Medical Decision Making[JOURNAL]
Sun
200 papers
Flexible Survival Extrapolation with Blended Hazards: Accounting for Treatment Effect Waning in Health Technology Assessment.
Zhu J, Hemstock M, Che Z … +2 more , Baio G, Birnie R
Med Decis Making · 2026 Jun · PMID 42317032 · Publisher ↗
BackgroundSurvival extrapolation is crucial in estimating the lifetime survival benefit of a treatment for health technology assessment (HTA). Conventional extrapolation methods, which assume that the long-term treatment...
BackgroundSurvival extrapolation is crucial in estimating the lifetime survival benefit of a treatment for health technology assessment (HTA). Conventional extrapolation methods, which assume that the long-term treatment effect (hazard ratio between treatment and comparator) follows the same pattern as observed in the short-term trial, have been challenged by a wide range of immuno-oncology therapies, particularly those with administrative stopping rules that mandate treatment discontinuation at a prespecified time point. A gradual waning of their treatment effects has been considered plausible and received growing attention from HTA stakeholders over the past decade. However, existing statistical methods often rely on unnecessarily strong waning assumptions.ObjectiveWe demonstrate the blended hazard method as a flexible way to account for treatment effect waning while incorporating external evidence in survival extrapolation.MethodThe blended hazard method fits separate parametric survival regression models to the observed randomized controlled trial data and external data that inform the common long-term hazard when there is no treatment effect. For each arm, the fitted internal and external hazard functions are blended based on a time-varying weight function, so that the blended hazard is initially dominated by the fitted internal hazard, then gradually approaches the fitted external hazard over a blending interval, and is finally dominated by the fitted external hazard. The time and rate of blending the internal and external information can be controlled by the weight function to allow for sensitivity analyses. NICE TA366 on pembrolizumab for advanced melanoma not previously treated with ipilimumab is used as a case study to demonstrate the practical implementation of this method.ResultsExtrapolations and restricted mean survival times from the blended hazard method closely matched the updated 7-y trial follow-up and showed better consistency than the TA366 base case across all sensitivity analysis scenarios.ConclusionThe method explicitly accounts for gradual treatment effect waning while incorporating external evidence and offers the flexibility to accommodate a broad range of waning scenarios, thereby effectively characterising uncertainty in extrapolation.HighlightsTreatment effect waning is considered plausible in survival extrapolation for many therapies, particularly those with treatment-stopping rules. However, there is a shortage of appropriate methods to model this phenomenon, and existing approaches either rely on strong waning assumptions or address it only as a post hoc check.We demonstrate the blended hazard method as a possible approach to account for treatment effect waning while incorporating external evidence.The blended hazard method possesses the flexibility to accommodate a wide range of waning scenarios, thereby relaxing unnecessarily strong assumptions and effectively characterizing uncertainty in survival extrapolation.
A Microsimulation Model for Chronic Kidney Disease Progression in Type 2 Diabetes Patients in the United States: Michigan Model for Diabetes-Chronic Kidney Disease Model.
Ye W, Ding X, Li J … +8 more , Singh R, Farej R, Kuo S, Elliott JC, Lott J, Yang CT, Kong SX, Herman WH
Med Decis Making · 2026 Jun · PMID 42317029 · Publisher ↗
ObjectivesTo develop and validate a microsimulation model to estimate the health outcomes and costs of chronic kidney disease (CKD) in type 2 diabetes (T2D) to inform health policies and reduce the burden of CKD.MethodsW...
ObjectivesTo develop and validate a microsimulation model to estimate the health outcomes and costs of chronic kidney disease (CKD) in type 2 diabetes (T2D) to inform health policies and reduce the burden of CKD.MethodsWe developed a comprehensive model for CKD in type 2 diabetes that assesses the impact of risk factors on the progression of urine albumin-to-creatinine ratio and estimated glomerular filtration rate and their impact on stroke, myocardial infarction (MI), congestive heart failure (CHF), end-stage kidney disease (ESKD), and death without dialysis or transplant using individual-level longitudinal data for T2D populations and summary data from published literature. We internally validated the model using data from the Chronic Renal Insufficiency Cohort (CRIC) of patients with T2D and CKD over 7 y and externally validated the model using the Canagliflozin and Renal Events in Diabetes with Established Nephropathy Clinical Evaluation (CREDENCE) trial data over 3 y.ResultsThe simulated event rates of ESKD, stroke, MI, CHF, and total mortality and the related 95% confidence intervals included the observed event rates in both the internal and external validation cohorts. Using this new model, we showed that lowering the blood pressure target from 140/90 to 120/80 mm Hg in patients with T2D and CKD was cost-saving at the population level.ConclusionThe Michigan Model for Diabetes-Chronic Kidney Disease (MMD-CKD) model provides accurate estimates of disease progression in patients with T2D and CKD. Modeling disease progression in this population will facilitate future assessments of the cost-effectiveness of systematic screening and interventions for CKD, which may reduce the health and economic burden of CKD in T2D. This model can also serve as a tool for predicting the clinical outcomes of individual patients with T2D and CKD. MMD-CKD 1.0 R Shiny app and is publicly available (https://michigandiabetesmodelinggroup.github.io/Software_App).HighlightsWe developed and validated a microsimulation model to simulate kidney disease progression, cardiovascular outcomes, mortality, direct medical costs, and quality of life in patients with chronic kidney disease (CKD) and type 2 diabetes (T2D).This model can be used to evaluate the long-term economic burden of CKD in T2D patients in the United States as well as to assess the costs and benefits associated with specific health policies and interventions.In addition, this model can help inform individual patients of their risk of end-stage kidney disease (ESKD) and cardiovascular events, thereby facilitating shared decision making.
Cardiovascular Risk Estimation and Statin Adherence: A Historical Cohort Study.
