Medical Decision Making[JOURNAL]
Sun
200 papers
A Scoping Review on Calibration Methods for Cancer Simulation Models.
Zhang Y, Lipa N, Alagoz O
Med Decis Making · 2025 Nov · PMID 40790869 · Full text
Calibration, a critical step in the development of simulation models, involves adjusting unobservable parameters to ensure that the outcomes of the model closely align with observed target data. This process is particula...
Calibration, a critical step in the development of simulation models, involves adjusting unobservable parameters to ensure that the outcomes of the model closely align with observed target data. This process is particularly vital in cancer simulation models with a natural history component, where direct data to inform natural history parameters are rarely available. We conducted a scoping review of studies published from 1980 to August 11, 2024, using keyword searches in PubMed and Web of Science. Eligible studies included cancer simulation models with a natural history component that used calibration methods for parameter estimation. A total of 117 studies met the inclusion criteria. Nearly all studies ( = 115) specified calibration targets, while most studies ( = 91) described the parameter search algorithms used. Goodness-of-fit metrics ( = 87), acceptance criteria ( = 53), and stopping rule ( = 46) were reported less frequently. The most commonly used calibration targets were incidence, mortality, and prevalence, typically drawn from cancer registries and observational studies. Mean squared error was the most commonly used goodness-of-fit measure. Random search was the predominant method for parameter search, followed by the Bayesian approach and the Nelder-Mead method. Despite recent advances in machine learning, such algorithms remain underutilized in the calibration of cancer simulation models. Further research is needed to compare the efficiency of different parameter search algorithms used for calibration.HighlightsThis work reviewed the literature of cancer simulation models with a natural history component and identified the calibration approaches used in these models with respect to the following attributes: cancer type, calibration target data source, calibration target type, goodness-of-fit metrics, search algorithms, acceptance criteria, stopping rule, computational time, modeling approach, and model stochasticity.Random search has been the predominant method for parameter search, followed by Bayesian approach and Nelder-Mead method.Machine learning-based algorithms, despite their fast advancement in the recent decade, have been underutilized in the cancer simulation models. Furthermore, more research is needed to compare different parameter search algorithms used for calibration.
Investigating Bias in the Evaluation Model Used to Evaluate the Effect of Breast Cancer Screening: A Simulation Study.
Røssell EL, Viuff JH, Lousdal ML … +1 more , Støvring H
Med Decis Making · 2025 Nov · PMID 40790859 · Publisher ↗
Observational studies are used to evaluate the effect of breast cancer screening programs, but their validity depends on use of different study designs. One of these is the evaluation model, which extends follow-up after...
Observational studies are used to evaluate the effect of breast cancer screening programs, but their validity depends on use of different study designs. One of these is the evaluation model, which extends follow-up after screening only if women have been diagnosed with breast cancer during the screening program. However, to avoid lead-time bias, the inclusion of risk time should be based on screening invitation and not breast cancer diagnosis. The aim of this study is to investigate potential bias induced by the evaluation model. We used large-scale simulated datasets to investigate the evaluation model. Simulation model parameters for age-dependent breast cancer incidence, survival, breast cancer mortality, and all-cause mortality were obtained from Norwegian registries. Data were restricted to women aged 48 to 90 y and a period before screening implementation, 1986 to 1995. Simulation parameters were estimated for each of 2 periods (1986-1990 and 1991-1995). For the simulated datasets, 50% were randomly assigned to screening and 50% were not. Simulation scenarios depended on the magnitude of screening effect and level of overdiagnosis. For each scenario, we applied 2 study designs, the evaluation model and ordinary incidence-based mortality, to estimate breast cancer mortality rates for the screening and nonscreening groups. For each design, these rates were compared to assess potential bias. In scenarios with no screening effect and no overdiagnosis, the evaluation model estimated 6% to 8% reductions in breast cancer mortality due to lead-time bias. Bias increased with overdiagnosis. The evaluation model was biased by lead time, especially in scenarios with overdiagnosis. Thus, the attempt to capture more of the screening effect using the evaluation model comes at the risk of introducing bias.HighlightsThe validity of observational studies of breast cancer screening programs depends on their study design being able to eliminate lead-time bias.The evaluation model has been used to evaluate breast cancer screening in recent studies but introduces a study design based on breast cancer diagnosis that may introduce lead-time bias.We used large-scale simulated datasets to compare study designs used to evaluate screening.We found that the evaluation model was biased by lead time and estimated reductions in breast cancer mortality in scenarios with no screening effect.
