Medical Decision Making[JOURNAL]
Sun
200 papers
Health State Values Should Not Be Used as Minimal Important Differences.
Parkin D
Med Decis Making · 2026 Feb · PMID 41194771 · Publisher ↗
This article critically examines the application of minimal important differences (MIDs) to health state values or utilities. The concept of MIDs aims to guide clinical and research decisions by identifying important cha...
This article critically examines the application of minimal important differences (MIDs) to health state values or utilities. The concept of MIDs aims to guide clinical and research decisions by identifying important changes in health-related quality-of-life (HRQoL) indicators. However, this cannot be used without additional information not contained within the indicator itself, so that the MID cannot be regarded as a property of the indicator. First, MIDs defined at the individual patient level cannot be meaningfully aggregated for groups without additional context. Second, any improvement in HRQoL is important for patients themselves, so decision making using an MID also requires context, such as resource costs for effecting change. Third, health state values incorporate a measure of importance according to patient preferences, so the only change that is unimportant is zero. Calculating and reporting MIDs for health state values is not only unhelpful but also misleading.HighlightsThe minimal important difference (MID) for health-related quality of life and patient-reported outcome measures is widely used but arguably is not only of limited use but also usually misleading because it lacks context-specific meaning.MIDs for individuals cannot be aggregated without judgments about the distribution of outcomes over patient groups, and quality-of-life indicators need context; thus, the MID cannot be regarded as a property of an indicator.Quality-of-life indicators that generate health state values or utilities incorporate importance based on patient preferences, so the only unimportant change is zero.Published research into MIDs for health state values is unhelpful and even misleading.
Estimating the Causal Effect of Realistic Treatment Strategies Using Longitudinal Observational Data.
Zhang Y, Bennett A, Manca A … +9 more , Mittelman M, Hoeks M, Smith A, Taylor A, Stauder R, de Witte T, Malcovati L, van Marrewijk C, Kreif N
Med Decis Making · 2026 Feb · PMID 41143386 · Publisher ↗
BackgroundReal-world data can inform health care decisions by allowing the evaluation of nuanced treatment strategies. Longitudinal observational data enable the assessment of dynamic treatment regimes (DTRs), strategies...
BackgroundReal-world data can inform health care decisions by allowing the evaluation of nuanced treatment strategies. Longitudinal observational data enable the assessment of dynamic treatment regimes (DTRs), strategies that adapt treatment over time based on patient history, but require causal inference methods to address time-varying confounding. Longitudinal targeted minimum loss-based estimation (LTMLE) is a machine learning-based double-robust approach for improved causal effect estimation.MethodsWe applied LTMLE to longitudinal registry data to evaluate the impact of erythropoiesis-stimulating agents (ESAs) in the clinical management of low to intermediate-1 risk myelodysplastic syndrome (MDS). We defined DTRs based on clinically relevant decision rules (e.g., commencing treatment when the hemoglobin level falls below a threshold) and compared them to static treatment regimes (always or never giving ESAs). Outcomes include mortality and health-related quality of life measured by EQ-5D scores.ResultsThe static regime of never administering ESAs resulted in declining counterfactual EQ-5D scores and increasing mortality risk over time. In contrast, both the static regime of continuous administration of ESAs and the use of dynamic regimes improved the EQ-5D scores and tended to reduce mortality, although the mortality differences were not statistically significant.ConclusionsThe article provides a case study application of the LTMLE method to evaluate realistic treatment policies under time-varying confounding. The findings support the potential benefits of dynamic treatment strategies for the management of MDS, highlighting the importance of personalized treatment adaptation. The study contributes methodological insights into the applications of LTMLE in small-sample, long-follow-up settings relevant to health technology assessment and policy making.HighlightsThis study applies the longitudinal targeted minimum loss estimation (LTMLE) method to evaluate the causal effect of static and dynamic treatment strategies using longitudinal observational data.We demonstrate the use of the LTMLE method to assess the impact of erythropoiesis stimulating agents (ESAs) on quality of life and mortality in patients with low to intermediate-1 risk myelodysplastic syndromes.The findings suggest that patients treated under dynamic ESA treatment regimes show an improved quality of life measured by EQ-5D scores and survival compared with those treated under the static treatment regime of never administering ESAs.This study contributes to the methodological literature by showcasing the application of the LTMLE method in a small-sample, long-follow-up setting with time-varying confounding, informing health technology assessment and policy decisions.
A Bayesian Modeling Framework for Health Care Resource Use and Costs in Trial-Based Economic Evaluations.
Gabrio A
Med Decis Making · 2026 Feb · PMID 41128018 · Full text
Individual-level data are routinely used in trial-based economic evaluations to assess the effectiveness and costs of a given intervention. While effectiveness measures are often expressed via utility scores derived from...
Individual-level data are routinely used in trial-based economic evaluations to assess the effectiveness and costs of a given intervention. While effectiveness measures are often expressed via utility scores derived from health-related quality-of-life instruments (e.g., EQ-5D questionnaires), information on different types of health care resource use (HRU) measures (e.g., number and types of services) are collected to compute the costs. Partially complete HRU data, particularly for self-reported questionnaires, are handled via ad hoc methods that rely on some assumptions (fill in a zero) that are typically hard to justify. Although methods have been proposed to account for the uncertainty surrounding missing data, particularly in the form of multiple imputation or Bayesian methods, these have mostly been implemented at the level of costs at different times or over the entire study period, while little attention has been given to how missing values at the level of HRUs should be addressed and their implications on the final analysis. We present a general Bayesian framework for the analysis of partially observed HRUs in trial-based economic evaluations, which can accommodate the typical complexities of the data (e.g., excess zeros, skewness, missingness) and quantify the impact of missingness uncertainty on the results. We show the benefits of our approach with a motivating example and compare the results to those from more standard analyses fitted at the level of cost variables after adopting some ad hoc imputation. This article highlights the importance of adopting a comprehensive modeling approach to handle partially observed HRU data in economic evaluations and the strategic advantages of building these models within a Bayesian framework.HighlightsMissing health care service data in trial-based economic evaluations are often removed or imputed using quite restrictive assumptions (e.g., no use of service).We propose a flexible Bayesian approach to account for missing health care service uncertainty and compare the results with models fitted at more aggregated levels (e.g., total costs) using a real case study.Our results show that, depending on the (assumed) missingness assumptions and the level of data aggregation at which analyses are performed, results may be considerably changed.When feasible, analyses should be conducted at the most disaggregated level to ensure that all available information collected in the trial is used in the analysis without relying on (often) restrictive ad hoc imputation approaches.