Finnikin S, Willis B, Evans T … +4 more , Finney B, Hui KNC, Khatib R, Marshall T
Med Decis Making · 2026 Jun · PMID 42289995 · Publisher ↗
BACKGROUND: Adherence to statins for the primary prevention of cardiovascular disease (CVD) is low but may be improved through shared decision making (SDM). CVD risk estimation is fundamental to SDM at statin initiation,...
BACKGROUND: Adherence to statins for the primary prevention of cardiovascular disease (CVD) is low but may be improved through shared decision making (SDM). CVD risk estimation is fundamental to SDM at statin initiation, and the absence of a CVD risk score may indicate that SDM has not taken place. This study explores statin initiation decisions by investigating whether CVD risk is associated with adherence to statins and CVD outcomes. DESIGN: A cohort of statin-naïve patients aged 40 to 84 y initiated on statins for primary prevention between 2017 and 2020 was identified and categorized by the presence or absence of a CVD risk score at statin initiation. Multivariable modeling determined the association between CVD risk score and statin adherence and persistence. A secondary analysis investigated the relationship with CVD outcomes and death. RESULTS: A total of 255,730 patients were included with a mean follow-up of 4.6 y. The presence of a CVD risk score (67.7% of patients) was associated with a 7% increase in adherence and an 8% reduction in discontinuations as well as a 15% reduction in CVD and all-cause mortality. CONCLUSION: The presence of a CVD risk score is associated with improvements in both adherence and persistence, which could indicate that the quality of initiation consultations, and a focus on SDM, improves the utility of statins. In addition, CVD risk scoring is associated with a large decrease in CVD/death, which cannot fully be explained by improvements in statin adherence and persistence, an important finding necessitating further investigation. IMPLICATIONS: Efforts to improve the integration of CVD risk scores and SDM in statin initiation consultations may significantly improve statin utilization and CVD outcomes.HighlightsWe present a comprehensive assessment of the adherence to and persistence with statins for the primary prevention of CVD, which, for the first time, is linked to the presence or absence of CVD risk scoring.CVD risk scoring is associated with improvements in both adherence and persistence as well as significant reductions in CVD and all-cause mortality.There should be renewed focus on the content of statin initiation consultations ensuring that CVD risk is communicated using a shared decision-making approach.Further consideration should be given to the important discrepancy in CVD and death seen when CVD risk scoring is, and is not, used in practice.
Taste or Scale? Methodological Approach to Health Preferences Comparison across Groups.
Tarfasa Faro S
Med Decis Making · 2026 Jun · PMID 42289833 · Publisher ↗
BACKGROUND: Researchers widely use discrete choice experiments (DCEs) to assess health preferences across subgroups. However, variations in decision consistency, rather than true differences in preferences, can drive obs...
BACKGROUND: Researchers widely use discrete choice experiments (DCEs) to assess health preferences across subgroups. However, variations in decision consistency, rather than true differences in preferences, can drive observed utility differences. Despite the growing use of DCEs to assess health preference heterogeneity, recent studies highlight a persistent lack of methodological transparency in accounting for unobserved heterogeneity, underscoring the need for technically robust approaches to support credible and actionable comparisons across groups. This study improves health preference research methods by directly addressing scale heterogeneity and reducing bias when comparing subgroups. METHODS: A simulated DCE evaluated hypothetical cancer treatments across 2 imagined groups (patients, caregivers). Each task presented 3 alternatives (including a status quo), varying in months gained, survival rate, side-effect severity, and out-of-pocket cost. Mixed logit models were estimated. Scale heterogeneity was addressed using the Swait-Louviere 2-step procedure. Willingness to pay (WTP) was computed and compared across groups via the Poe et al. (2005) simulation-based test. RESULTS: The Swait-Louviere test confirmed significant scale heterogeneity ( < 0.05) but no meaningful taste differences ( > 0.10). Once scale effects were accounted for, the analysis revealed a shared preference structure across patients and caregivers, with variability driven by inconsistent decision making rather than true preference divergence. Consistent with this, none of the between-group WTP differences were statistically significant, reinforcing the absence of meaningful subgroup contrasts and underscoring the importance of separating scale from taste to avoid biased inference. CONCLUSIONS: Adjusting for scale heterogeneity strengthens DCE validity by reducing bias from decision noise and enabling accurate subgroup comparisons. Using simulated data, this study applied the Swait-Louviere 2-step and scale-invariant WTP contrasts to separate taste from scale; both methods converged, showing that heterogeneity reflected scale rather than true preference differences, with negligible WTP gaps. Routine scale diagnostics, taste (preference) tests under equalized scale, and welfare space reporting are recommended to ensure valid inference. However, as this study used simulated data with no real respondents, its findings are illustrative only and not intended for real-world inference; generalizability and external drivers of scale heterogeneity were not assessed.Key HighlightsThe study enhances methodological rigor by explicitly addressing scale heterogeneity-an often-overlooked bias that improves the validity and real-world relevance of preference-based insights.Applying the Swait-Louviere test and willingness to pay, whenever possible, enables researchers to distinguish true preference differences from response inconsistency across choice datasets.The findings advocate for the routine inclusion of scale diagnostics in stated-preference research to strengthen health decision making and modeling practice.
Mind the Gap: Impact of New Labels on Public Perceptions and Calculated Risk of Adverse Outcomes after a Melanoma In Situ Diagnosis-A Secondary Analysis of an Online Randomized Experiment.
Wu Z, Boroumand F, Nickel B … +4 more , Adamson AS, Parker L, Davies E, Bell KJL
Med Decis Making · 2026 Jun · PMID 42289825 · Publisher ↗
BACKGROUND: Alternative diagnostic labels for melanoma in situ may better reflect its lower risk (15-y survival of 98%) compared with invasive melanoma (10-y survival ranging from 98% for American Joint Committee on Canc...