Development of a Tool to Measure the Dyadic Process of Shared Decision Making in Young Children: The Making Decisions for Kids (MADE for Kids) Survey.
Opel DJ, Ayala E, Spielvogle H … +7 more , Ibrahim A, Orr O, Beretta A, Kroshus E, Weiss EM, Zhou C, Shah SK
Med Decis Making · 2025 Oct · PMID 40781752 · Full text
BackgroundIncorporating clinician and patient perspectives in the measurement of shared decision making (SDM) is aligned with SDM's inherently dyadic nature. There are no tools measuring SDM in pediatrics from multiple p...
BackgroundIncorporating clinician and patient perspectives in the measurement of shared decision making (SDM) is aligned with SDM's inherently dyadic nature. There are no tools measuring SDM in pediatrics from multiple perspectives. The objective of this study was to develop a tool to measure SDM from the perspectives of both clinicians and parents of young children.DesignWe used a stepwise iterative approach to tool development beginning with de novo item generation and followed by augmentation of the item pool by adapting items from existing instruments. After the study team removed redundant items, 3 parents and 3 SDM experts rated the remaining items for their ability to capture SDM in pediatrics; items with the lowest mean ratings were removed. To assess the preliminary tool's face validity, usability, and item understandability, we pretested it, revising it iteratively, with sequential cohorts of English-speaking parents and clinicians from 2 US children's hospitals.ResultsWe generated an initial list of 28 items for the parent and clinician versions of the tool, which we reduced to 20 items after preliminary review. After review by parents and SDM experts, we cut 9 items and added 1 additional item for a total of 12 items. We pretested the preliminary tool with 31 clinicians and 30 parents across 3 sequential cohorts. The final tool contained 12 items and an optional free-text item.LimitationsAll participants were English speaking, limiting its generalizability.ConclusionsWe have developed a usable preliminary tool for measuring the dyadic process of SDM in pediatrics.ImplicationsThis tool represents an important first step toward addressing the measurement of SDM in pediatrics from multiple perspectives.HighlightsIn this study, we have developed the first shared decision making (SDM) tool specifically for use with parents of young children.Although further study is needed to determine the psychometric properties of this tool, it has the potential to address an important gap in our ability to measure SDM in pediatrics from multiple perspectives.
Modeling the Impact of Multicancer Early Detection Tests: A Review of Natural History of Disease Models.
Mandrik O, Whyte S, Kunst N … +6 more , Rayner A, Harden M, Dias S, Payne K, Palmer S, Soares MO
Med Decis Making · 2025 Nov · PMID 40753481 · Full text
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...
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.
Patients' Attitude toward Less Frequent Surveillance of Low-Risk Pancreatic Cysts: A Multicenter European Cohort Study.
Sprij MLJA, de Kok IMCM, Nieboer DD … +16 more , Capurso G, Meziani J, Wielenga MCB, van der Ende MCM, Smits ME, Casadei R, Schwartz MP, van Vilsteren FGI, Hoge C, Quispel R, Honkoop P, van der Waaij LA, Rossi G, Tan ACITL, Bruno MJ, Cahen DL
Med Decis Making · 2025 Oct · PMID 40753475 · Full text
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 individ...
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 ( = 185) and Italy ( = 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.
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.
Trouiller JB, Quenéchdu A, Toumi M … +2 more , Boyer L, Laramée P
Med Decis Making · 2025 Nov · PMID 40751431 · Publisher ↗
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 reimburs...
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.
Process for Rapid Co-development of a Decision Aid Prototype for Population-wide Cancer Screening.
Assan OQ, Uwizeye CB, Zomahoun HTV … +8 more , Nduwimana O, Dubuisson W, Sillon G, Bergeron D, Groulx S, Deck W, Giguère A, Légaré F
Med Decis Making · 2025 Oct · PMID 40657730 · Full text
Decision aids (DA) are more likely to be adopted if co-developed with stakeholders and culturally adapted. Using the DEVELOPTOOLS Reporting Checklist, we describe a process for rapid co-development of a culturally adapte...