Reinforcement Learning-Based Control of Epidemics on Networks of Communities and Correctional Facilities.
Weyant C, Lee S, Goldhaber-Fiebert JD
Med Decis Making · 2026 Feb · PMID 41122973 · Full text
BackgroundCorrectional facilities can act as amplifiers of infectious disease outbreaks. Small community outbreaks can cause larger prison outbreaks, which can in turn exacerbate the community outbreaks. However, strateg...
BackgroundCorrectional facilities can act as amplifiers of infectious disease outbreaks. Small community outbreaks can cause larger prison outbreaks, which can in turn exacerbate the community outbreaks. However, strategies for epidemic control in communities and correctional facilities are generally not closely coordinated. We sought to evaluate different strategies for coordinated control.MethodsWe developed a stochastic simulation model of an epidemic spreading across a network of communities and correctional facilities. We parameterized it for the initial phases of the COVID-19 epidemic for 1) California communities and prisons based on community data from covidestim, prison data from the California Department of Corrections and Rehabilitation, and mobility data from SafeGraph, and 2) a small, illustrative network of communities and prisons. For each community or prison, control measures were defined by the intensity of 2 activities: 1) screening to detect and isolate cases and 2) nonpharmaceutical interventions (e.g., masking and social distancing) to reduce transmission. We compared the performance of different control strategies including heuristic and reinforcement learning (RL) strategies using a reward function, which accounted for both the benefit of averted infections and nonlinear cost of the control measures. Finally, we performed analyses to interpret the optimal strategy and examine its robustness.ResultsThe RL control strategy robustly outperformed other strategies including heuristic approaches such as those that were largely used during the COVID-19 epidemic. The RL strategy prioritized different characteristics of communities versus prisons when allocating control resources and exhibited geo-temporal patterns consistent with mitigating prison amplification dynamics.ConclusionRL is a promising method to find efficient policies for controlling epidemic spread on networks of communities and correctional facilities, providing insights that can help guide policy.HighlightsFor modelers, we developed a stochastic simulation model of an epidemic spreading across a network of communities and correctional facilities, and we parameterized it for the initial phases of the COVID-19 epidemic for California communities and prisons in addition to an illustrative network.We compared different control strategies using a reward function that accounted for both the benefit of averted infections and cost of the control measures; we found that reinforcement learning robustly outperformed the other strategies including heuristic approaches such as those that were largely used during the COVID-19 epidemic.For policy makers, our work suggests that they should consider investing in the further development of such methods and using them for the control of future epidemics.We offer qualitative insights into different factors that might inform resource allocation to communities versus prisons during future epidemics.
Patient and Physician Perspectives on Using Risk Prediction to Support Breast Cancer Surveillance Decision Making.
Gunn CM, Boyer N, Sheikh S … +7 more , Lee JM, Woloshin S, Specht JM, Hubbard RA, Bowles EJA, Su YR, Tosteson ANA
Med Decis Making · 2026 Jan · PMID 41117001 · Full text
IntroductionBreast cancer survivors have a higher risk of interval cancers relative to the screening population. Patient characteristics including features of the primary cancer and its treatment can help predict interva...
IntroductionBreast cancer survivors have a higher risk of interval cancers relative to the screening population. Patient characteristics including features of the primary cancer and its treatment can help predict interval second breast cancer risk, but patient and physician perspectives on how risk prediction tools might enhance surveillance decision making are not well characterized.DesignWe conducted a qualitative study of women with breast cancer who had completed primary treatment and multispecialty physicians recruited through Breast Cancer Surveillance Consortium registries. We conducted semi-structured focus groups with 5 to 7 breast cancer survivors and individual physician interviews. All participants were presented with information about an interval cancer risk prediction tool. We elicited participant perspectives on aspects of the tool's design, relevance, and use for surveillance decision making. Data coding, thematic analysis, and interpretation were guided by the principles of theoretical thematic analysis.ResultsForty physician interviews and 4 focus groups involving 23 breast cancer survivors were analyzed. Two prominent areas of focus emerged: 1) perspectives on how a risk prediction tool would enhance and add value to patient-centered care and 2) risk prediction tools can be a means to improve communication about risk of in-breast recurrence or new breast cancer.ConclusionsThis study provides data on breast cancer survivor and physician perceptions of a new risk prediction tool to support surveillance imaging decisions among breast cancer survivors.ImplicationsAn interval second breast cancer risk prediction tool may promote evidence-based care across an array of physicians and different clinical settings. Future research should identify care delivery settings and features that promote adoption and support use in ways that improve shared decision making and patient outcomes.HighlightsThis qualitative study of breast cancer survivors and physicians found that risk prediction tools to support surveillance decisions were perceived positively when positioned as a supplement to the patient-physician relationship.Both patients and physicians said that a tool supported by strong evidence and accessible outputs would be valuable for shared decision making.
Facilitating Visualizations of Future Emotions: Leveraging the Narrative Immersion Model to Explore the Potential of Narratives to Reduce Affective Forecasting Errors.
Hundal K, Scherr CL, Zikmund-Fisher BJ
Med Decis Making · 2026 Jan · PMID 41111306 · Publisher ↗
BackgroundAffective forecasting errors (i.e., errors in people's predictions about future emotions) are common in health decision making and can negatively affect health outcomes. Although narrative interventions have be...