BACKGROUND: Alternative diagnostic labels for melanoma in situ may better reflect its lower risk (15-y survival of 98%) compared with invasive melanoma (10-y survival ranging from 98% for American Joint Committee on Cancer stage IA to 19% for stage IV). DESIGN: Secondary analysis of an online randomized experiment in Australian adults without melanoma. Participants were randomized to a hypothetical diagnosis of "melanoma in situ (MIS)" (control), "low-risk melanocytic neoplasm," or "low-risk melanocytic neoplasm, in situ" and completed a survey. OUTCOMES: Perceived risk measures were future invasive melanoma and mortality risk (0%-100%), comparative risk, affective risk, and vulnerability (7-point Likert scales). Calculated risk measures were lifetime invasive melanoma risk (from participants' risk factors) and melanoma mortality probability (Australian sex-/age-specific mortality rates). ANALYSIS: An intention-to-treat analysis across randomized groups was performed, unadjusted and adjusted for covariates (linear regression models). RESULTS: In total, 1,668 adults were recruited. Compared with MIS, perceived melanoma mortality risk was lower for low-risk melanocytic neoplasm (-10.4%, 95% confidence interval [CI]: -13.1% to -7.63%, < 0.001) and for low-risk melanocytic neoplasm, in situ (-7.4%, 95% CI: -10.2% to -4.6%, < 0.001). Similar patterns were observed for perceived risk of invasive melanoma; comparative, affective risk; and vulnerability. Participants in all groups substantially overestimated their lifetime risk of invasive melanoma (by 48.7%) and of dying from melanoma (by 32.0%) compared with the calculated risk; overestimation was lower in alternative label groups. CONCLUSIONS: Diagnostic labels without the word "melanoma" reduced risk overestimation, supporting MIS relabeling to mitigate overdiagnosis harm by reflecting its largely indolent nature. TRIAL REGISTRATION: ANZCTR: 386943HighlightsAlternative diagnostic labels for melanoma in situ that do not include the word "melanoma" significantly decreased perceived risk compared with melanoma in situ.Participants substantially overestimated their risk; alternative labels reduced this overestimation of perceived risk compared with calculated risk.A new label for melanoma in situ may better communicate the lower risk of adverse outcomes for this lesion compared with invasive melanoma. This may reduce patient anxiety and allow for management decisions that align with their values and preferences.
A Metamodel-Based General-Purpose Autocalibration Tool for Simulation Models.
Khaniyev T, Işık ES, Chhatwal J … +2 more , Yıldırım İF, Ayer T
Med Decis Making · 2026 Jun · PMID 42267600 · Publisher ↗
Simulation calibration is the process of configuring a simulation model's parameters to improve the agreement between the model output and the desired calibration targets (e.g., observed historical data). For most realis...
Simulation calibration is the process of configuring a simulation model's parameters to improve the agreement between the model output and the desired calibration targets (e.g., observed historical data). For most realistic simulation models, this calibration process can be quite computationally expensive, as it requires running the simulation model for each parameter combination. To alleviate this problem, metamodels offer a tradeoff between accuracy and computational efficiency for extensive simulative analysis with a highly complex parameter space. In this study, we examine 4 simulation calibration approaches. Randomly-Simulate (RS) is a simulation-based benchmark widely used in the literature. Optimally-Predict (OP) is an optimization-based approach that we adapt to the simulation calibration setting. Building on these 2 baselines, we introduce 2 hybrid strategies, Predict-then-Simulate (PtS) and Simulate-then-Predict (StP), which combine simulation runs and metamodel-based optimization in complementary orders. We compare all 4 methods in terms of calibration accuracy and computational cost. While the metamodel-based OP approach substantially reduced computational cost relative to RS and identified parameter combinations near the optimal configuration, the hybrid strategies delivered superior calibration performance. In particular, the PtS approach, which combines metamodel-based optimization with targeted simulation refinement, achieved on average a 46% reduction in total actual error compared with the RS benchmark, while maintaining computational efficiency. The study introduces a novel metamodel-based optimization approach to simulation calibration and illustrates its potential benefits for computationally expensive studies. While developed for deterministic targets, the method provides a foundation for future extensions to settings involving stochastic simulation outputs and other forms of model uncertainty. An open-access Python implementation of the proposed framework is provided to facilitate adoption and reproducibility.HighlightsA metamodel-based optimization approach is proposed for calibrating simulation model parameters, which can offer computational advantages particularly in cases in which direct calibration is expensive due to complex or high-dimensional simulation models.A hybrid approach that narrows down the search space via metamodel-based optimization and then uses simulation runs for fine-tuning offers a sweet spot in the tradeoff between computational efficiency and accuracy.
The Dutch Implantable Cardioverter-Defibrillator Decision Aid in Clinical Practice: A Stepped-Wedge Randomized Controlled Trial.
Yilmaz D, Egorova AD, Grauss R … +6 more , Spierenburg HAM, Venooy K, van Woerkens LPM, Robles de Medina R, Schalij MJ, van Erven L
Med Decis Making · 2026 Jun · PMID 42251498 · Publisher ↗
IntroductionThe role of shared decision making (SDM) has become increasingly pivotal, particularly in nuanced choices such as those involving implantable cardioverter-defibrillator (ICD) therapy. This study evaluates the...