Decision aids (DA) are more likely to be adopted if co-developed with stakeholders and culturally adapted. Using the DEVELOPTOOLS Reporting Checklist, we describe a process for rapid co-development of a culturally adapted DA prototype for population-wide cancer-screening programs. Our systematic, collaborative, and iterative methodology had 7 phases: 1) set up the process by adopting best governance practices (e.g., identify and engage stakeholders, adapt our collaborative DA design process, validate development process), with governance comprising 20 individuals from a wide range of sectors including at least 2 citizens; 2) identify and analyze existing DAs relevant to the cancerscreening of interest by conducting a systematic review; 3) share results with stakeholders and make recommendations; 4) formulate Quebec-specific DA content and consult stakeholders including users by conducting e-Delphi surveys; 5) co-design a prototype with stakeholders, including users, following international DA standards; 6) translate the DA using translation-back translation approaches and deploy; and 7) knowledge mobilization (KMb) using end-of-grant and integrated KMb activities. Using the User-Centred Design 11-Item Measure (UCD-11), our proposed process scored 10 of 11 on the UCD-11. Overall, we expect this new co-developed process to ensure that good-quality, user-centered, and culturally adapted DAs for cancer screening are produced within reasonable timeframes. We also expect it to foster the adoption of the DAs.HighlightsWe report on a 7-step process for collaborating with various stakeholders to create a culturally adapted decision aid (DA) prototype for deciding about cancer screening in Quebec, Canada.The process includes: ○ Making sure the DA prototype design includes users and other interested parties and reflects their needs, perceptions, values, and preferences.○ Finding and analyzing existing DAs on cancer screening to decide what ours should include○ Respecting international standards and criteria for DA design○ Repeated rounds of expert consensus about the exact content, with revisions between each roundThis method could help the rapid creation of DAs shaped by users' interests and will ultimately encourage shared decision making.
Weight Status Transitions and Validation of an Obesity Model for Aboriginal and Torres Strait Islander Children and Adolescents.
Lung T, Killedar A, Howard K … +6 more , Wen LM, Kelly P, Dickson M, Sherriff S, Baur L, Hayes A
Med Decis Making · 2025 Nov · PMID 40652352 · Full text
ObjectivesAboriginal and Torres Strait Islander children and adolescents are at higher risk of overweight and obesity, highlighting an inequitable public health concern. The aim of this study was to estimate transition p...
ObjectivesAboriginal and Torres Strait Islander children and adolescents are at higher risk of overweight and obesity, highlighting an inequitable public health concern. The aim of this study was to estimate transition probabilities and validate a model predicting the epidemiologic trajectory of overweight and obesity in Australian Aboriginal and Torres Strait Islander children.MethodsAn individual-level state-transition Markov model was developed to model transitions between healthy weight, overweight, and obesity for Aboriginal and Torres Strait Islander children aged between 2 and 14 y. Age-specific annual transition probabilities were derived from semi-parametric survival analyses using the Longitudinal Study of Indigenous Children. The model used annual cycles over a 12-y time horizon, and the epidemiological predictions of the model were validated using both internal and external data, according to best practice guidelines. The starting age of the model was 2 to 4 y and 4 to 5 y for the internal and external validation, respectively. Aboriginal and Torres Strait Islander children from the Longitudinal Study of Australian Children were used as the external validation cohort.ResultsA total of 1,643 children with 11,514 complete anthropometric measurements were used to estimate transition probabilities. The model predictions showed both good internal and external validity, with most predictions falling within the 95% confidence intervals of measured data. The model was able to reliably capture the epidemiology of overweight and obesity prevalence in early childhood.ConclusionsOur model predictions showed good internal and external validity, ensuring our model is fit for purpose to use to evaluate Aboriginal and Torres Strait Islander-led programs to achieve a healthy weight.HighlightsAboriginal and Torres Strait Islander children experience high rates of overweight and obesity; hence, there is a need for high-quality evidence on both effectiveness and cost-effectiveness of Aboriginal and Torres Strait Islander-led childhood obesity prevention programs to ensure they offer value for money.This is the first study to develop and validate a predictive model using anthropometric data from Aboriginal and Torres Strait Islander children to inform decision making on childhood obesity programs.Our model predictions showed good internal and external validity, ensuring our model is fit for purpose to use to evaluate Aboriginal and Torres Strait Islander-led programs to achieve a healthy weight.The model provides a framework to assist policy makers in identifying when best to intervene in childhood as well as the most effective approaches for maintaining a healthy weight for Aboriginal and Torres Strait Islander children.
Do Caregivers of Asian Patients with Advanced Cancer Help or Hinder Patient Understanding of Illness and Involvement in Decision Making?