BackgroundAffective forecasting errors (i.e., errors in people's predictions about future emotions) are common in health decision making and can negatively affect health outcomes. Although narrative interventions have been used to mitigate these errors, many studies did not clearly identify the specific errors targeted or examine the impact of different narrative types on affective forecasting. We applied the narrative immersion model (NIM) to capture the nuances of narratives on mitigating specific affective forecasting errors in health decision making.MethodsUsing a narrative review of existing narrative affective forecasting interventions, we investigated the potential of experience, process, and outcome narratives to reduce specific affective forecasting errors (e.g., focalism, immune neglect).ResultsDifferent narrative types-experience, process, and outcome-may play distinct roles in mitigating affective forecasting errors. Experience narratives may reduce affective forecasting errors by describing what people most likely (targeted) or might (representative) experience, process narratives by modeling optimal decision making, and outcome narratives by broadening people's understanding of possible emotional outcomes. We further discuss how narrative characteristics related to content and structure (e.g., perspective taking, transportation, etc.) may advance narrative effects on affective forecasting.ConclusionsOur findings have implications for intervention design as they facilitate the selection of narrative types tailored to specific affective forecasting errors (e.g., framing, misconstruals, or impact bias).HighlightsSpecific affective forecasting errors may be reduced through different types of narratives, but greater understanding is needed regarding the exact mechanisms.The narrative immersion model is a useful framework to investigate the potential of experience, process, and outcome narratives to reduce specific types of affective forecasting errors.We describe the pathways through which narrative types most likely influence affective forecasting and facilitate the choice of narrative message type for a specific affective forecasting error.Narratives designed for affective forecasting interventions should include detailed and realistic descriptions of people's emotional health care experiences.Other narrative characteristics (e.g., realism, perspective taking, transportation) might affect a person's ability to imagine future emotional health states, and future research should consider their effects on affective forecasting.
Determinants of Physicians' Referrals for Suspected Cancer Given a Risk-Prediction Algorithm: Linking Signal Detection and Fuzzy Trace Theory.
Kostopoulou O, Pálfi B, Arora K … +1 more , Reyna V
Med Decis Making · 2026 Jan · PMID 41099585 · Full text
BackgroundPrevious research suggests that physicians' inclination to refer patients for suspected cancer is a relatively stable characteristic of their decision making. We aimed to identify its psychological determinants...
BackgroundPrevious research suggests that physicians' inclination to refer patients for suspected cancer is a relatively stable characteristic of their decision making. We aimed to identify its psychological determinants in the presence of a risk-prediction algorithm.MethodsWe presented 200 UK general practitioners with online vignettes describing patients with possible colorectal cancer. Per the vignette, GPs indicated the likelihood of referral (from highly unlikely to highly likely) and level of cancer risk (negligible/low/medium/high), received an algorithmic risk estimate, and could then revise their responses. After completing the vignettes, GPs responded to questions about their values with regard to harms and benefits of cancer referral for different stakeholders, perceived severity of errors, acceptance of false alarms, and attitudes to uncertainty. We tested whether these values and attitudes predicted their earlier referral decisions.ResultsThe algorithm significantly reduced both referral likelihood ( = -0.06 [-0.10, -0.007], = 0.025) and risk level ( = -0.14 [-0.17, -0.11], < 0.001). The strongest predictor of referral was the value GPs attached to patient benefits ( = 0.30 [0.23, 0.36], < 0.001), followed by benefits ( = 0.18 [0.11, 0.24], < 0.001) and harms ( = -0.14 [-0.21, -0.08], < 0.001) to the health system/society. The perceived severity of missing a cancer vis-à-vis overreferring also predicted referral ( = 0.004 [0.001, 0.007], = 0.009). The algorithm did not significantly reduce the impact of these variables on referral decisions.ConclusionsThe decision to refer patients who might have cancer can be influenced by how physicians perceive and value the potential benefits and harms of referral primarily for patients and the moral seriousness of missing a cancer vis-à-vis over-referring. These values contribute to an internal threshold for action and are important even when an algorithm informs risk judgments.HighlightsPhysicians' inclination to refer patients for suspected cancer is determined by their assessment of cancer risk but also their core values; specifically, their values in relation to the perceived benefits and harms of referrals and the seriousness of missing a cancer compared with overreferring.We observed a moral prioritization of referral decision making, in which considerations about benefits to the patient were foremost, considerations about benefits but also harms to the health system or the society were second, while considerations about oneself carried little or no weight.Having an algorithm informing assessments of risk influences referral decisions but does not remove or significantly reduce the influence of physicians' core values.
Using Discrete Choice Experiments (DCEs) to Compare Social and Personal Preferences for Health and Well-Being Outcomes.
Wickramasekera N, Ta AT, Field B … +1 more , Tsuchiya A
Med Decis Making · 2026 Feb · PMID 41099579 · Full text
BackgroundEconomic evaluations in health typically assume a nonwelfarist framework, arguably better served by preferences elicited from a social perspective than a personal one. However, most health state valuation studi...
BackgroundEconomic evaluations in health typically assume a nonwelfarist framework, arguably better served by preferences elicited from a social perspective than a personal one. However, most health state valuation studies elicit personal preferences, leading to a methodological inconsistency. No studies have directly compared social and personal preferences for outcomes using otherwise identical scenarios, leaving their empirical relationship unclear.AimThis unique study examines whether the choice of eliciting preferences from a social or personal perspective influences valuations of health and well-being outcomes.MethodsUsing discrete choice experiments, social and personal preferences for health and well-being attributes were elicited from the UK general public recruited from an internet panel ( = 1,020 personal, = 3,009 social surveys). Mixed logit models were estimated, and willingness-to-pay (WTP) values for each attribute were calculated to compare differences between the 2 perspectives.ResultsWhile no significant differences were observed in the effects of physical and mental health, loneliness, and neighborhood safety across the 2 perspectives, significant differences emerged in WTP values for employment and housing quality. For instance, other things being the same, personal preferences rate being retired as more preferable than being an informal caregiver, but the social preferences rate them in the reverse order.ConclusionOur findings demonstrate that the perspective matters, particularly for valuing outcomes such as employment and housing. These findings indicate that the exclusive use of personal preferences to value states such as employment and housing quality may potentially lead to suboptimal resource allocation, given that such valuations reflect individual rather than societal benefit. This highlights the importance of considering perspective especially in the resource allocation of public health interventions.HighlightsPersonal preferences were not aligned with social preferences for employment and housing quality outcomes.Respondents valued health outcomes the same in both social and personal perspectives.Using personal preferences in public health resource allocation decisions may not reflect societal priorities.
Effects of Prior Diagnosis on Second Opinions and Pathologist Viewing Behaviors: Results from a Randomized Trial in Breast Pathology.