IntroductionThe role of shared decision making (SDM) has become increasingly pivotal, particularly in nuanced choices such as those involving implantable cardioverter-defibrillator (ICD) therapy. This study evaluates the impact of the Dutch ICD Decision Aid on SDM in patients up for ICD implantation or replacement.MethodsA stepped-wedge randomized controlled trial was conducted across 6 Dutch hospitals between February 2018 and September 2019, involving patients eligible for ICD implantation or pulse-generator exchange. SDM experiences of the patients and involved medical professionals were assessed using SDM-Q-9 and SDM-Q-Doc questionnaires, respectively. The Decisional Conflict Scale (DCS) scores measured effective decision making. The intervention group received the decision aid on top of standard care.ResultsA total of 150 patients and 233 health care providers were included in the study. For health care providers, SDM scores did not differ: the SDM-Q-Doc median score was 36 (28-38) in the control phase and 35 (33-40) in the intervention phase ( = 0.81). Patients in both the intervention and control groups demonstrated high SDM scores as well. Decisional conflict scores were low: the median DCS score was 12.5 (4.3-23.4) in the intervention phase and 16.4 (6.25-25.0) in the control phase ( = 0.45). Patients with a higher education provided more correct answers to the theoretical knowledge questions. In addition, patients up for a pulse-generator exchange also had significantly more correct answers.ConclusionsAlthough the Dutch ICD Decision Aid did not result in significant differences in SDM scores or levels of decisional conflict between patient groups, both measures remained consistently favorable overall. The decision aid still holds promise as a valuable resource. Efforts should focus on refining decision-making tools and improving patient knowledge and the quality of patient-centered care.HighlightsA digital decision aid did not significantly increase shared decision-making (SDM) scores for patients and health care providers, as SDM levels were already high across all groups.Despite high SDM scores, patient knowledge about implantable cardioverter-defibrillator (ICD) therapy remained low, highlighting a gap in understanding.Patients with higher education or prior experience with ICDs demonstrated better knowledge retention, indicating the need for tailored educational interventions.The study emphasizes the ongoing challenge of ensuring unbiased, well-informed decision making in ICD therapy, especially during pulse-generator replacements.
Clinical and Cost-Effectiveness of Shared Decision Making: Evidence from a Prospective Multicenter Study Evaluating a Hospital-Based Intervention in Germany.
Barzen Coors M, Scheibler F, Rüffer JU … +5 more , Wehkamp K, Schneider U, Schüttig W, Geiger F, Sundmacher L
Med Decis Making · 2026 Jun · PMID 42249569 · Publisher ↗
ObjectiveTo evaluate the clinical and cost-effectiveness of SHARE TO CARE (S2C), a complex intervention for hospital-wide, systematic implementation of shared decision making.MethodsWe analyzed clinical effectiveness, he...
ObjectiveTo evaluate the clinical and cost-effectiveness of SHARE TO CARE (S2C), a complex intervention for hospital-wide, systematic implementation of shared decision making.MethodsWe analyzed clinical effectiveness, health care resource utilization, and implementation costs of S2C from the statutory health insurance perspective using a quasi-experimental difference-in-differences approach with evidence from the Department of Neurology. Clinical outcomes included inpatient hospital admissions, emergency department admissions, and rates of standard and advanced imaging procedures. Implementation costs comprised those related to the conception, development, process integration, ongoing support, and auditing of S2C. Health care utilization data covered inpatient and outpatient care, pharmaceuticals, therapeutic services, assistive devices, and nursing care. We conducted sensitivity analyses to account for uncertainties.FindingsS2C was associated with a reduction in inpatient hospital admissions, emergency department admissions, and imaging rates in the intervention group. The cost analyses aligned with these findings, showing reduced total costs and health care resource utilization in the intervention group. Although none of the estimates reached the predefined thresholds for statistical significance, the primary analysis yielded weak evidence ( < 0.1) of a reduction in emergency department admissions in the intervention group. Overall, savings outweighed the costs of implementing S2C, suggesting cost-effectiveness.ConclusionsS2C has the potential to reduce emergency department admissions and overall health care costs from the statutory health insurance perspective. Further research should investigate generalizability, the timing of the treatment effect, and potential biases introduced by the COVID-19 pandemic. The demonstrated effects of shared decision making (SDM) have encouraged statutory health insurances in Germany to offer additional reimbursement for clinics certified under the S2C program. The S2C model illustrates how payers and providers can collaborate to facilitate the nationwide implementation of SDM.HighlightsThe implementation of SHARE TO CARE (S2C) was associated with a statistically nonsignificant reduction in emergency department admissions after 1 y from the statutory health insurance perspective, based on data from the Department of Neurology.The cost savings from reduced health care utilization outweighed the implementation costs, and despite not reaching statistical significance, the results support the potential cost-effectiveness of S2C.S2C has the potential for nationwide implementation as a systematic form of shared decision making.Future research should investigate the generalizability of the results to other health care settings.
Exploring Perceived Interactions between EQ-5D-5L and Bolt-ons Using Composite Time-Tradeoff Valuations: A Qualitative Study.
Pangestu S, Rencz F, Roudijk B … +2 more , Purba FD, Jakubczyk M
Med Decis Making · 2026 May · PMID 42206353 · Publisher ↗
ObjectivesThis study qualitatively explored how bolt-ons affect the perception of EQ-5D-5L core dimensions in a valuation context.MethodsSixty Indonesian adults (aged 20-67 y, 50% female) each valued 10 health states usi...
ObjectivesThis study qualitatively explored how bolt-ons affect the perception of EQ-5D-5L core dimensions in a valuation context.MethodsSixty Indonesian adults (aged 20-67 y, 50% female) each valued 10 health states using composite time tradeoff (cTTO). States were presented in either forward (EQ-5D-5L, then with 1 bolt-on, then 2) or backward (reversed sequence) order. Participants were assigned to 1 of 3 bolt-on dyads: vision and tiredness, cognition and social relationships, or skin irritation and self-confidence. In semistructured qualitative interviews, respondents described how adding or removing bolt-ons changed the perceived importance of the 5 core dimensions. We classified these changes as related either to measurement or to valuation, with the latter further categorized as a relative or absolute shift in importance.ResultsCognition and vision generated the most shifts in perceived importance in the forward and backward groups, respectively. Regardless of ordering group, most shifts occurred between the EQ-5D-5L alone and the version with 1 bolt-on (first presented in the dyad), with significantly fewer shifts observed between the 1-bolt-on and 2-bolt-on states. In the forward group, most shifts were classified as measurement (56%) or relative preference (29%), while the reverse was true in the backward group: relative preference (53%) or measurement (31%). Absolute preference was least common across both groups.ConclusionsThis is the first study to explore how individuals reason when valuing EQ-5D-5L+bolt-on health states. Our findings suggest that interactions between dimensions are complex and may be influenced by presentation order. Further qualitative research should directly investigate absolute preferential reasoning.HighlightsNo studies have qualitatively explored how individuals value EQ-5D health states with bolt-ons. Understanding how bolt-ons influence reasoning and interact with core dimensions is crucial for informing valuation methods and modeling strategies.Our findings show that bolt-ons can alter how participants perceive the importance of EQ-5D-5L dimensions, although changes are mostly not preference driven. Participants often rely on accessible reasoning, such as conceptual associations between dimensions. Effects vary by the bolt-on used and presentation order.Interactions between bolt-ons and core dimensions complicate efforts to develop robust valuation approaches. Future qualitative studies should aim to capture preference-based reasoning, while quantitative work is needed to disentangle preferential from nonpreferential effects.