Ozdemir S, Chaudhry I, Malhotra C … +2 more , Van Houtven C, Finkelstein EA
Med Decis Making · 2025 Oct · PMID 40652351 · Publisher ↗
PurposeIt is unclear whether caregivers help or hinder patients' involvement in decision making and understanding of illness. We thus investigated the extent to which caregivers' preferred level of patient involvement in...
PurposeIt is unclear whether caregivers help or hinder patients' involvement in decision making and understanding of illness. We thus investigated the extent to which caregivers' preferred level of patient involvement in decision making and understanding of treatment goals are associated with those of patients.MethodsWe used survey data from 229 patients with metastatic cancer and their family caregivers living in Singapore spanning 2 y prior to the patient's death. We used mixed-effects regressions to investigate the associations between 1) caregiver-preferred level of patient involvement () and patient preferred and perceived level of involvement in decision making at subsequent assessments () and 2) patient and caregiver understanding of treatment goals at the same assessments ().ResultsCaregivers who preferred higher levels of patient involvement in decision making at were more likely to have patients who also preferred higher levels of involvement (odds ratio [OR] = 1.19; = 0.03) and who perceived experiencing higher levels of involvement in decision making (OR = 1.24; < 0.01) at . Compared with an uncertain understanding of treatment goals, caregivers who had an inaccurate understanding at were more likely to have patients who had an inaccurate understanding (relative risk ratio [RRR] = 8.56; = 0.03), and caregivers who had an accurate understanding at were more likely to have patients with an accurate understanding (RRR = 3.02; = 0.01) at .ConclusionOur findings suggest that caregiver preferences for patient involvement in decision making and understanding of treatment goals are significantly associated with those of patients. Enhancing caregiver education and involvement may be pivotal in improving patient participation and comprehension in the context of metastatic cancer care.HighlightsPatients of caregivers who preferred higher levels of patient involvement in decision making at earlier time points were more likely to prefer and experience higher levels of involvement in decision making at subsequent assessments.Patients of caregivers with an inaccurate understanding of treatment goals were more likely to have an inaccurate understanding, while patients of caregivers with an accurate understanding were also more likely to have an accurate understanding.
Population Preferences for Treatment in Life-Limiting Illness: Valuing the Way Time Is Spent at the End of Life.
Kenny P, Street DJ, Hall J
Med Decis Making · 2025 Oct · PMID 40637171 · Full text
IntroductionSocietal preferences over different health states are used to guide service planning, but there has been little investigation of treatment preferences at the end of life. This study aimed to examine populatio...
IntroductionSocietal preferences over different health states are used to guide service planning, but there has been little investigation of treatment preferences at the end of life. This study aimed to examine population preferences for active treatment or palliation for cancer patients when life expectancy is limited and the relative importance of time spent in hospital or with functional limitation.MethodsWe used a discrete choice experiment that presented respondents with a series of hypothetical patients who had died, describing their last few months of life. Respondents selected the end-of-life alternative they thought best. Data were collected from 1,502 Australian adults participating in an online survey panel. Latent class analysis was used to identify groups with different preference patterns.ResultsFour preference groups were identified along with an additional group that we termed , as they appeared to respond at random. Among the 1,070 respondents assigned to 1 of the 4 preference groups, 33.5% favored longer overall survival regardless of how that time was spent; 26.1% were willing to accept a shorter survival time for less time in the hospital or completely incapacitated at home, and they had a stronger preference for palliative care in older patients; 22.5% strongly supported the use of palliative care regardless of the age of the patients, preferring less time in the hospital or time at home with any functional limitations; and 17.9% had a strong preference to not use palliative care.ConclusionsOur results show distinct heterogeneity in population preferences for end-of-life care. Policy goals and service planning should acknowledge this heterogeneity and provide end-of-life support services that offer the flexibility to enhance patient choice. Many current funding approaches are not consistent with the philosophy of patient-centered care. Policy makers can and should be exploring innovative approaches to improve efficiency and equity.HighlightsSocial preferences, based on a general population survey, vary across palliative and active care approaches.Preferences for palliative care and willingness to tolerate time in hospital and time at home with activity limitations varied within the groups willing to trade quality and quantity of life.Policy, resource allocation, and funding methods should accommodate this variability.
Evidence on Methods for Communicating Health-Related Probabilities: Comparing the Making Numbers Meaningful Systematic Review to the 2021 IPDAS Evidence Paper Recommendations.
