Medical Decision Making[JOURNAL]
Sun
200 papers
From Stories to Action: How Framing, Perspective, and Identifiability in Personal Narratives Influence Vaccination Decisions.
Kogut T, Pittarello A, Slovic P
Med Decis Making · 2026 May · PMID 41499196 · Full text
PurposeWe examined how different narrative aspects related to the COVID-19 pandemic influenced unvaccinated individuals' willingness to vaccinate (WTV) against a future virus. We tested whether the stories focused on the...
PurposeWe examined how different narrative aspects related to the COVID-19 pandemic influenced unvaccinated individuals' willingness to vaccinate (WTV) against a future virus. We tested whether the stories focused on the perspective of the actor (who chose to vaccinate or not) versus the affected (affected by that decision), framing the outcome as death versus survival, and presenting an identified individual versus an unidentified group.MethodsA total of 1,545 respondents read scenarios depicting individuals' (actors') decisions to either vaccinate against COVID-19 or refuse vaccination, alongside the framing of the consequences for the affected individuals: death versus survival. The protagonists were either identified by name and photo or described as a group of unidentified people. Participants reported their emotions, perceived risk from the virus and the vaccine, and their future WTV against a new virus. They also reported their past vaccination decisions.ResultsWhen the narrative focused on affected individuals, framing outcomes in terms of death increased WTV by heightening the perceived threat of the virus. Conversely, when the focus was on the actor, the lifesaving frame was more effective, especially when the actor was identified. A concrete case of someone vaccinated who saved others evoked positive emotions, boosting WTV.LimitationsOur hypothetical scenarios and the cross-sectional methodology might limit understanding of the long-term effects.ConclusionsScenarios highlighting a person who died increase the perceived threat of the virus and enhance WTV. Conversely, information about a person who was vaccinated and saved others boosts positive emotions and increases WTV.ImplicationsPublic health campaigns can boost vaccination by sharing stories of vaccinated individuals who saved lives, evoking positive emotions. Highlighting the virus's dangers can also raise the perceived threat and motivate uptake.HighlightsVariations in narratives influence unvaccinated individuals' willingness to vaccinate.Emphasizing the death of those affected evokes greater threat perception of the virus, enhancing vaccine intent.Personal stories of vaccinated individuals saving others can boost positive emotions and vaccination willingness.
Machine Learning for Intensive Care Unit Length-of-Stay Prediction: A Simulation-Based Approach to Bed Capacity Management.
Garber S, Okhrin Y
Med Decis Making · 2026 Apr · PMID 41454594 · Full text
BackgroundWhile machine learning (ML) models are increasingly used to predict outcomes in health care, their practical effect on health care operations, such as bed capacity management, remains underexplored. There is a...
BackgroundWhile machine learning (ML) models are increasingly used to predict outcomes in health care, their practical effect on health care operations, such as bed capacity management, remains underexplored. There is a variety of traditionally used evaluation metrics to analyze ML models; however, decision makers in health care settings require a deeper understanding of their implications for resource management. Traditional performance measures often fail to provide this practical insight.MethodsIn this work, we conduct a simulation study to evaluate the impact of ML-driven length-of-stay (LOS) predictions on intensive care unit (ICU) bed capacity management. Two classification models differing in terms of explainability and interpretability, logistic regression (LR) and extreme gradient boosting (XGB), are applied to predict ICU-LOS. We use the HiRID dataset containing high-frequency data of more than 33,000 patients. The predictions of the ML models are integrated into a simulation framework that replicates real-world ICU bed management, allowing for the assessment of the practical implications of using these algorithms in a clinical setting.ResultsThe application of both classification models results in improved capacity control regarding the key performance indicators in the simulation study, with XGB outperforming LR. While LR leads to slight overoccupancy in the ICU, slight underoccupancy can be observed when XGB is applied.ConclusionOur study bridges the gap between predictive accuracy and practical application by emphasizing the importance of evaluating ML models within the context of ICU capacity management. The simulation-based approach offers a more relevant assessment for health care practitioners, providing actionable insights that go beyond classical performance measures and directly address the needs of decision makers in clinical practice.HighlightsWe apply multiple classification models for ICU-LOS prediction using time-series data. This approach enables an update of the initial prediction resulting in the possibility of efficiently managing intensive care capacities.We present a simulation-based approach to evaluate ML algorithms and their impact on bed capacity management in real-world clinical settings.Our work provides in-depth insights into the impact of using ML techniques as decision support systems in the ICU and can lead to increased acceptance in practice.