Kerr KF, Eguchi MM, Shucard H … +4 more , Drew T, Weaver DL, Elmore JG, Brunyé TT
Med Decis Making · 2026 Jan · PMID 41020455 · Full text
ObjectiveTo study the effects of exposure to a prior diagnosis (PD) on second opinions in breast pathology.Materials and MethodsPathologists interpreted digital breast biopsy cases in 2 phases separated by a washout. Pha...
ObjectiveTo study the effects of exposure to a prior diagnosis (PD) on second opinions in breast pathology.Materials and MethodsPathologists interpreted digital breast biopsy cases in 2 phases separated by a washout. Phase 2 interpretations were randomly assigned to PD or no PD. When presented, PD was always more or less severe than a participant's phase 1 diagnosis. Viewing behaviors, including zoom level, were recorded during all interpretations. Twenty pathologists yielded 556 interpretations of 32 different cases.ResultsPathologists were 71% more likely to give a less severe diagnosis when exposed to a less severe PD than with no PD (RR 1.71, 95% CI 1.33-2.20, < 0.001). In comparison, when exposed to a more severe PD than with no PD, pathologists were 27% more likely to give a more severe diagnosis, but the effect was not significant (RR 1.27, 95% CI 0.87-1.86, = 0.223). Compared with no PD, viewing behavior shifted toward more focus on critical image regions with exposure to a less severe PD and toward higher zoom levels with exposure to a more severe PD.DiscussionResults indicate anchoring and confirmation biases from PD exposure, such that second opinions after PD exposure are not independent assessments. Viewing behaviors illustrated how PD alters the interpretive process, including increased zooming when exposed to a more severe PD. Results have implications for best practices for computer-aided diagnosis tools.ImplicationsWhen giving a second opinion, exposure to a PD can sway diagnostic classifications and alter interpretive behavior, highlighting a need for protocols that encourage independent assessments.HighlightsIn pathology diagnosis, second opinions are systematically influenced by prior diagnostic information.Less severe prior diagnoses shift pathologists' visual attention toward clinically critical regions of a pathology image, whereas more severe prior diagnoses tend to elicit increased magnification during case interpretation.Specific viewing behaviors partially mediate the effect of prior diagnoses on second opinion diagnoses.When prior diagnoses are disclosed to pathologists, anchoring and confirmation biases undermine the independence of second opinion decisions.
Does Messaging for Reducing Breast Cancer Overscreening in Older Women Have Differential Responses among Medical Minimizers and Maximizers?
Schoenborn NL, Gollust SE, Nagler RH … +5 more , Schonberg MA, Boyd CM, Xue QL, Nader YM, Pollack CE
Med Decis Making · 2026 Jan · PMID 41020449 · Full text
BackgroundMessaging strategies hold promise to reduce breast cancer overscreening. However, it is not known whether they may have differential effects among medical maximizers who prefer to take action about their health...
BackgroundMessaging strategies hold promise to reduce breast cancer overscreening. However, it is not known whether they may have differential effects among medical maximizers who prefer to take action about their health versus medical minimizers who prefer to wait and see.MethodsIn a randomized controlled survey experiment that included 2 sequential surveys with 3,041 women aged 65+ y from a US population-based online panel, we randomized participants to 1) no messages, 2) single exposure to a screening cessation message, or 3) 2 exposures over time to the screening cessation message. We assessed support for stopping screening in a hypothetical patient and intention to stop screening oneself on 7-point scales, where higher values indicated stronger support and intentions to stop screening. We conducted stratified analyses by medical-maximizing preference and moderation analysis.ResultsOf the women, 40.7% ( = 1,238) were medical maximizers; they had lower support and intention for screening cessation in all groups compared with the medical minimizers. Two message exposures increased support for screening cessation among medical maximizers, with a mean score of 3.68 (95% confidence interval [CI] 3.51-3.85) compared with no message (mean score 2.20, 95% CI 2.00-2.39, < 0.001). A similar pattern was seen for screening intention. Linear regression models showed no differential messaging effect by medical-maximizing preference.ConclusionsMedical maximizers, although less likely to support screening cessation, were nonetheless responsive to messaging strategies designed to reduce breast cancer overscreening.HighlightsIt is not known if a message on rationales for stopping breast cancer screening would have differential effects among medical maximizers who prefer to take action when it comes to their health versus medical minimizers who prefer to wait and see.In a 2-wave randomized controlled survey experiment with 3,041 older women, we found that medical maximizers, although less likely to support screening cessation compared with medical minimizers, were nonetheless responsive to the messaging intervention, and the magnitude of the intervention effect was similar between maximizers and minimizers.Medical maximizers reported higher levels of worry and annoyance after reading the message compared with the minimizers, but the absolute levels of worry and annoyance were low.Our findings suggest that messaging can be a useful tool for reducing overscreening even in a highly reluctant population.
Influence of Anecdotes of IVF Success on Treatment Decision Making: An Online Randomized Controlled Trial.
Chadwick V, Goldwater MB, van Laer T … +4 more , Smith J, Cvejic E, McCaffery KJ, Copp T
Med Decis Making · 2026 Jan · PMID 41020433 · Full text
BackgroundAlthough in vitro fertilization (IVF) has enhanced fertility opportunities for many people, it also comes with considerable burden. Concerns have been raised about patients holding unrealistic expectations and...