Zikmund-Fisher BJ, Benda NC, Ancker JS
Med Decis Making · 2025 Oct · PMID 40621897 · Full text
PurposeTo summarize the degree to which evidence from our recent Making Numbers Meaningful (MNM) systematic review of the effects of data presentation format on communication of health numbers supports recommendations fr...
PurposeTo summarize the degree to which evidence from our recent Making Numbers Meaningful (MNM) systematic review of the effects of data presentation format on communication of health numbers supports recommendations from the 2021 International Patient Decision Aids Standards (IPDAS) Collaboration papers on presenting probabilities.MethodsThe MNM review generated 1,119 distinct findings (derived from 316 papers) related to communication of probabilities to patients or other lay audiences, classifying each finding by its relation to audience task, type of stimulus (data and data presentation format), and up to 10 distinct sets of outcomes: identification and/or recall, contrast, categorization, computation, probability perceptions and/or feelings, effectiveness perceptions and/or feelings, behavioral intentions or behavior, trust, preference, and discrimination. Here, we summarize the findings related to each of the 35 IPDAS paper recommendations.ResultsStrong evidence exists to support several IPDAS recommendations, including those related to the use of part-to-whole graphical formats (e.g., icon arrays) and avoidance of verbal probability terms, 1-in-X formats, and relative risk formats to prevent amplification of probability perceptions, effectiveness perceptions, and/or behavioral intentions as well as the use of consistent denominators to improve computation outcomes. However, the evidence base appears weaker and less complete for other IPDAS recommendations (e.g., recommendations regarding numerical estimates in context and evaluative labels). The IPDAS papers and the MNM review agree that both communication of uncertainty and use of interactive formats need further research.ConclusionsThe idea that no one visual or numerical format is optimal for every probability communication situation is both an IPDAS panel recommendation and foundational to the MNM project's design. Although no MNM evidence contradicts IPDAS recommendations, the evidence base needed to support many common probability communication recommendations remains incomplete.HighlightsThe Making Numbers Meaningful (MNM) systematic review of the literature on communicating health numbers provides mixed support for the recommendations of the 2021 International Patient Decision Aids Standards (IPDAS) evidence papers on presenting probabilities in patient decision aids.Both the IPDAS papers and the MNM project agree that no single visual or numerical format is optimal for every probability communication situation.The MNM review provides strong evidentiary support for IPDAS recommendations in favor of using part-to-whole graphical formats (e.g., icon arrays) and consistent denominators.The MNM review also supports the IPDAS cautions against verbal probability terms and 1-in-X formats as well as its concerns about the potential biasing effects of relative risk formats and framing.MNM evidence is weaker related to IPDAS recommendations about placing numerical estimates in context and use of evaluative labels.
An Experimental Investigation of Treatment Decisions under Ambiguity and Conflict.
Shou Y, Gulliver A, Farrer LM … +3 more , Dawel A, Newman E, Smithson M
Med Decis Making · 2025 Oct · PMID 40613384 · Publisher ↗
IntroductionEffective risk communication is essential for medical professionals to assist patients to make informed decisions. However, risk communication can be challenging as patients receive ambiguous and conflicting...
IntroductionEffective risk communication is essential for medical professionals to assist patients to make informed decisions. However, risk communication can be challenging as patients receive ambiguous and conflicting information.ObjectivesThis study aimed to examine how uncertainty influences individuals' perceptions and preferences and interacts with message framing in a medical treatment decision scenario.MethodsThe present study included a large representative sample of Australians ( = 805). A randomized experiment was conducted presenting a scenario about hypothetical COVID-19 treatment alternatives with varying uncertainty and framing in treatment information.ResultsThe results showed that conflicting information and loss framing had deleterious effects on participants' willingness to take a treatment and trust in the sources providing the information, compared with information that was precise, ambiguous, or in a gain frame. The effects could be stronger among participants who are risk averse, anxious, and native language speakers.ConclusionThe findings highlight that patients may be more averse to a treatment option and reduce their trust in medical professionals when they are provided with ambiguous information and particularly when information that conflicts with other sources including other medical professionals. It is important for medical professionals to be aware of other information patients have sourced that may conflict with information provided by the medical professionals during the consultation and to assist patients with high levels of risk aversion and anxiety in their decision making.HighlightsConflicting information and loss framing had deleterious effects on participants' willingness to take a treatment.Conflicting information and loss framing also reduced participants' trust in the sources of the information.The deleterious effects were stronger among participants who were native language speakers and were risk averse and anxious.
