives: </strong>(1) To demonstrate the use of quality-adjusted life-years (QALYs) as an outcome measure for comparing performance between simulation models and identifying the most accurate model for economic evaluation and health technology assessment. QALYs relate directly to decision making and combine mortality and diverse clinical events into a single measure using evidence-based weights that reflect population preferences. (2) To explore the usefulness of Q², the proportional reduction in error, as a model performance metric and compare it with other metrics: mean squared error (MSE), mean absolute error, bias (mean residual), and <i>R</i>².</p><p><strong>Methods: </strong>We simulated all EXSCEL trial participants (<i>N</i> = 14,729) using the UK Prospective Diabetes Study Outcomes Model software versions 1 (UKPDS-OM1) and 2 (UKPDS-OM2). The EXSCEL trial compared once-weekly exenatide with placebo (median 3.2-y follow-up). Default UKPDS-OM2 utilities were used to estimate undiscounted QALYs over the trial period based on the observed events and survival. These were compared with the QALYs predicted by UKPDS-OM1/2 for the same period.</p><p><strong>Results: </strong>UKPDS-OM2 predicted patients' QALYs more accurately than UKPDS-OM1 did (MSE: 0.210 v. 0.253; Q²: 0.822 v. 0.786). UKPDS-OM2 underestimated QALYs by an average of 0.127 versus 0.150 for UKPDS-OM1. UKPDS-OM2 predictions were more accurate for mortality, myocardial infarction, and stroke, whereas UKPDS-OM1 better predicted blindness and heart disease. Q² facilitated comparisons between subgroups and (unlike <i>R</i>²) was lower for biased predictors.</p><p><strong>Conclusions: </strong>Q² for QALYs was useful for comparing global prediction accuracy (across all clinical events) of diabetes models. It could be used for model registries, choosing between simulation models for economic evaluation and evaluating the impact of recalibration. Similar methods could be used in other disease areas.</p><p><strong>Highlights: </strong>Diabetes simulation models are currently validated by examining their ability to predict the incidence of individual events (e.g., myocardial infarction, stroke, amputation) or composite events (e.g., first major adverse cardiovascular event).We introduce Q², the proportional reduction in error, as a measure that may be useful for evaluating and comparing the prediction accuracy of econometric or simulation models.We propose using the Q² or mean squared error for QALYs as global measures of model prediction accuracy when comparing diabetes models' performance for health technology assessment; these can be used to select the most accurate simulation model for economic evaluation and to evaluate the impact of model recalibration in diabetes or other conditions.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"45-59"},"PeriodicalIF":3.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11645849/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142548605","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":"Veterans' Lung Cancer Risk Conceptualizations versus Lung Cancer Screening Shared Decision-Making Conversations with Clinicians: A Qualitative Study.","authors":"Jacqueline H Boudreau, Rendelle E Bolton, Eduardo R Núñez, Tanner J Caverly, Lauren Kearney, Samantha Sliwinski, Abigail N Herbst, Christopher G Slatore, Renda Soylemez Wiener","doi":"10.1177/0272989X241292643","DOIUrl":"10.1177/0272989X241292643","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The Veterans Health Administration (VA) recommends lung cancer screening (LCS), including shared decision making between clinicians and veteran patients. We sought to characterize 1) veteran conceptualization of lung cancer risk and 2) veteran and clinician accounts of shared decision-making discussions about LCS to assess whether they reflect veteran concerns.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We conducted qualitative interviews at 6 VA sites, with 48 clinicians and 34 veterans offered LCS in the previous 6 mo. We thematically analyzed transcripts, focusing on lung cancer risk perceptions, LCS decision making, and patient-clinician conversations.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Three themes emerged. 1) Veterans' lung cancer risk conceptualizations incorporated smoking, occupational hazards, and family history, whereas clinicians focused on smoking as the primary risk factor. 2) Veterans' risk perceptions were influenced by symptoms, recency of exposures, and anecdotes about smoking, cancer, and lung disease, leading some veterans to believe other risk factors outweighed smoking in increasing lung cancer risk. 3) Both veterans and clinicians described LCS conversations centered on smoking, with little mention of other risks.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Limitations: &lt;/strong&gt;Our findings may not reflect non-VA settings; for example, veterans may be more concerned about airborne hazards.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;While airborne hazards strongly influenced veterans' lung cancer risk conceptualizations, clinicians seldom addressed this risk factor during LCS shared decision making, instead focusing on smoking.