BackgroundAlthough in vitro fertilization (IVF) has enhanced fertility opportunities for many people, it also comes with considerable burden. Concerns have been raised about patients holding unrealistic expectations and continuing treatment indefinitely. This study aimed to investigate whether anecdotes of IVF success affect hypothetical intentions to continue treatment despite very low chances of success.DesignOnline randomized controlled trial with a parallel 3-arm design, conducted in May 2022. After viewing a clinical vignette depicting 6 unsuccessful IVF cycles with less than 5% chance of subsequent treatment success, 606 females aged 18 to 45 years in Australia were randomized to receive either 1) an anecdote of IVF success despite limited chances, 2) the anecdote of success and an anecdote of failure, or 3) no anecdote. Outcomes were intention to undergo another IVF cycle, worry, likelihood of success, and narrative transportation.ResultsThere was a main effect of anecdote condition on intention to have another IVF cycle, with participants randomized to the positive and negative anecdote having higher intention than those given no additional information (mean difference = 0.65, 95% confidence interval [CI] = 0.12-1.18, = 0.017). There were no differences between conditions regarding worry, likelihood of success, or narrative transportation. In adjusted analyses accounting for prior IVF experience, the main effect of anecdotes on intention was no longer statistically significant. Those with prior IVF experience reported a statistically higher likelihood of success and narrative transportation than those without prior IVF experience (mean difference [MD] = 34.28, 95% CI = 27.26-41.30, < 0.001, and MD = 1.35, 95% CI = 0.96-1.74, < 0.001, respectively).ConclusionHearing anecdotes may encourage continuation of IVF despite extremely low chances of success. These findings, along with our sample's overestimation of IVF success, illustrate the importance of frequent and frank discussions about expected treatment outcomes.Trial registration:ACTRN12622000576729.HighlightsThe presence of IVF anecdotes increased the intention to undergo another IVF cycle despite extremely low chances of success.Balancing an anecdote of success with an anecdote of failure had no attenuating effect on intention.IVF providers should be wary of the potential impact of success stories on patients' decision making.In the vignette depicting overuse of IVF, participants with previous IVF experience greatly overestimated the likelihood of success with another IVF cycle, supporting previous research finding that patients often have unrealistically high expectations about their own chance of success.
Lottery or Triage? Controlled Experimental Evidence from the COVID-19 Pandemic on Public Preferences for Allocation of Scarce Medical Resources.
Thomas RL, Roope LS, Duch R … +3 more , Robinson T, Zakharov A, Clarke P
Med Decis Making · 2026 Jan · PMID 41014147 · Full text
BackgroundBioethicists have advocated lotteries to distribute scarce health care resources, highlighting the benefits that make them attractive amid growing health care challenges. During the COVID-19 pandemic, lotteries...
BackgroundBioethicists have advocated lotteries to distribute scarce health care resources, highlighting the benefits that make them attractive amid growing health care challenges. During the COVID-19 pandemic, lotteries were used to distribute vaccines within priority groups in some settings, notably in the United States. Nonetheless, limited evidence exists on public attitudes toward lotteries.MethodsTo assess public support for vaccine allocation by lottery versus expert committee, we conducted a survey-based experiment during the pandemic. Between November 2020 and May 2021, data were collected from 15,380 respondents across 14 diverse countries. Respondents were randomly allocated (1:1) to 1 of 2 hypothetical scenarios involving COVID-19 vaccine allocation among nurses: 1) by lottery and 2) prioritization by a committee of expert physicians. The outcome was agreement on the appropriateness of the allocation mechanism on a scale ranging from 0 () to 100 (), with differences stratified by a range of covariates. Two-sided tests were used to test for overall differences in mean agreement between lottery and expert committee.FindingsMean agreement with lottery allocation was 37.25 (95% confidence interval [CI] 34.86-39.65), ranging from 21.1 (95% CI 15.07-27.13) in Chile to 62.33 (95% CI 54.45-70.21) in India. In every country, expert committee allocation received higher support, with mean agreement of 61.19 (95% CI: 60.04-62.35), varying from 51.25 in Chile to 69.77 in India. Greater agreement with lotteries was observed among males, higher-income individuals, those with lower education, and those identifying as politically right leaning.ConclusionsDespite arguments for lottery-based allocation of medical resources, we found low overall public support, albeit with substantial variation across countries. Successful implementation of lottery allocation will require targeted public engagement and clear communication of potential benefits.HighlightsThis study surveyed 15,380 respondents from 14 diverse countries during the COVID-19 pandemic, analyzing international agreement with the appropriateness of using lottery allocation for scarce health care resources.There was universal preference for allocating vaccines by expert committee rather than by lotteries, but there was significant variation in agreement between countries, indicating the need for region-specific policy approaches.Successful implementation of lottery allocation requires targeted public engagement and communication of their benefits, especially with groups less supportive of lotteries.
Reducing Substance Use-Related Harms: A Simulation-Optimization Framework for the Design and Evaluation of Harm Reduction Vending Machines.
Zafarnejad R, Griffin PM, Zgierska AE … +1 more , Zhang A
Med Decis Making · 2025 Nov · PMID 40990576 · Publisher ↗
IntroductionThis study introduces a simulation-optimization framework designed to optimize the services of opioid-focused harm reduction vending machines (HRVMs). Given the rising rates of overdose deaths and increased p...
IntroductionThis study introduces a simulation-optimization framework designed to optimize the services of opioid-focused harm reduction vending machines (HRVMs). Given the rising rates of overdose deaths and increased potential for infectious diseases among persons who inject drugs (PWID), HRVMs can become an important harm reduction (HR) strategy by providing essential supplies that mitigate health risks.MethodsWe developed and validated an agent-based simulation-optimization framework to model the impact of HRVM-item allocation on the burden of opioid-related harms, accounting for demand dynamics, item restocking, and regional characteristics. The model evaluated health outcomes-cases of HIV, HCV, and fatal and nonfatal overdose-using disability-adjusted life-years (DALYs). Scenario-based analyses were conducted for different HRVM configurations, considering current legal limits on safer-injection supplies, fentanyl's growing role as a drug of choice, and potential future policy changes.ResultsThe base scenario estimated optimal HRVM capacity allocation at approximately 48.5% fentanyl test strips (FTS), 26.2% naloxone, and 25.3% safer injection kits. However, sensitivity analyses showed significant variations based on fentanyl prevalence and willingness to use FTS. In scenarios of intentional fentanyl use with high FTS utilization, allocation favored FTS, while scenarios with low FTS utilization prioritized naloxone and injection kits. Adding addiction treatment referral services to HRVMs further reduced DALYs and societal costs, primarily by preventing fatal overdoses. Safer injection kits consistently reduced blood-borne infections compared with scenarios without these kits.ConclusionsThe framework could aid in HRVMãrelated service planning and evaluation, highlighting the importance of strategic inventory management and linkages to addiction care for enhanced health outcomes. HRVMs show potential as scalable, cost-effective HR interventions, warranting further research on their impact on service accessibility and health outcomes.HighlightsA novel simulation-optimization framework for designing and evaluating harm reduction vending machines (HRVMs) is presented.Optimal baseline allocation for products in the HRVMs included fentanyl test strips (48.5%), naloxone (26.2%), and safer injection kits (25.3%).Sensitivity analysis indicated optimal allocations vary substantially by local fentanyl prevalence and by individual harm reduction behaviors surrounding the use of fentanyl test strips.HRVM implementation reduces societal costs and disability-adjusted life-years associated with substance use-related harms.