Patient Decision Aids into Routine Orthopedic Care: Results from an Implementation Study at 12 Sites.
Sepucha K, Vo H, Marques F … +15 more , Valentine KD, Abdeen A, Bedair H, Chen AF, Eisler J, Freccero D, Jayakumar P, Kropfl E, Paul K, Ricciardi B, Vigil D, Wexler R, Williamson T, Yates A, Cha T
Med Decis Making · 2026 May · PMID 41439597 · Full text
BackgroundDecision aids (DAs) are evidence-based tools to improve patient-centered care, but their use in routine care is limited. The purpose of this project was to work with orthopedic practices to deliver DAs.MethodsE...
BackgroundDecision aids (DAs) are evidence-based tools to improve patient-centered care, but their use in routine care is limited. The purpose of this project was to work with orthopedic practices to deliver DAs.MethodsEligible sites needed to identify an administrative and clinical champion and have access to DAs for treatment of hip, knee, and/or spine conditions. The implementation strategies included an Orthopaedic Learning Collaborative (OLC), external facilitation, and audit and feedback. The project was conducted over 15 mo with 5 OLC sessions, individual monthly meetings, and monthly data reports. Clinicians and staff completed a baseline survey prior to the start of the project. Sites provided details on their DA workflow and number of DAs delivered. We calculated adoption (the number of specialists who used DAs) and estimated reach (percentage of eligible patients who received DAs). We calculated descriptive statistics and explored predictors of reach.ResultsTwelve participating sites had an average annual orthopedic surgical volume of 550, half were academic medical centers, and some (4/13, 30.7%) had prior experience with orthopedic DAs. Adoption was 76% (60/79 physicians). Sites distributed 9,626 DAs and reached 44% of eligible patients (range 7%-100%). Sites that indicated at baseline that DA delivery was a high priority for staff had higher reach (60% reach for high v. 47% for moderate v. 9% for low priority, = 0.21). Sites with no prior experience with DAs had higher reach than those with prior experience did (60% v. 38%, = 0.26, = 0.71).ConclusionsParticipating sites were able to implement workflows that reached about half of eligible patients. Establishing DA delivery as a priority for staff at the outset appears important for reach, while prior experience does not.HighlightsThe 12 sites were able to reach, on average, 44% of eligible patients with decision aids in routine care demonstrating feasibility of distribution.The study and associated implementation toolkit provide concrete examples of workflows for orthopedic practices interested in incorporating decision aids into routine care.A bundle of implementation strategies, including a learning collaborative, external facilitation, and audit and feedback, helped most sites meet targets for decision aid implementation.
Uncertainty around Health State Values Used in Cost-Effectiveness Analysis: How It Arises and How to Deal with It.
Parkin D, Briggs A, Abangma G … +2 more , Lloyd A, Devlin N
Med Decis Making · 2026 Feb · PMID 41408725 · Full text
Health state values, often in the form of value sets that list values applied to particular health states, are used in cost-effectiveness analyses of health care to calculate gains in quality-adjusted life-years. These v...
Health state values, often in the form of value sets that list values applied to particular health states, are used in cost-effectiveness analyses of health care to calculate gains in quality-adjusted life-years. These values are subject to several sources of uncertainty, arising from the fact that values are not constants but variables and are of different types including variability, heterogeneity, statistical uncertainty, and methodological variation. Currently, these sources are not fully documented and are not fully accounted for when creating and analyzing economic evaluation models. This may provide to users of such models a false sense of the precision of quality-adjusted life-year gain estimates and therefore of cost-effectiveness. This article provides a comprehensive account of such sources of uncertainty and how they interact. It also provides a more detailed account of how uncertainty arises in studies that elicit and model value sets. Its aim is to encourage research to measure and report uncertainty around health state values so it can be better accounted for in cost-effectiveness analyses.HighlightsHealth state values (HSVs) used in cost-effectiveness analysis are subject to multiple types of uncertainty, including variability, heterogeneity, statistical uncertainty, and methodological variation.Current reporting and guidelines often fail to fully document or address all sources of uncertainty in HSVs, which can mislead users about the precision of QALY and cost-effectiveness estimates.Valuation studies should report measures of uncertainty (such as standard errors or variance/covariance matrices) for HSVs, not just point estimates.Researchers, decision modellers, and guideline developers should recognise, measure, and report HSV uncertainty more thoroughly to improve the reliability of cost-effectiveness analyses.