The Potential for a Benefit-Estimating Algorithm to Improve Recommendations for Preventive Services: Comparing Algorithm Recommendations with Those of Primary Care Providers.
Conte ML, Boonstra P, Caverly TJ … +4 more , Fishstrom A, Flynn A, Taksler GB, Friedman CP
Med Decis Making · 2026 May · PMID 42159175 · Full text
PurposeTo compare an established benefit-estimating algorithm for recommending and prioritizing preventive services for a patient (Individualized Precision Prevention; IPP) with concordant rankings from primary care prov...
PurposeTo compare an established benefit-estimating algorithm for recommending and prioritizing preventive services for a patient (Individualized Precision Prevention; IPP) with concordant rankings from primary care providers (PCPs).MethodsWe developed 12 realistic routine patient care scenarios focused on preventive services and recruited 40 PCPs to rank the priority of recommended preventive services. Our analysis compared the benefit-estimating algorithm's rankings of preventive services for each of the 12 patient scenarios to the PCPs' rankings using length-dependent rank-biased overlap (LDRBO) calculations. Moderate concordance would suggest that the computer algorithm presented an opportunity to improve preventive care, whereas very high or low concordance would call into question what the algorithm could contribute to clinical practice.ResultsFor all 12 patient care scenarios, comparing the benefit-estimating algorithm's output to the combined priority rankings from all PCPs yields a mean value of 0.45, corresponding to a moderate level of concordance or agreement between the numeric rankings of the algorithm and the expert provider rankings. This study illustrates the potential importance of having computed IPP recommendations readily available for point-of-care decision making by PCPs.ConclusionWe demonstrate that this approach aligned with the overall judgment of clinical experts and may help providers prioritize preventive services in time-constrained clinical contexts. The modest correlation between the benefit-estimating algorithm and expert providers suggests that, in some cases, the algorithm has the potential to provide useful advice about preventive services during care.HighlightsUsing scenarios, we compared how primary care providers and an algorithm prioritized recommendations for preventive services based on individual information about a patient.The providers' rankings of the clinical importance of preventive services were moderately concordant with rankings produced by the algorithm, suggesting that the algorithm presents an opportunity to improve the effects of preventive care.For half of the scenarios, the algorithm recommended one preventive service that was not in the PCPs' consensus top 3, suggesting that the algorithm may raise provider awareness of services that may be beneficial to specific patients.An algorithm-driven approach to individualized precision prevention that uses a patient's data to generate personalized recommendations of preventive services can help providers and patients identify and prioritize high-priority preventive services together.
Communicating Time-to-Event Treatment Effects in Randomized Trials: A Randomized Experiment among General Practitioners.
Giese H, Gaissmaier W, Kuss O … +1 more , Wegwarth O
Med Decis Making · 2026 May · PMID 42154459 · Publisher ↗
BackgroundTime-to-event outcomes from randomized controlled trials (RCTs) are often communicated without clearly conveying how treatment effects evolve over time. This can limit clinicians' ability to interpret results a...
BackgroundTime-to-event outcomes from randomized controlled trials (RCTs) are often communicated without clearly conveying how treatment effects evolve over time. This can limit clinicians' ability to interpret results and support patient decision making.MethodsWe conducted an online experiment with 250 German general practitioners in April 2024. Participants evaluated treatment effects presented in 4 common formats: hazard ratios, prolongation of life, restricted mean survival time (RMST), and absolute risk reduction. We assessed 1) understanding, defined as the ability to correctly compare effect sizes (small, medium, large), and 2) acceptability of each format. We also tested whether providing baseline risk information (control group outcomes) improved performance.ResultsParticipants' effectiveness ratings did not differ between small, medium, and large treatment effects in any format. RMST presentations were judged less effective but more acceptable than the other formats. Providing baseline risk information did not influence effectiveness ratings or acceptance.LimitationsThe use of a convenience sample may limit generalizability.ConclusionsGeneral practitioners struggled to interpret time-to-event treatment effects across all formats. Although RMST was preferred, no format supported accurate understanding of effect size.ImplicationsCurrent approaches may not adequately support communication of time-to-event outcomes in clinical practice. More effective strategies are needed, likely combining absolute time-based measures with clear contextual information such as baseline risk.Registration: 10.17605/OSF.IO/U69YMHighlightsTime-to-event outcomes from randomized trials are difficult for clinicians to interpret.General practitioners were unable to distinguish between small, medium, and large treatment effects across formats.Restricted mean survival time was preferred but did not improve understanding of time-to-event effects.Current formats do not support communication of time-to-event outcomes in clinical decision making.
Modeling Health Effects of Alternative Treatment Options during Surgical Delay to Inform Prioritization of Surgical Care.
van Alphen A, Venema E, Yang B … +5 more , Reckers-Droog V, Huis In 't Veld L, Baatenburg de Jong R, Lingsma H, Value Based Operation Room Triage Team Collaborators
Med Decis Making · 2026 Jul · PMID 42153774 · Full text
ObjectivesTo estimate the health effects of temporarily substituting surgeries with alternative treatments to inform discussions on prioritization strategies during surgical capacity constraints.Study Setting and DesignT...