Integrating Shared Decision Making and Decision Support Tools into Clinical Practice Guidelines: What Does It Take? A Qualitative Study.
Fischer L, Wollny R, Schewe LV … +13 more , Scheibler F, Karge T, Langer T, Schaefer C, Florez ID, Hutchinson A, Li S, Maes-Carballo M, Munn Z, Perestelo-Perez L, Puljak L, Stiggelbout A, Pieper D
Med Decis Making · 2026 Jan · PMID 40955090 · Publisher ↗
Awareness of shared decision making (SDM) is growing, but its integration into clinical practice guidelines (CPGs) remains challenging. We sought expert insights to identify strategies for more successfully integrating S...
Awareness of shared decision making (SDM) is growing, but its integration into clinical practice guidelines (CPGs) remains challenging. We sought expert insights to identify strategies for more successfully integrating SDM and decision support tools into CPGs. Specifically, our objectives were to determine 1) how to identify CPG recommendations where SDM is most relevant and 2) what factors in CPG development hinder or facilitate the consideration of SDM and the development of decision support tools. . We conducted semi-structured interviews with experts on CPGs and SDM. We analyzed the data using Mayring's qualitative content analysis. The 16 interviewed participants proposed several determinants of and strategies for identifying SDM-relevant recommendations. The most frequently mentioned determinant was "multiple options with benefits and harms where choices depend on individual preferences." The most frequently mentioned strategy was prioritization, similar to the CPG scoping phase. Participants highlighted the role of patient partners in facilitating the consideration of SDM in CPG development but noted that a supportive culture toward both patient and public involvement and SDM is needed. The absence of standardized methods and inadequate resources hinder the consideration of SDM and the combined development of CPGs and decision support tools. The current format of CPGs was deemed overwhelming, while the inclusion of choice awareness in CPG recommendations could facilitate SDM. The identified strategies provide a starting point for CPG organizations to explore ways for integrating SDM and decision support tools into CPGs while considering context-specific barriers and facilitators. Further research is needed to assess the usefulness and feasibility of the proposed strategies. New policies and stronger collaboration between CPG and SDM communities appear to be needed to address identified barriers.HighlightsWe explored expert knowledge and experience on how to successfully integrate shared decision making (SDM) and decision support tools into clinical practice guidelines (CPGs).A combined development of CPGs and decision support tools was deemed essential; however, development processes often remain separate, with the CPG development group unaware of the decision support tool development group, and vice versa.In addition to stating choice awareness in CPGs, participants highlighted the critical role of patient partners in considering SDM in CPG development, but resource issues and a culture that neglects patient involvement and SDM remain.For CPG development groups to consider SDM and for health care professionals to practice it, things need to be as easy as possible.
A Break from the Norm? Parametric Representations of Preference Heterogeneity for Discrete Choice Models in Health.
Buckell J, Wreford A, Quaife M … +1 more , Hancock TO
Med Decis Making · 2025 Nov · PMID 40910513 · Full text
BackgroundAny sample of individuals has its own unique distribution of preferences for choices that they make. Discrete choice models try to capture these distributions. Mixed logits are by far the most commonly used cho...
BackgroundAny sample of individuals has its own unique distribution of preferences for choices that they make. Discrete choice models try to capture these distributions. Mixed logits are by far the most commonly used choice model in health. Many parametric specifications for these models are available. We test a range of alternative assumptions and model averaging to test if or how model outputs are affected.DesignScoping review of current modeling practices. Seven alternative distributions and model averaging over all distributional assumptions were compared on 4 datasets: 2 were stated preference, 1 was revealed preference, and 1 was simulated. Analyses examined model fit, preference distributions, willingness to pay, and forecasting.ResultsAlmost universally, using normal distributions is the standard practice in health. Alternative distributional assumptions outperformed standard practice. Preference distributions and the mean willingness to pay varied significantly across specifications and were seldom comparable to those derived from normal distributions. Model averaging offered distributions allowing for greater flexibility and further gains in fit, reproduced underlying distributions in simulations, and mitigated against analyst bias arising from distribution selection. There was no evidence that distributional assumptions affected predictions from models.LimitationsOur focus was on mixed logit models since these models are the most common in health, although latent class models are also used.ConclusionsThe standard practice of using all normal distributions appears to be an inferior approach for capturing random preference heterogeneity. Researchers should test alternative assumptions to normal distributions in their models.HighlightsHealth modelers use normal mixing distributions for preference heterogeneity.Alternative distributions offer more flexibility and improved model fit.Model averaging offers yet more flexibility and improved model fit.Distributions and willingness to pay differ substantially across alternatives.
Weighing Parenthood Wishes: A Conjoint Analysis of Criteria to Prioritize Infertile Couples for Publicly Funded Fertility Treatment.
Van Muylder A, Kessels R, D'Hooghe T … +1 more , Luyten J
Med Decis Making · 2025 Nov · PMID 40826820 · Publisher ↗
BackgroundParenthood is a key life goal for many, but infertility affects about 1 in 6 globally. While fertility treatments offer solutions, their high costs limit access. Many health systems provide public funding, yet...