Modeling Recurrent Events: A Tutorial Based on Relapse and Remitting Episodes during Medication-Assisted Treatment for Opioid Use Disorder.
Chrysanthopoulou SA, Wang J, Nolen S … +4 more , Madushani ARWM, Murphy SM, Linas BP, White LF
Med Decis Making · 2026 Apr · PMID 41392532 · Full text
In health or medical studies, participants can often experience the outcome(s) of interest multiple times during the observation period, creating recurrent event data. Depending on the primary research objective, advance...
In health or medical studies, participants can often experience the outcome(s) of interest multiple times during the observation period, creating recurrent event data. Depending on the primary research objective, advanced statistical methods are required to correctly analyze this special type of data. This tutorial discusses 4 general frameworks, appropriate for analyzing recurrent events data: 1) extended Cox, 2) parametric survival, 3) longitudinal, and 4) multistate models. We present in detail the implementation of these methods, including a description of the required dataset structure, R code, and interpretation of results, using data from the CTN-0051 study, a randomized clinical trial comparing the effectiveness of opioid use disorder treatments. The objectives of 3 use case scenarios exemplify the usage and relevance of the methods for the analysis of recurrent events: 1) estimate adjusted effects, 2) make individual-level predictions, and 3) model a complicated process involving multidirectional transitions between disease states. We compare the methods, comment on their strengths and limitations, and make recommendations on the preferred method depending on the primary research objective.HighlightsRecurrent events are a common phenomenon in experimental research settings, and their analysis requires advanced survival modeling approaches. This tutorial aims to explain and make these approaches more accessible with code and detailed instructions.We compare a detailed list of statistical methods for analyzing recurrent events and make suggestions on which one should be used depending on the study objective.This tutorial will enable researchers to make better use of recurrent events data.
Reconsidering Cancer Screening, Cancer-Specific Mortality, and Overdiagnosis: A Public Health and Ethical Perspective-Reply.
Henning C, Sroczynski G, Hallsson L … +3 more , Jahn B, Siebert U, Mühlberger N
Med Decis Making · 2026 Feb · PMID 41382982 · Publisher ↗
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Do Moral Views Change during a Crisis? An Experiment on Health Care Priority Setting.
Strand L, Gustavsson E, Tinghög G
Med Decis Making · 2026 May · PMID 41382963 · Full text
BackgroundResource scarcity during large-scale crises, such as pandemics, can increase the emphasis on efficiency in medical decision making. However, it remains unclear whether such shifts are primarily driven by the di...
BackgroundResource scarcity during large-scale crises, such as pandemics, can increase the emphasis on efficiency in medical decision making. However, it remains unclear whether such shifts are primarily driven by the direct experience of scarcity or by the way in which ethical principles for health care priority setting are expressed in the context of a crisis. This study investigates whether a national crisis affects public support for health care priority-setting principles and whether abstract versus concrete formulations of these principles shape that support.DesignWe conducted a preregistered online experiment ( = 1,404) to examine public attitudes toward three ethical principles formalized in the Swedish ethical platform-human dignity, needs-solidarity, and cost-effectiveness-in both crisis and noncrisis contexts. We also manipulated how the principles were presented, using either abstract or concrete formulations.ResultsIn the crisis condition, support for the human dignity and cost-effectiveness principles decreased, while support for the needs-solidarity principle increased. However, these effects were small, and the overall ranking of the principles remained stable. Notably, the level of abstractness had a stronger impact than the crisis context did: support for needs solidarity was higher when described abstractly, whereas support for cost-effectiveness increased when it was presented in a more concrete, action-oriented way. Support for the human dignity principle was unaffected by the abstractness manipulation.ConclusionThe findings suggest that people's moral views are relatively stable in the face of crisis. Rather than the crisis context itself, the way ethical principles are formulated-abstractly or concretely-may be a more powerful driver of shifts in public support for different moral values in health care priority setting.HighlightsPublic support for ethical principles remained largely stable during a simulated national crisis.The level of abstraction in how principles were presented strongly influenced support.Support for needs-solidarity increased in a crisis, while cost-effectiveness support declined.The way ethical principles were formulated had a greater impact than the presence of a crisis.