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Implications: &lt;/strong&gt;In 2022, the US Congress highlighted the link between military toxic exposures and lung cancer risk, requiring VA clinicians to discuss these exposures and conferring automatic VA benefits to exposed veterans with cancer. There is a time-sensitive need for tools to support VA clinicians in discussing military hazards as a lung cancer risk factor, which may result in more engaging, less stigmatizing LCS shared decision-making conversations.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Highlights: &lt;/strong&gt;Veterans' conceptualizations of their lung cancer risk were multifactorial and sometimes ranked exposure to occupational airborne hazards and family history above smoking in increasing lung cancer risk.However, patient-clinician lung cancer screening (LCS) conversations were typically brief and focused on smoking, which could stigmatize patients and failed to engage veterans in discussing what mattered most to them in thinking about their lung cancer risk.These findings are of heightened importance in light of the 2022 Sergeant First Class Heath Robinson Honoring our Promise to Address Comprehensive Toxics (PACT) Act, which requires VA clinicians to discuss toxic military exposures and their relationship to lung cancer and other health conditions.Tools that help clinicians assess and incorporate multiple risk fac","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"86-96"},"PeriodicalIF":3.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142689640","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 Longitudinal Study of the Association of Awareness of Disease Incurability with Patient-Reported Outcomes in Heart Failure.","authors":"Jia Jia Lee, Chetna Malhotra, Kheng Leng David Sim, Khung Keong Yeo, Eric Finkelstein, Semra Ozdemir","doi":"10.1177/0272989X241297694","DOIUrl":"10.1177/0272989X241297694","url":null,"abstract":"<p><strong>Objectives: </strong>To examine awareness of disease incurability among patients with heart failure over 24 mo and its associations with patient characteristics and patient-reported outcomes (distress, emotional, and spiritual well-being).</p><p><strong>Methods: </strong>This study analyzed 24-mo data from a prospective cohort study of 251 patients with heart failure (New York Heart Association class III/IV) recruited from inpatient wards in Singapore General Hospital and National Heart Centre Singapore. Patients were asked to report if their doctor told them they were receiving treatment to cure their condition. \"No\" responses were categorized as being aware of disease incurability, while \"Yes\" and \"Uncertain\" were categorized as being unaware and being uncertain about disease incurability, respectively. We used mixed-effects multinomial logistic regression to investigate the associations between awareness of disease incurability and patient characteristics and mixed-effects linear regressions to investigate associations with patient outcomes.</p><p><strong>Results: </strong>The percentage of patients who were aware of disease incurability increased from 51.6% at baseline to 76.4% at 24-mo follow-up (<i>P</i> < 0.001). Compared with being unaware of disease incurability, being aware was associated with older age (relative risk ratio [RRR] = 1.04; <i>P</i> = 0.005), adequate self-care confidence (RRR = 5.06; <i>P</i> < 0.001), participation in treatment decision making (RRR = 2.13; <i>P</i> = 0.006), higher education (RRR = 2.00; <i>P</i> = 0.033), financial difficulty (RRR = 1.18; <i>P</i> = 0.020), symptom burden (RRR = 1.08; <i>P</i> = 0.001), and ethnicity (<i>P</i> < 0.05). Compared with being unaware of disease incurability, being aware was associated with higher emotional well-being (β = 0.76; <i>P</i> = 0.024), while being uncertain about disease incurability was associated with poorer spiritual well-being (β = -3.16; <i>P</i> = 0.006).</p><p><strong>Conclusions: </strong>Our findings support the importance of being aware of disease incurability, addressing uncertainty around disease incurability among patients with heart failure, and helping patients make informed medical decisions. The findings are important to Asian and other cultures where the prognosis disclosure to terminally ill patients is generally low with an intention to \"protect\" patients.</p><p><strong>Highlights: </strong>Our 24-mo study with heart failure patients showed an increase from 52% to 76% in patients being aware of disease incurability.Compared with being unaware of disease incurability, being aware was associated with higher emotional well-being, while uncertainty about disease incurability was associated with poorer spiritual well-being.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"97-108"},"PeriodicalIF":3.