BackgroundParenthood is a key life goal for many, but infertility affects about 1 in 6 globally. While fertility treatments offer solutions, their high costs limit access. Many health systems provide public funding, yet budget constraints prevent fully funded access, often leaving patients with significant out-of-pocket costs. Policy makers face the challenge of prioritizing individuals for publicly funded treatments, but how to do this remains unclear and underresearched. Worldwide, funding policies vary widely, often adopting controversial access criteria.MethodsWe investigated Belgian population preferences for prioritizing in vitro fertilization (IVF) funding through a discrete-choice experiment with a representative sample of 3,000 Belgians. Attributes included maternal and partner age, infertility cause, civil status, prior biological children, and treatment cost. Using a Bayesian D-optimal design and panel mixed logit model, we assessed criteria relevance. The resulting multiattribute utility function created a priority ranking of couples, which we compared to the ranking under the current Belgian policy, which focuses only on maternal age (<43 y).ResultsAnalysis of 29,670 prioritization choices identified maternal age, infertility cause, and prior biological children as key criteria. Maternal age of 35 y was prioritized highest, age 25 y as high as 40 y, followed by declining priority until 55 y. Biomedical malfunctions were prioritized over same-sex relationships or unhealthy lifestyles, with the latter prioritized lowest. Having no prior biological children was prioritized categorically higher than having 1, 2, or 3 children, all prioritized equally. Preferences were homogeneous across sociodemographic groups.ConclusionsHow to set IVF funding priorities remains a matter of debate. Our study shows that the Belgian population considers multiple criteria beyond maternal age to prioritize couples, calling for further discussion on ethical justifiability and access implications.HighlightsParenthood is a key life goal to many, but about 1 in 6 are affected by infertility. However, in most countries, public funding for fertility treatment is not provided to everyone who could benefit, and hard choices are inevitable.This study used a discrete-choice experiment in a representative sample of the Belgian population to investigate which criteria should be used for prioritization.Results indicated that maternal age, cause of infertility, and the number of prior biological children were the most significant factors in determining public support for IVF funding. Partner age, civil status of the couple, and cost of IVF treatment were not important.People use multiple criteria to set IVF funding priorities, beyond maternal age (the only criterion used in the current Belgian funding policy). Future research should explore the ethical justifiability and practical implications of using cause of infertility and number of prior children as additional criteria.
Values Clarification Methods in Decision Support Tools for Lung Cancer Screening: A Systematic Review and Content Analysis.
Crossnohere NL, Negash R, Schwimmer M … +3 more , Voisin C, Bridges JFP, Jonas DE
Med Decis Making · 2025 Oct · PMID 40819287 · Publisher ↗
BackgroundValues clarification methods may be particularly appropriate for decision support in lung cancer screening (LCS), for which patients must consider a complex tradeoff of benefits and harms. Values clarification...
BackgroundValues clarification methods may be particularly appropriate for decision support in lung cancer screening (LCS), for which patients must consider a complex tradeoff of benefits and harms. Values clarification methods that are explicit and use theory-based methods may best support decision making.PurposeTo characterize values clarification methods in decision support tools for LCS and explore associations with behavioral and decisional outcomes.Data SourcesPubMed, Cochrane Library, CINAHL, APA PsycINFO, and Embase, supplemented with gray literature and hand searches.Study SelectionStudies evaluating patient-facing LCS decision support tools.Data ExtractionWe extracted information on study characteristics and the decision support tools evaluated in each study, including method of values clarification (explicit, implicit, or none). Study quality was evaluated using an adapted version of the SUNDAE Checklist.Data SynthesisWe identified 48 studies (10,233 participants) evaluating 32 unique decision support tools for LCS. More than 80% of tools included values clarification methods, split between explicit ( = 13) and implicit ( = 13) methods. Only 1 explicit values clarification used a theory-based method. Meta-analysis of randomized controlled trials indicated that using a decision support tool doubled the odds of receiving LCS (pooled odds ratio 1.98, 95% confidence interval 1.21-3.25, 9 studies), a pattern driven by increased uptake of screening following use of tools with explicit or no values clarification. Studies lacking values clarification were of lower quality than those with explicit or implicit methods ( = 0.04).LimitationsAlmost no tools applied theory-based methods for explicit values clarification, limiting conclusions about their impact.ConclusionsLCS decision support tools routinely incorporate values clarification methods and appear to enhance screening uptake. However, theory-based values clarification methods, which may further improve decision support quality, remain underutilized.HighlightsValues clarification is a core aspect of shared decision making. It may be especially valuable for decision making regarding lung cancer screening (LCS), as patients must weigh a complex balance of benefits and harms.This systematic review identified 48 studies assessing 32 unique decision support tools for LCS. More than 80% of these tools incorporated values clarification methods, with an equal distribution of explicit and implicit methods.Among the subset of studies using a randomized controlled trial, the use of a decision support tool doubled the odds of an individual undergoing LCS.Decision support tools designed to support shared decision making in LCS commonly incorporate values clarification methods. However, they infrequently use theory-based methods, which are increasingly thought to provide high-quality decision support.
Incentivizing Adherence to Gender-Affirming PrEP Programs: A Stated Preference Discrete-Choice Experiment among Transgender and Gender Nonbinary Adults.
Wilson-Barthes MG, Javellana Restar A, Operario D … +1 more , Galárraga O
Med Decis Making · 2025 Nov · PMID 40819192 · Publisher ↗
ObjectivesTransgender (trans) people have disproportionately high HIV risk, yet adherence to preexposure prophylaxis (PrEP) remains low in this population. We aimed to determine which factors matter most in the decision...
ObjectivesTransgender (trans) people have disproportionately high HIV risk, yet adherence to preexposure prophylaxis (PrEP) remains low in this population. We aimed to determine which factors matter most in the decision of HIV-negative transgender adults to adhere to long-acting injectable PrEP (LA-PrEP), and the acceptability of providing incentives conditional on LA-PrEP program engagement.MethodsFrom March to April 2023, 385 trans adults in Washington State completed a discrete-choice experiment (DCE) eliciting preferences for a conditional economic incentive program that would provide free LA-PrEP and gender-affirming care during bimonthly visits. We used the best-best preference elicitation method across 2 hypothetical programs with an opt-out option. Program attributes included incentive format and amount, method for determining PrEP adherence, and type of hormone co-prescription. We used a rank-ordered mixed logit model for main results and estimated respondents' marginal willingness to accept each program attribute. We plotted the probability of choosing an incentivized LA-PrEP program over a range of respondent characteristics.ResultsThe optimal program design would 1) deliver incentives in cash, 2) confirm PrEP adherence via blood testing, 3) provide counseling in person, and 4) provide prescriptions for injectable gender-affirming hormones. From a maximum incentive amount of $1,200/year, respondents were willing to forgo up to $689 to receive incentives in cash (instead of voucher) and up to $547 to receive injectable (instead of oral) hormones. The probability of choosing a hypothetical program over no program waned as adults aged (>40 y) and as income increased (>$75,000/y).ConclusionsConditional economic incentives are likely acceptable and effective for improving LA-PrEP adherence, especially among younger trans adults with fewer financial resources. A randomized trial is needed to confirm the DCE's validity for predicting actual program uptake.HighlightsGender-related stigma, economic barriers, and medical concerns about hormone interactions can keep transgender (trans) adults from engaging in HIV prevention behaviors.Combining gender-affirming care with conditional economic incentives may help reduce present bias and increase trans persons' motivation to adhere to long-acting injectable preexposure prophylaxis (LA-PrEP).From a maximum yearly incentive of $1,200, trans discrete-choice experiment respondents were willing to forgo up to $689 to receive a cash (rather than voucher) incentive and up to $547 to receive co-prescriptions for injectable (rather than oral) hormones as part of a hypothetical HIV prevention program.The probability of choosing an LA-PrEP program over no program begins to wane as adults age (>40 y) and as annual income increases (>$75,000/year), such that incentivized LA-PrEP programs may be especially salient for younger trans adults with fewer financial resources.