Corrigendum to "Calculating the Expected Net Benefit of Sampling for Survival Data: A Tutorial and Case Study".
Med Decis Making · 2026 Apr · PMID 41347604 · Publisher ↗
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Reconsidering Cancer Screening, Cancer-Specific Mortality, and Overdiagnosis: A Public Health and Ethical Perspective.
Takahashi T
Med Decis Making · 2026 Feb · PMID 41334614 · Publisher ↗
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When Do Published Cost-Effectiveness Analyses Include Societal Costs? An Empirical Analysis, 2013-2023.
Patil D, Liljas B, Neumann PJ … +1 more , Li M
Med Decis Making · 2026 Apr · PMID 41321207 · Publisher ↗
ObjectiveTo examine trends in the inclusion of societal costs in published cost-effectiveness analyses (CEAs), factors associated with their inclusion, and the impact of societal costs on incremental costs and incrementa...
ObjectiveTo examine trends in the inclusion of societal costs in published cost-effectiveness analyses (CEAs), factors associated with their inclusion, and the impact of societal costs on incremental costs and incremental cost-effectiveness ratios (ICERs).MethodsWe analyzed 7,800 CEAs from 2013 to 2023 using the Tufts Medical Center CEA registry. The inclusion of societal costs in CEAs was evaluated across study characteristics. Associations between study characteristics and the inclusion of societal costs were analyzed using multivariate logistic regression. For studies reporting health care and societal perspectives, we assessed the impact of including societal costs on incremental costs and ICERs.ResultsFrom 2013 to 2023, CEAs including societal costs increased from 19% to 28%. Productivity was the most frequently reported component (12%), followed by transportation (8%), caregiver time (6%), patient time (5%), and consumption costs (1%). Compared with US-based analyses, studies from Scandinavian countries (adjusted odds ratio [OR]: 3.6) and the Netherlands (5.6) had higher odds of including societal costs, whereas studies from Canada (0.7), Australia (0.6), and the United Kingdom (0.4) had lower odds. Studies on mental health disorders (6.2) and immunization (4.1) had the highest odds of including societal costs. Compared with CEAs focused on adults, CEAs targeting pediatric populations had higher odds (OR: 1.6), while those targeting the elderly had lower odds (OR: 0.7). Upon inclusion of societal costs, incremental costs decreased in 72% and increased in 28% of studies; the ICER decreased in 74% and increased in 26% of studies.ConclusionDespite the increase in recent years, societal costs are infrequently included in CEAs, with substantial variation by country, disease, and population. Including societal costs can meaningfully improve value assessments and should be guided by relevance, evidence, and decision context.HighlightsBuilding on prior work by Kim et al. (2020), which analyzed approximately 6,900 cost-effectiveness analyses (CEAs), this study examined a larger and more recent sample of 7,800 CEAs from 2013 to 2023. In addition to updating the evidence base, we conducted new analyses to assess trends, associated factors, and the effect of including societal costs on incremental cost-effectiveness ratios (ICERs), thus providing insights that were not explored in prior work and addressing a key evidence gap in health economics.The inclusion of societal costs in CEAs rose modestly from 19% to 28% from 2013 to 2023, with substantial variation across countries, diseases, and intervention types. In some cases, the inclusion of societal costs affected incremental costs and ICERs enough to cross commonly used cost-effectiveness thresholds.The inclusion of societal costs can help improve value assessments in health care interventions, but it should be guided by relevance, available evidence, and the potential to influence decision making. Identifying when and where societal costs meaningfully affect outcomes can support more consistent and appropriate use.