The Impact of Machine Learning Mortality Risk Prediction on Clinician Prognostic Accuracy and Decision Support: A Randomized Vignette Study.
Parikh RB, Ferrell WJ, Girard A … +4 more , White J, Fang S, Bekelman JE, Schapira MM
Med Decis Making · 2025 Aug · PMID 40613383 · Full text
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 evaluat...
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, < 0.001). ML outputs using an absolute presentation strategy alone (mean change 27.4%, 95% 16.8-38.1, < 0.001) or with reference dependence (mean change 33.4%, 95% 23.9-42.8, < 0.001) improved accuracy, but reference dependence alone did not (mean change 2.0% [95% CI -11.1 to 15.0], = 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%, respectively).ML-based prognostic assessments without explanations improve prognostic accuracy but do not change decisions around palliative care referral or advance care planning.
Organ Donation Decisions: When Deviating from the Status Quo Heightens Perceived Vulnerability.
Motsenok M, Kogut T
Med Decis Making · 2025 Oct · PMID 40613359 · Full text
BackgroundResearch suggests that the method used to determine voluntary consent (i.e., opt-in versus opt-out policies) greatly affects the number of registered organ donors in various countries. Although the concept of o...
BackgroundResearch suggests that the method used to determine voluntary consent (i.e., opt-in versus opt-out policies) greatly affects the number of registered organ donors in various countries. Although the concept of organ transplantation is broadly supported, the relatively low percentage of registered donors in opt-in countries is puzzling. We suggest that deviating from the status quo (such as signing an organ donor card in opt-in countries or removing oneself from the list of registered donors in opt-out countries) heightens one's sense of vulnerability.DesignWe examined our prediction in 2 online experiments involving participants from the United States (studies 1 and 2), which has an opt-in organ-donation policy, and from the United Kingdom (study 2), a country that has recently changed its policy to opt out.ResultsIn study 1, registered organ donors perceived their vulnerability as greater after being reminded of their decision, but vulnerability perceptions were not affected by such a reminder among nondonors who upheld the status quo. In study 2, imagining oneself making an organ donation decision that deviates from the status quo (signing a commitment under an opt-in policy or removing oneself from the registered donors list under an opt-out policy) increased participants' perceived personal vulnerability.ConclusionsThe decision to become an organ donor may affect individuals' sense of physical vulnerability, depending on their country's donation policy. Potentially, deviating from the status quo may curtail willingness for organ donation. Understanding the psychological barriers to organ donation may help overcome them by presenting the issue in a manner that takes such perceptions into account. We recommend future research to explore whether this heightened sense of vulnerability potentially deters organ donation in opt-in countries.HighlightsThe decision to become an organ donor may affect individuals' sense of physical vulnerability, depending on their country's donation policy (opt in versus opt out).Registered organ donors perceived their vulnerability as greater after being reminded of their decision, but vulnerability perceptions were not affected by such a reminder among nondonors who upheld the status quo.Imagining oneself making an organ donation decision that deviates from the status quo (signing a commitment under an opt-in policy or removing oneself from the registered donors list under an opt-out policy) increased participants' perceived personal vulnerability.Future research is needed to examine whether this heightened sense of vulnerability affects actual organ donation decisions.
Facilitators and Barriers of the Use of Prognostic Models for Clinical Decision Making in Acute Neurologic Care: A Systematic Review.
Hu EXY, van Hoorn ES, Retel Helmrich IRA … +3 more , Muehlschlegel S, Rietjens JAC, Lingsma HF
Med Decis Making · 2025 Aug · PMID 40581849 · Full text
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 insufficie...
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 ( = 9), reassurance of clinical judgment ( = 6) perceived improved patient outcomes ( = 4), standardization of care ( = 4), resource optimization ( = 3), and extension of clinical knowledge ( = 3). Barriers included perceived misinterpretation during risk communication ( = 3), mistrust in data ( = 3), perceived reduction of clinicians' autonomy ( = 3), and ethical considerations ( = 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.
STEER: Open Access Resources for Conducting Structured Expert Elicitation for Health Care Decision Making.
Jankovic D, Horscroft J, Lee D … +2 more , Bojke L, Soares M
Med Decis Making · 2025 Aug · PMID 40567045 · Publisher ↗
In a landscape of accelerated approvals and a less mature evidence base, constrained health systems make reimbursement decisions based on uncertain evidence about the expected clinical and economic value of a health tech...