ObjectivesTo estimate the health effects of temporarily substituting surgeries with alternative treatments to inform discussions on prioritization strategies during surgical capacity constraints.Study Setting and DesignThis study was conducted in the context of the Dutch health care system. We used a decision model to estimate the health effects of temporal substituting surgery with an alternative treatment option for 11 diseases. Model outcome was the number of disability-adjusted life-years (DALYs) associated with the alternative treatment during the waiting period for surgery. For each "disease-surgery" combination, the alternative treatment with the lowest DALYs was labeled the "best alternative." These "best alternatives" were ranked in descending order of DALYs, suggesting a prioritization of surgeries.Data Sources and Analytic SampleParameter values were obtained from registries, scientific literature, and expert consultations.Principal FindingsA total of 23 "disease-surgery-alternative treatment" combinations were evaluated. Substituting immediate pacemaker implantation with optimal medical therapy for symptomatic bradycardia results in the largest health loss per month, estimated at 0.13 DALYs (95% confidence interval [CI] 0.08 to 0.19). Replacing implantable cardioverter-defibrillator implantation with optimal medical therapy for ventricular arrhythmias results in the second-largest health loss of 0.08 DALYs per month delay (95% CI 0.05 to 0.10). In contrast, surgeries in which substitution with alternative treatments results in minimal health impact include radiation therapy instead of laser treatment for laryngeal cancer T1-2 (-0.01 DALY, 95% CI -0.02 to 0.00) and chemoradiation instead of surgical resection for laryngeal cancer T3-4 (-0.01 DALY, 95% CI -0.02 to 0.01).ConclusionsOur study facilitates the comparison of surgery and alternative treatment options across diseases and surgical disciplines and can contribute evidence-based discussions on prioritization among health care stakeholders.HighlightsThis study quantifies the health impact of substituting surgery with alternative treatments across 11 diseases using disability-adjusted life-years (DALYs).Some alternative treatments resulted in minimal or no health loss, while others were associated with substantial health detriment.The findings support evidence-based prioritization of surgical care by identifying where substitution is most and least harmful to patient outcomes.
Potential Paths Forward from "On Representations and Quantifications of Uncertainty".
Leshno M, Goldhaber-Fiebert JD
Med Decis Making · 2026 Jul · PMID 42093292 · Full text
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Revaccination after an Adverse Event: A Patient Uses Bayesian Reasoning to Weigh COVID and Vaccine Risks.
Hansen J
Med Decis Making · 2026 May · PMID 42093291 · Publisher ↗
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A Tutorial on Discrete Event Simulation Models Using a Cost-Effectiveness Analysis Example in R.
Lopez-Mendez M, Goldhaber-Fiebert JD, Alarid-Escudero F
Med Decis Making · 2026 Jul · PMID 42087677 · Publisher ↗
Discrete event simulation (DES) is a flexible and computationally efficient approach for modeling diverse processes; however, DES remains underutilized in health care and medical decision making due to a lack of reliable...
Discrete event simulation (DES) is a flexible and computationally efficient approach for modeling diverse processes; however, DES remains underutilized in health care and medical decision making due to a lack of reliable and reproducible implementations. We developed an open-source DES framework to simulate individual-level state-transition models (iSTMs) in continuous time accounting for treatment effects, time dependence on state residence, and age-dependent mortality. Our DES implementation employs a modular and easily adaptable structure, with each module corresponding to a unique transition between health states. To simulate the evolution of the process (i.e., individual state transitions), we adapted the next-reaction algorithm from the stochastic chemical reactions literature. Simulation-time dependence (age-dependent mortality) and state residence time dependence (transition from sick to sicker) are seamlessly incorporated into the DES framework via validated nonparametric and parametric sampling routines (e.g., inversion method) of event times. Treatment effects are integrated as scaling factors of the hazard functions (proportional hazards). We illustrate the framework's benefits by implementing the Sick-Sicker Model in R and conduct a cost-effectiveness analysis and probabilistic analysis. We also obtain epidemiological outcomes of interest from the DES output, such as disease prevalence, survival probabilities, and distributions of state-specific dwell times. Our DES framework offers a reliable and accessible alternative that enables the simulation of more realistic dynamics of state-transition processes at potentially lower implementation and computational costs than discrete-time iSTMs.HighlightsDiscrete event simulation (DES) is a flexible and efficient approach to simulate diverse processes in model-based decision analysis.The tutorial presents an open-source DES framework to simulate individual-level state-transition models (iSTMs) in continuous time.The modular structure of our DES framework accommodates treatment effects, time-dependent transitions, and age-dependent mortality using validated sampling methods.The coded example in R uses the Sick-Sicker Model to compute a cost-effectiveness analysis, epidemiological outcomes, probabilistic analysis, and value-of-information analysis.
A Microsimulation-Based Approach for Mitigating Societal Bias in Chronic Kidney Disease Data.
Foryciarz A, Alarid-Escudero F, Basel G … +5 more , Cusick MM, Phillips RL, Bazemore A, Adams AS, Rose S
Med Decis Making · 2026 Jul · PMID 42050893 · Full text
PurposeThe data-generating mechanisms underlying health care data are infrequently considered, leading to inequitable equilibria being reinforced throughout the care continuum. As race-based criteria are reassessed, incl...