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142640086","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":"Assessing Methods for Adjusting Estimates of Treatment Effectiveness for Patient Nonadherence in the Context of Time-to-Event Outcomes and Health Technology Assessment: A Simulation Study.","authors":"Abualbishr Alshreef, Nicholas Latimer, Paul Tappenden, Simon Dixon","doi":"10.1177/0272989X241293414","DOIUrl":"10.1177/0272989X241293414","url":null,"abstract":"<p><strong>Purpose: </strong>We aim to assess the performance of methods for adjusting estimates of treatment effectiveness for patient nonadherence in the context of health technology assessment using simulation methods.</p><p><strong>Methods: </strong>We simulated trial datasets with nonadherence, prognostic characteristics, and a time-to-event outcome. The simulated scenarios were based on a trial investigating immunosuppressive treatments for improving graft survival in patients who had had a kidney transplant. The primary estimand was the difference in restricted mean survival times in all patients had there been no nonadherence. We compared generalized methods (g-methods; marginal structural model with inverse probability of censoring weighting [IPCW], structural nested failure time model [SNFTM] with g-estimation) and simple methods (intention-to-treat [ITT] analysis, per-protocol [PP] analysis) in 90 scenarios each with 1,900 simulations. The methods' performance was primarily assessed according to bias.</p><p><strong>Results: </strong>In implementation nonadherence scenarios, the average percentage bias was 20% (ranging from 7% to 37%) for IPCW, 20% (8%-38%) for SNFTM, 20% (8%-38%) for PP, and 40% (20%-75%) for ITT. In persistence nonadherence scenarios, the average percentage bias was 26% (9%-36%) for IPCW, 26% (14%-39%) for SNFTM, 26% (14%-36%) for PP, and 47% (16%-72%) for ITT. In initiation nonadherence scenarios, the percentage bias ranged from -29% to 110% for IPCW, -34% to 108% for SNFTM, -32% to 102% for PP, and between -18% and 200% for ITT.</p><p><strong>Conclusion: </strong>In this study, g-methods and PP produced more accurate estimates of the treatment effect adjusted for nonadherence than the ITT analysis did. However, considerable bias remained in some scenarios.</p><p><strong>Highlights: </strong>Randomized controlled trials are usually analyzed using the intention-to-treat (ITT) principle, which produces a valid estimate of effectiveness relating to the underlying trial, but when patient adherence to medications in the real world is known to differ from that observed in the trial, such estimates are likely to result in a biased representation of real-world effectiveness and cost-effectiveness.Our simulation study demonstrates that generalized methods (g-methods; IPCW, SNFTM) and per-protocol analysis provide more accurate estimates of the treatment effect than the ITT analysis does, when adjustment for nonadherence is required; however, even with these adjustment methods, considerable bias may remain in some scenarios.When real-world adherence is expected to differ from adherence observed in a trial, adjustment methods should be used to provide estimates of real-world effectiveness.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"60-73"},"PeriodicalIF":3.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142631358","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":"Multi-indication Evidence Synthesis in Oncology Health Technology Assessment: Meta-analysis Methods and Their Application to a Case Study of Bevacizumab.","authors":"Janharpreet Singh, Sumayya Anwer, Stephen Palmer, Pedro Saramago, Anne Thomas, Sofia Dias, Marta O Soares, Sylwia Bujkiewicz","doi":"10.1177/0272989X241295665","DOIUrl":"10.1177/0272989X241295665","url":null,"abstract":"<p><strong>Background: </strong>Multi-indication cancer drugs receive licensing extensions to include additional indications, as trial evidence on treatment effectiveness accumulates. We investigate how sharing information across indications can strengthen the inferences supporting health technology assessment (HTA).</p><p><strong>Methods: </strong>We applied meta-analytic methods to randomized trial data on bevacizumab, to share information across oncology indications on the treatment effect on overall survival (OS) or progression-free survival (PFS) and on the surrogate relationship between effects on PFS and OS. Common or random indication-level parameters were used to facilitate information sharing, and the further flexibility of mixture models was also explored.</p><p><strong>Results: </strong>Treatment effects on OS lacked precision when pooling data available at present day within each indication separately, particularly for indications with few trials. There was no suggestion of heterogeneity across indications. Sharing information across indications provided more precise estimates of treatment effects and surrogacy parameters, with the strength of sharing depending on the model. When a surrogate relationship was used to predict treatment effects on OS, uncertainty was reduced only when sharing effects on PFS in addition to surrogacy parameters. Corresponding analyses using the earlier, sparser (within and across indications) evidence available for particular HTAs showed that sharing on both surrogacy and PFS effects did not notably reduce uncertainty in OS predictions. Little heterogeneity across indications meant limited added value of the mixture models.