In a landscape of accelerated approvals and a less mature evidence base, constrained health systems make reimbursement decisions based on uncertain evidence about the expected clinical and economic value of a health technology. Uncertain decisions require expert judgments, and there has recently been a drive to improve the accountability and transparency in the way these judgments are collected and reported. Structured expert elicitation (SEE) refers to formal methods to quantify experts' judgments. Protocols for conducting SEE exist; however, the time and resource requirements of SEE and the lack of simple tools for its implementation are potential deterrents to its implementation. This article describes the development of Structured Expert Elicitation Resources (STEER), a collection of open access resources based on a published protocol for SEE specific to the health care decision-making (HCDM) setting. The resources cover the entire SEE process from design to reporting. The resources include an overview and a practical guide for conducting SEE in this setting, adaptable tools for building bespoke SEE exercises, training materials for experts taking part in SEE, resources used in previous SEE exercises, and examples of published SEE in HCDM. The materials cover practical considerations such as timelines team skills requirements, and administrative requirements such as contracting. The use of off-the-shelf resources can streamline the SEE process in HCDM while maintaining robustness.HighlightsThere is a drive to improve accountability and transparency in the way expert judgments are used in health care decision making; however, the time and resource requirements of SEE and the lack of simple tools for its implementation are potential deterrents to its implementation.Structured Expert Elicitation Resources (STEER) is a collection of open access resources for conducting SEE in health care decision making, based on a published methods protocol for SEE specific to this setting.The use of off-the-shelf resources can streamline the SEE process in health care decision making while maintaining robustness.
Impact of Limited Sample Size and Follow-up on Partitioned Survival and Multistate Modeling-Based Health Economic Models: A Simulation Study.
Beca JM, Chan KKW, Naimark DMJ … +1 more , Pechlivanoglou P
Med Decis Making · 2025 Aug · PMID 40560693 · Full text
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...
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.
Association between Exposure to Statin Choice and Adherence to Statins: An Observational Cohort Study.
Martinez KA, Montori VM, Rodriguez F … +5 more , Tereshchenko LG, Kovach JD, Boyer C, Hurwitz HM, Rothberg MB
Med Decis Making · 2025 Oct · PMID 40553465 · Full text
BackgroundStatin Choice is a shared decision-making encounter tool embedded in the electronic health record.ObjectiveTo describe the association between the use of Statin Choice, statin prescriptions by clinicians, presc...
BackgroundStatin Choice is a shared decision-making encounter tool embedded in the electronic health record.ObjectiveTo describe the association between the use of Statin Choice, statin prescriptions by clinicians, prescription fills (primary adherence), and statin adherence at 12 mo (secondary adherence).DesignObservational cohort study at the Cleveland Clinic Health System.SubjectsStatin-naïve adults aged 40 to 75 y with a 10-y atherosclerotic cardiovascular disease (ASCVD) risk of ≥5% and a primary care appointment between January 2020 and July 2021.Main MeasuresThe primary exposure was the use of Statin Choice during a clinical encounter. We measured whether the use of Statin Choice was associated with statin prescriptions. We measured statin adherence based on pharmacy fill data at 60 d (primary adherence) and 12 mo (secondary adherence). We used mixed-effects logistic regression to estimate the adjusted odds of statin prescriptions and adherence at the 3 time points by the use of Statin Choice.Key ResultsAmong 17,001 statin-naïve patients, 13% viewed Statin Choice and 7% were prescribed a statin. The median ASCVD risk was 10%. Patients who were shown Statin Choice had 9.04 higher odds of being prescribed a statin compared with patients not shown Statin Choice (95% confidence interval [CI]: 7.86-10.4). Among patients prescribed a statin, the use of Statin Choice was associated with 5.75 higher odds of primary adherence compared with usual care (95% CI: 4.22-7.83). At 12 mo, Statin Choice use was significantly associated with adherence in the unadjusted analysis (OR: 1.58; 95% CI: 1.05-2.08) but was not significant after adjustment for patient factors. Patients shown Statin Choice had an average of 12 mg/dL reduction in low-density lipoprotein cholesterol at 12 mo (95% CI: -16 mg/dL, -10) compared with those not shown Statin Choice.ConclusionIn this observational study, Statin Choice use was strongly associated with statin prescription and fills and weakly associated with adherence to statins for up to 1 y. A randomized trial is needed to confirm causality.HighlightsStatin Choice is an electronic health record-embedded shared decision-making encounter tool available for free in many health care systems.Small randomized controlled trials have found modest associations between the use of Statin Choice and statin adherence using patient-reported data.In our large study using pharmacy fill data, clinician use of Statin Choice during a medical encounter was associated with significantly greater patient adherence with statins up to 1 y later.Exposure to Statin Choice was associated with a significant reduction in low-density lipoprotein cholesterol over 1 y.