Meta-Modeling as a Variance-Reduction Technique for Stochastic Model-Based Cost-Effectiveness Analyses.
Li Z, Knowlton GS, Wheatley MM … +2 more , Jenness SM, Enns EA
Med Decis Making · 2025 Nov · PMID 40814193 · Full text
PurposeWhen using stochastic models for cost-effectiveness analysis (CEA), run-to-run outcome variability arising from model stochasticity can sometimes exceed the change in outcomes resulting from an intervention, espec...
PurposeWhen using stochastic models for cost-effectiveness analysis (CEA), run-to-run outcome variability arising from model stochasticity can sometimes exceed the change in outcomes resulting from an intervention, especially when individual-level efficacy is small, leading to counterintuitive results. This issue is compounded for probabilistic sensitivity analyses (PSAs), in which stochastic noise can obscure the influence of parameter uncertainty. This study evaluates meta-modeling as a variance-reduction technique to mitigate stochastic noise while preserving parameter uncertainty in PSAs.MethodsWe applied meta-modeling to 2 simulation models: 1) a 4-state Sick-Sicker model and 2) an agent-based HIV transmission model among men who have sex with men (MSM). We conducted a PSA and applied 3 meta-modeling techniques-linear regression, generalized additive models, and artificial neural networks-to reduce stochastic noise. Model performance was assessed using and root mean squared error (RMSE) values on a validation dataset. We compared PSA results by examining scatter plots of incremental costs and quality-adjusted life-years (QALYs), cost-effectiveness acceptability curves (CEACs), and the occurrence of unintuitive results, such as interventions appearing to reduce QALYs due to stochastic noise.ResultsIn the Sick-Sicker model, stochastic noise increased variance in incremental costs and QALYs. Applying meta-modeling techniques substantially reduced this variance and nearly eliminated unintuitive results, with and RMSE values indicating good model fit. In the HIV agent-based model, all 3 meta-models effectively reduced outcome variability while retaining parameter uncertainty, yielding more informative CEACs with higher probabilities of being cost-effective for the optimal strategy.ConclusionsMeta-modeling effectively reduces stochastic noise in simulation models while maintaining parameter uncertainty in PSA, enhancing the reliability of CEA results without requiring an impractical number of simulations.HighlightsWhen using complex stochastic models for cost-effectiveness analysis (CEA), stochastic noise can overwhelm intervention effects and obscure the impact of parameter uncertainty on CEA outcomes in probabilistic sensitivity analysis (PSA).Meta-modeling offers a solution by effectively reducing stochastic noise in complex stochastic simulation models without increasing computational burden, thereby improving the interpretability of PSA results.
Life Expectancy Predicted by Decision-Analytic Models Evaluating Screening for Prostate, Lung, Breast, and Colorectal Cancer: A Systematic Review Focusing on Competing Mortality Risks.
Henning C, Sroczynski G, Hallsson L … +3 more , Jahn B, Siebert U, Mühlberger N
Med Decis Making · 2025 Nov · PMID 40808368 · Publisher ↗
BackgroundIt is still a matter of debate whether a reduction in cancer-specific mortality due to cancer screening fully translates into a reduction in all-cause mortality and thus into a gain in life expectancy. Neverthe...
BackgroundIt is still a matter of debate whether a reduction in cancer-specific mortality due to cancer screening fully translates into a reduction in all-cause mortality and thus into a gain in life expectancy. Nevertheless, decision-analytic models simulating the health consequences of screening compared with no screening predict substantial gains in life expectancy.PurposeThe aim of this review was to systematically assess methodological competing mortality risk features that affect the translation of cancer-specific mortality reductions into gains in life expectancy in decision-analytic screening models for prostate, lung, breast, and colorectal cancer.Data SourcesLiterature databases were systematically searched for clinical and economic decision-analytic models evaluating the effect of screening for prostate, lung, breast, and colorectal cancer compared with no screening.Study SelectionForty-two clinical and economic decision-analytic models were included for narrative synthesis.Data ExtractionBasic information and specific methodological features of the included decision-analytic models were extracted using a standardized approach.Data SynthesisCharacteristics and methodological features of the identified studies were summarized in evidence tables.LimitationsThe review focused on models that reported undiscounted outcomes of life-years gained for standard screening strategies.ConclusionsThis review highlights key modeling features related to competing mortality risks that should be considered in decision-analytic models assessing the effects of cancer screening. All included models predicted gains in life expectancy with screening, although the magnitude of these gains varied both within and across cancer types. Models that considered competing mortality risks tended to predict smaller lifetime gains from screening interventions. Future studies should prioritize the use of advanced modeling approaches that account for competing mortality risks to improve the accuracy of benefit-harm assessments in cancer screening.HighlightsThis is the first systematic assessment of methodological competing mortality risk features of decision-analytic screening models across 4 cancer types.Models vary greatly regarding predicted gains in life expectancy, natural history assumptions (onset and progression rates), methodological model features, and screening strategies.Models that considered competing mortality risks or adjusted life expectancy for comorbidities predicted smaller lifetime gains for screening compared with no screening.
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