Estimating Productivity Losses per HIV Infection due to Premature HIV Mortality in the United States.
Islam MH, Chesson HW, Song R … +4 more , Hutchinson AB, Shrestha RK, Viguerie A, Farnham PG
Med Decis Making · 2026 Feb · PMID 41288194 · Full text
BackgroundUpdated estimates of the productivity losses per HIV infection due to premature HIV mortality are needed to help quantify the economic burden of HIV and inform cost-effectiveness analyses.MethodsWe used the hum...
BackgroundUpdated estimates of the productivity losses per HIV infection due to premature HIV mortality are needed to help quantify the economic burden of HIV and inform cost-effectiveness analyses.MethodsWe used the human capital approach to estimate the productivity loss due to HIV mortality per HIV infection in the United States, discounted to the time of HIV infection. We incorporated published data on age-specific annual productivity, life expectancy at HIV diagnosis, life-years lost from premature death among persons with HIV (PWH), the number of years from HIV infection to diagnosis, and the percentage of deaths in PWH attributable to HIV. For the base case, we used 2018 life expectancy data for all PWH in the United States. We also examined scenarios using life expectancy in 2010 and life expectancy for cohorts on antiretroviral therapy (ART). We conducted sensitivity analyses to understand the impact of key input parameters.ResultsWe estimated the base-case overall average productivity loss due to HIV mortality per HIV infection at $65,300 in 2022 US dollars. The base-case results showed a 45% decrease in the estimated productivity loss compared with the results when applying life expectancy data from 2010. Productivity loss was 83% lower for cohorts of PWH on ART compared with the base-case scenario. Results were sensitive to assumptions about percentage of deaths attributable to HIV and heterogeneity in age at death.ConclusionThis study provides valuable insights into the economic impact of HIV mortality, illustrating reductions in productivity losses over time due to advancements in treatments.HighlightsUpdated estimates of productivity losses per HIV infection due to premature HIV mortality can help assess the total economic burden of HIV in the United States.This study estimates productivity losses per HIV infection for overall, by sex, and by varying ages of HIV infection.Advancement in treatment has contributed to a significant reduction in productivity losses due to premature HIV mortality in the United States over the past decade.
Use of Expected Utility to Evaluate Artificial Intelligence-Enabled Rule-out Devices for Mammography Screening.
Fan KL, Thompson YLE, Chen W … +2 more , Abbey CK, Samuelson FW
Med Decis Making · 2026 Apr · PMID 41277386 · Publisher ↗
BackgroundAn artificial intelligence (AI)-enabled rule-out device may autonomously remove patient images unlikely to have cancer from radiologist review. Many published studies evaluate this type of device by retrospecti...
BackgroundAn artificial intelligence (AI)-enabled rule-out device may autonomously remove patient images unlikely to have cancer from radiologist review. Many published studies evaluate this type of device by retrospectively applying the AI to large datasets and use sensitivity and specificity as the performance metrics. However, these metrics have fundamental shortcomings because sensitivity will always be negatively affected in retrospective studies of rule-out applications of AI.MethodWe reviewed 2 performance metrics to compare the screening performance between the radiologist-with-rule-out-device and radiologist-without-device workflows: positive/negative predictive values (PPV/NPV) and expected utility (EU). We applied both methods to a recent study that reported improved performance in the radiologist-with-device workflow using a retrospective US dataset. We then applied the EU method to a European study based on the reported recall and cancer detection rates at different AI thresholds to compare the potential utility among different thresholds.ResultsFor the US study, neither PPV/NPV nor EU can demonstrate significant improvement for any of the algorithm thresholds reported. For the study using European data, we found that EU is lower as AI rules out more patients including false-negative cases and reduces the overall screening performance.ConclusionsDue to the nature of the retrospective simulated study design, sensitivity and specificity can be ambiguous in evaluating a rule-out device. We showed that using PPV/NPV or EU can resolve the ambiguity. The EU method can be applied with only recall rates and cancer detection rates, which is convenient as ground truth is often unavailable for nonrecalled patients in screening mammography.HighlightsSensitivity and specificity can be ambiguous metrics for evaluating a rule-out device in a retrospective setting. PPV and NPV can resolve the ambiguity but require the ground truth for all patients. Based on utility theory, expected utility (EU) is a potential metric that helps demonstrate improvement in screening performance due to a rule-out device using large retrospective datasets.We applied EU to a recent study that used a large retrospective mammography screening dataset from the United States. That study reported an improvement in specificity and decrease in sensitivity when using their AI as a rule-out device retrospectively. In terms of EU, we cannot conclude a significant improvement when the AI is used as a rule-out device.We applied the method to a European study that reported only recall rates and cancer detection rates. Since there is no established EU baseline value in European mammography screening workflow, we estimated the EU baseline using data from previous literature. We cannot conclude a significant improvement when the AI is used as a rule-out device for the European study.In this work, we investigated the use of EU to evaluate rule-out devices using large retrospective datasets. This metric, used with retrospective clinical data, could be used as supporting evidence for rule-out devices.