Forewarning Artificial Intelligence about Cognitive Biases.
Wang J, Redelmeier DA
Med Decis Making · 2025 Oct · PMID 40553457 · Full text
Artificial intelligence models display human-like cognitive biases when generating medical recommendations. We tested whether an explicit forewarning, "Please keep in mind cognitive biases and other pitfalls of reasoning...
Artificial intelligence models display human-like cognitive biases when generating medical recommendations. We tested whether an explicit forewarning, "Please keep in mind cognitive biases and other pitfalls of reasoning," might mitigate biases in OpenAI's generative pretrained transformer large language model. We used 10 clinically nuanced cases to test specific biases with and without a forewarning. Responses from the forewarning group were 50% longer and discussed cognitive biases more than 100 times more frequently compared with responses from the control group. Despite these differences, the forewarning decreased overall bias by only 6.9%, and no bias was extinguished completely. These findings highlight the need for clinician vigilance when interpreting generated responses that might appear seemingly thoughtful and deliberate.HighlightsArtificial intelligence models can be warned to avoid racial and gender bias.Forewarning artificial intelligence models to avoid cognitive biases does not adequately mitigate multiple pitfalls of reasoning.Critical reasoning remains an important clinical skill for practicing physicians.
How to Report Research on the Communication of Health-Related Numbers: The Research on Communicating Numbers (ReCoN) Guidelines.
Benda NC, Zikmund-Fisher BJ, Ancker JS
Med Decis Making · 2025 Oct · PMID 40553451 · Full text
BackgroundResearch with lay audiences (e.g., patients, the public) can inform the communication of health-related numerical information. However, a recent systematic review (Making Numbers Meaningful) highlighted several...
BackgroundResearch with lay audiences (e.g., patients, the public) can inform the communication of health-related numerical information. However, a recent systematic review (Making Numbers Meaningful) highlighted several common issues in the literature that impair readers' ability to evaluate and replicate these studies.PurposeTo create a set of guidelines for reporting research regarding the research on communicating numbers to lay audiences for health-related purposes.Reporting RecommendationsWe present 6 common reporting issues from research on communicating numbers that pertain to the background motivating the study, experimental design and analysis reporting, description of the outcomes, and reporting of the data presentation formats. To address these issues, we propose a set of 7 reporting guidelines including 1) specifying how study objectives address a gap in evidence on research on communicating numbers, 2) clearly reporting all combinations of data presentation formats (experimental conditions) compared, 3) providing verbatim examples of the data that were presented to the audience, 4) describing whether or not participants had access to the data presentation formats while outcomes were assessed, 5) reporting the wording of all outcome measures, 6) using standardized terms for both outcomes and data presentation formats, and 7) ensuring that broad outcome concepts such as gist, comprehension, or knowledge are concretely defined.ConclusionsFuture studies involving research on communicating health-related numbers should use these guidelines to improve the quality of reporting and ease of evidence synthesis in future efforts.HighlightsOur systematic review allowed us to exhaustively identify and enumerate several common reporting issues from research on communicating numbers that make it challenging to synthesize evidence.Reporting issues involved not including the background motivating the gap the study addresses, insufficiently describing experimental designs and analyses, and failing to report information regarding the outcomes measured.We propose 7 reporting guidelines for future research on communicating numbers to address the issues detected:1. Specification of how study objectives address a gap in evidence on research communicating numbers2. Clearly reporting all combinations of data presentation format elements compared3. Providing verbatim examples of the data presentation formats4. Describing whether participants had access to the data presentation formats while outcomes were assessed5. Reporting the wording of all outcome measures6. Using standardized terms for both outcomes and data presentation formats7. Ensuring that broad outcome concepts such as gist, comprehension, or knowledge are concretely definedImplementation of these guidelines will facilitate knowledge synthesis of research on communicating numbers and support creating evidence-based guidelines of best practices for communicating health-related numbers to lay audiences.
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