PurposeThe data-generating mechanisms underlying health care data are infrequently considered, leading to inequitable equilibria being reinforced throughout the care continuum. As race-based criteria are reassessed, including in chronic kidney disease, the effect of those criteria on patterns of disease progression should also be reevaluated. We proposed a microsimulation model for attenuating societal bias in primary care chronic kidney disease data to study this.MethodsWe developed a continuous-time, discrete-event individual-level simulation model of kidney function decline, measured by estimated glomerular filtration rate (eGFR). The model simulates individual eGFR trajectories over time and enables generating counterfactual outcome distributions that would have been observed in the absence of race-based diagnosis and treatment criteria. eGFR decline is accelerated by hypertension, diabetes, and reaching chronic kidney disease stage 3a and can be delayed by interventions, which are applied based on eGFR level, measured with or without an adjustment for Black race. A Bayesian calibration procedure was applied to identify rates of eGFR decline corresponding to stage distributions in the cohort.ResultsUnder the counterfactual scenario without a race adjustment, Black individuals qualify for diagnosis earlier, and non-Black individuals later, than under the reference scenario with race adjustment. The difference was largest for earlier stages and smaller at each consecutive stage. We do not observe differences in life expectancy between the 2 scenarios.LimitationsLarge variability in the prevalence of treatment and heterogeneity in treatment effectiveness may affect our results.ConclusionsBeyond estimating the clinical consequences of the eGFR equation change, our work offers an alternative to previously proposed data-debiasing approaches. The simulated data can be used to inform future interventions and policy decisions.HighlightsWe developed a microsimulation model of chronic kidney disease progression with primary care data that reflect the effect of removing race-based diagnostic and treatment criteria.The removal of race-based diagnostic criteria in our simulations changed the timing of qualification for chronic kidney disease diagnosis, ranging from 0.6 y to 9.6 y, with opposite effects for Black and non-Black patients.The simulated differences in expected survival after removing the race adjustment did not exceed 2 mo among individuals who developed chronic kidney disease.The explicit representation of the data-generation process can help anticipate the effect that policy changes can have on clinical data distributions.
Using Regression Discontinuity in Time to Strengthen Real-World Evidence: A Case Study in Lung Cancer.
Chen NC, Zemplenyi AT, Adamson B … +4 more , Kaizer AM, O'Bryant CL, McQueen RB, Anderson KE
Med Decis Making · 2026 Jul · PMID 42037076 · Publisher ↗
ObjectiveReal-world evidence is increasingly leveraged to assess treatment effectiveness outside of clinical trials, yet unmeasured confounders and missing data pose challenges to causal inference, which is particularly...
ObjectiveReal-world evidence is increasingly leveraged to assess treatment effectiveness outside of clinical trials, yet unmeasured confounders and missing data pose challenges to causal inference, which is particularly problematic when incorporating historical controls that lack recent prognostic factors. We applied the regression discontinuity in time (RDiT) design, a quasi-experimental approach, in a real-world case study of second-line pembrolizumab versus docetaxel for advanced non-small-cell lung cancer (aNSCLC). We compared results from the RDiT method with time-stratified inverse probability treatment weighting (ts-IPTW), benchmarking results against long-term trial data.MethodsWe conducted a retrospective cohort study of patients who received second-line pembrolizumab or docetaxel after platinum-based chemotherapy between 2011 and 2023. The introduction of pembrolizumab (Q2 2016) served as the discontinuity threshold in an RDiT framework, with treatment probabilities estimated via logistic regression. Survival outcomes, including hazard ratios (HRs), median overall survival, and restricted mean survival time (RMST), were compared across RDiT, ts-IPTW, and reconstructed trial estimates.Data SourcesThis study used the US-based, electronic health record-derived deidentified Flatiron Health Research Database.ResultsAmong 1,975 patients (1,014 pembrolizumab, 961 docetaxel), RDiT estimated an adjusted median survival of 11.5 mo for pembrolizumab versus 6.9 mo for docetaxel (HR 0.65, 95% confidence interval [CI]: 0.48, 0.89), compared with ts-IPTW (HR 0.52, 95% CI: 0.42, 0.64) and 5-y trial data (HR 0.70, 95% CI: 0.61, 0.80). RDiT produced smaller survival gains that were better aligned with trial results relative to ts-IPTW, suggesting it may help mitigate unmeasured confounding in real-world studies.ConclusionsThe RDiT may provide effect estimates more consistent with trial data, particularly when confounding is a concern. More research is required to examine its performance in other applications.HighlightsThe regression discontinuity in time (RDiT) method incorporates historical controls and addresses unobserved confounders to strengthen causal inference.Compared with traditional propensity score-based approaches, RDiT accommodates historical and concurrent controls and reduces reliance on comprehensive measurement of observed confounders when treatment practices or biomarkers change over time.As real-world evidence increasingly informs regulatory, coverage, and pricing decisions, rigorous analytic methods are essential to produce credible and decision-relevant estimates.
Assessment of Survival and the Decision to Engage in Palliative Care when Facing a Defeat in the ICU.
Gad H, Diedrich D, Laudanski K
Med Decis Making · 2026 Apr · PMID 42032898 · Publisher ↗
BackgroundMedical providers often face challenges in accurately predicting the survival of critically sick patients. Optimistic forecasts can lead to the overuse of resources, while overly cautious predictions might rest...
BackgroundMedical providers often face challenges in accurately predicting the survival of critically sick patients. Optimistic forecasts can lead to the overuse of resources, while overly cautious predictions might restrict treatments. This study examines the role of specific psychological factors, analyzed realistically and holistically, in predicting survival outcomes for intensive care unit patients.MethodsThis single-center cohort study evaluated health care providers (e.g., physicians, residents and fellows, and advanced practice practitioners) using two 7-d clinical vignettes. Providers assessed the need for mechanical ventilation (MV), renal replacement therapy (RRT), a percutaneous endoscopic gastrostomy (PEG) tube, or palliative care. Psychological factors were measured using scales that assessed ambiguity tolerance, rationality versus emotional defensiveness, anxiety related to uncertainty, decision-making style, and risk taking. These psychological traits were analyzed using a more realistic and holistic approach, employing cluster techniques. Providers also determined whether they had enough information to evaluate the patient's condition and compared their survival estimates to APACHE II scores.ResultsIn general, engagement in MV and RRT was common by day 2, although physicians were significantly less likely to recommend RRT. Providers generally suggested starting a palliative care consultation by day 6, with a noticeable shift on day 4. Three distinct composite psychological groups emerged: optimistic denial individuals (ODI), optimistic providers (OP), and resilient providers (RP). While these composite psychological groups did not significantly influence engagement in mechanical therapies, they did affect palliative care decisions: RP were more likely to request palliative care, whereas ODI were much less likely to do so. In contrast, individual psychological traits had nonsignificant correlations with the decision to use therapies. Providers initially overestimated survival probabilities during the first 3 d compared with APACHE II survival estimates. However, after day 4, this trend reversed, with providers becoming significantly more pessimistic versus the predictive score and increasingly requesting palliative care involvement.ConclusionsProviders' psychological profiles, rather than their clinical experience, significantly influenced decisions about organ-support therapies and palliative care. Survival estimates showed a biphasic pattern: initially, providers overestimated survival compared with APACHE II predictions, then became more pessimistic and more likely to consult palliative care after day 4.HighlightsIntensive care unit survival predictions by providers followed a biphasic pattern: optimistic early on, then increasingly pessimistic after day 4.Psychological traits such as denial and ambiguity tolerance influenced palliative care decisions more than clinical experience did.Resilient providers were more likely to initiate timely palliative care, while denial-prone providers delayed it.Clinicians and critical care teams should be aware of how their psychological makeup can affect patient care decisions and outcomes.