</p><p><strong>Conclusions: </strong>Meta-analysis methods can be usefully applied to share information on treatment effectiveness across indications in an HTA context, to increase the precision of target indication estimates. Sharing on surrogate relationships requires caution, as meaningful precision gains in predictions will likely require a substantial evidence base and clear support for surrogacy from other indications.</p><p><strong>Highlights: </strong>We investigated how sharing information across indications can strengthen inferences on the effectiveness of multi-indication treatments in the context of health technology assessment (HTA).Multi-indication meta-analysis methods can provide more precise estimates of an effect on a final outcome or of the parameters describing the relationship between effects on a surrogate endpoint and a final outcome.Precision of the predicted effect on the final outcome based on an effect on the surrogate endpoint will depend on the precision of the effect on the surrogate endpoint and the strength of evidence of a surrogate relationship across indications.Multi-indication meta-analysis methods can be usefully applied to predict an effect on the final outcome, particularly where there is limited evidence in the indication of interest.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"17-33"},"PeriodicalIF":3.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11645851/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142649525","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":"Use of Adaptive Conjoint Analysis-Based Values Clarification in a Patient Decision Aid Is Not Associated with Better Perceived Values Clarity or Reduced Decisional Conflict but Enhances Values Congruence.","authors":"Nida Gizem Yılmaz, Arwen H Pieterse, Danielle R M Timmermans, Annemarie Becker, Birgit Witte-Lissenberg, Olga C Damman","doi":"10.1177/0272989X241298630","DOIUrl":"10.1177/0272989X241298630","url":null,"abstract":"<p><strong>Background: </strong>Evidence is lacking on the most effective values clarification methods (VCMs) in patient decision aids (PtDAs). We tested the effects of an adaptive conjoint analysis (ACA)-based VCM compared with a ranking-based VCM and no VCM on several decision-related outcomes, with the decisional conflict and its subscale \"perceived values clarity\" as primary outcomes.</p><p><strong>Design: </strong>Online experimental study with 3 conditions: no VCM versus ranking-based VCM versus <i>ACA</i>-based VCM (<i>N</i> = 282; <i>M_age</i> = 63.11 y, <i>s</i> = 12.12), with the latter 2 conditions including attributes important for a lung cancer treatment decision. We assessed 1) decisional conflict, 2) perceived values clarity (decisional conflict subscale), 3) perceived cognitive load, 4) anticipated regret, 5) ambivalence, 6) preparedness for decision making, 7) hypothetical treatment preference, and 8) values congruence (proxy). We performed analysis of covariance and linear regression. Age and level of deliberation were included as potential moderators, and we controlled for subjective numeracy (covariate). We exploratively tested the moderating effects of subjective numeracy and health literacy (without covariates).</p><p><strong>Results: </strong>We found no significant effect of type of VCM on overall decisional conflict or perceived values clarity. Age had a moderating effect: in younger participants, no VCM (v. ranking-based VCM) led to more values clarity, while in older participants, a ranking-based VCM (v. no VCM) led to more values clarity. Completing the ACA-based VCM, compared with no VCM, resulted in more values congruence.</p><p><strong>Limitations: </strong>The hypothetical choice situation might have induced lower levels of cognitive/affective involvement in the decision.</p><p><strong>Conclusions: </strong>This study found mixed effects of an ACA-based VCM. It did not decrease decisional conflict or increase perceived values clarity, yet it did improve values congruence.</p><p><strong>Implications: </strong>Completion of an ACA-based VCM in a PtDA may increase values congruence.</p><p><strong>Highlights: </strong>An adaptive conjoint analysis or a ranking-based values clarification method did not decrease analog patients' decisional conflict nor did it increase their perceived values clarity.In younger participants, no VCM (v. ranking-based VCM) led to more values clarity, while in older participants, a ranking-based VCM (v. no VCM) led to more values clarity.An adaptive conjoint analysis task for values clarification resulted in more values congruence.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"109-123"},"PeriodicalIF":3.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11645850/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142649526","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}