Exploring the Lack of Concordance between the Preferred and Actual Approach to Colorectal Cancer Screening in Older Adults: A Qualitative Study.
Kraun L, Leavitt L, Valentine KD … +8 more , Atlas SJ, Fairfield KM, Han PKJ, Mancini B, Richter JM, Siegel L, Simmons L, Sepucha K
Med Decis Making · 2026 Apr · PMID 42015676 · Publisher ↗
BackgroundConcordance, or alignment of care with patients' preferences, is a key component of high-quality decision making. Some patients may not have a clear preference, and others may not receive care aligned with thei...
BackgroundConcordance, or alignment of care with patients' preferences, is a key component of high-quality decision making. Some patients may not have a clear preference, and others may not receive care aligned with their preference-both situations indicating a lack of concordance. The reasons behind these situations remain poorly understood. This study explores the reasons for lack of concordance in colorectal cancer screening among older adults.MethodsInterviews were conducted with 160 older adults from the Promoting Informed Decisions About Colorectal Cancer Screening in Older Adults trial (NCT03959696) who did not meet the criteria for concordance. A thematic analysis of 152 analyzable interviews was performed to explore reasons for lack of concordance.ResultsFour themes summarize the different reasons for the lack of concordance: 1) provider discussion and the need for more guidance (e.g., patients reported very limited discussion and desire for more information), 2) age-related considerations (e.g., patients acknowledge that at their age, screening may no longer be needed), 3) changes in health condition (e.g., patients report other health issues that take priority over screening), and 4) the impact of COVID-19 and practical barriers (e.g., patients report a desire to avoid hospitals and procedures).ConclusionsThe lack of concordance stemming from limited discussion, guidance, or lack of clear preference signal low decision quality, whereas the lack of concordance from changing patient preferences over time has implications for timing of measurement. To improve concordance, patients need support to clarify their preferences as well as support to implement their preferred approach.HighlightsLimited provider discussion, age-related factors, changing health priorities, and COVID-19-related or practical challenges were identified as key contributors to lack of concordance.Achieving high concordance will require helping patients clarify their preferences, strengthening shared decision making, and providing implementation support.Researchers also need to be aware of evolving preferences and implications for timing of preference measurements.
Widespread Take-Home Naloxone Use Averted the Majority of Potential Opioid Poisoning Deaths in British Columbia, 2019-2024: A Bayesian Modelling Study.
Irvine MA, Ge W, Liu L … +4 more , Williams S, Lock K, Palis H, Kinniburgh B
Med Decis Making · 2026 Jul · PMID 42012358 · Publisher ↗
BackgroundThe toxic unregulated drug supply in North America continues to produce high rates of drug deaths. In response, several harm reduction interventions have been introduced and/or expanded, including take-home nal...
BackgroundThe toxic unregulated drug supply in North America continues to produce high rates of drug deaths. In response, several harm reduction interventions have been introduced and/or expanded, including take-home naloxone (THN). Estimating the impact is challenged by a lack of complete reporting data.ObjectiveThe aim of this study was to estimate the impact of interventions on drug deaths in British Columbia from January 2019 to September 2024.MethodsWe extended on a Bayesian hierarchical Markov chain model of drug poisoning events including interventions for overdose prevention sites and opioid agonist treatment. The extended model uses the reported number of THN kits used and distributed and all kits shipped to sites. These data are incorporated into the likelihood to estimate THN kit use during an opioid poisoning event by region and site type. Simulation studies evaluated the model's performance.ResultsThe estimated probability of THN kit use during an opioid poisoning event was 42.98% (95% credible interval [CrI]: 41.12-44.84) for kits distributed from community sites and 13.41% (95% CrI: 12.57-14.40) for overdose prevention sites. Correctional centers, pharmacy, and emergency department THN kits all had the lowest probability of use at 0.12% (95% CrI: 0.11-0.13), 1.04% (95% CrI: 0.96-1.13), and 0.65% (95% CrI: 0.60-0.71), respectively. The combined rate of deaths averted was 1,294 (95% CrI: 1,138-1,438) per 100,000 persons who inject drugs, which represents 78% (95% CrI: 76-80) of potential deaths.ConclusionDespite the increasing toxicity of the illegal drug supply, harm reduction interventions including THN have had a large impact on the number of drug deaths. Estimates of the impact of THN based on reported use alone would greatly underestimate the total impact.HighlightsWe developed a novel Bayesian hierarchical model to estimate take-home naloxone (THN) kit use during opioid poisonings using incomplete but complementary program and surveillance data.The model provides site-specific and regional estimates of kit use, highlighting significant differences by site type and geography.Simulation studies show the model can estimate the probability of THN kit use under realistic data limitations, supporting its use in policy evaluation.Public health decision makers can use this method to better assess and optimize harm reduction programs when direct usage data are scarce.
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