A New Integrative Modeling Approach for Generating Counterfactual Projections of Colorectal Cancer Incidence Rates in the Absence of Organized Screening in Australia.
Luo Q, Lew JB, Worthington J … +9 more , Kahn C, Ge H, He E, Caruana M, David M, O'Connell DL, Canfell K, Steinberg J, Feletto E
Med Decis Making · 2026 Feb · PMID 41263263 · Full text
BackgroundThe Australian National Bowel Cancer Screening Program (NBCSP), which provides 2-yearly screening to people aged 50 to 74 y, had a phased rollout from 2006 and was fully implemented in 2020. To measure the effe...
BackgroundThe Australian National Bowel Cancer Screening Program (NBCSP), which provides 2-yearly screening to people aged 50 to 74 y, had a phased rollout from 2006 and was fully implemented in 2020. To measure the effectiveness of the NBCSP accounting for age-specific trends, we aimed to develop a novel integrative method to project colorectal cancer (CRC) incidence rates from 2006 to 2045 in the absence of the NBCSP (referred to as "no-NBCSP projections") while addressing the challenge of complex age-specific trends in CRC incidence.MethodsWe constructed a new dataset by replacing the observed data for NBCSP-eligible individuals aged 50 to 74 y with intermediate projections based on pre-NBCSP data from 1982 to 2005. We compared the no-NBCSP CRC incidence projected using a standard age-period-cohort (APC) model, age-stratified APC models, and the integrative modeling approach.ResultsThe integrative modeling approach captured complex age-specific trends better than the standard and age-stratified APC models did. Without the NBCSP, the overall CRC incidence rates would be expected to decline from 2005 to 2025, followed by increases from 2026 to 2045. The incidence rates for those aged <50 y would be projected to continue increasing to 2045, and an increase in incidence rates for older age groups would be projected to occur from 2020 for ages 50 to 54 y, from 2030 for ages 65 to 74 y, and from 2035 for ages 75 y and older.ConclusionsThese no-NBCSP projections provide a counterfactual benchmark against which to measure the impact of the NBCSP on CRC incidence in Australia, and they have been used as new calibration targets for a simulation model of CRC and screening in Australia. The methods developed here could be used to generate comparators to assess the impact of other public health interventions.HighlightsWe constructed counterfactual projections of colorectal cancer (CRC) incidence rates in the absence of the National Bowel Cancer Screening Program (no-NBCSP projections).To do this, we developed a new integrative modeling approach to capture complex age-specific colorectal cancer incidence trends.These no-NBCSP projections provide a counterfactual benchmark against which to measure the impact of the NBCSP on CRC incidence in Australia.These projections stress the need for ongoing assessment of the starting age for the NBCSP, to tackle the increasing incidence for people younger than 50 y.
Vaccination Strategies against HPV Infection and Cervical Cancer in China: A Transmission Modeling Study.
Shi Y, Sun N, Ren J … +4 more , Sun J, Xiong J, Zhu H, Zhu G
Med Decis Making · 2026 Apr · PMID 41237272 · Publisher ↗
BackgroundCervical cancer, driven predominantly by persistent high-risk human papillomavirus (HPV) infection, ranks as the fourth most common malignancy in women worldwide. China faces barriers to achieving the World Hea...
BackgroundCervical cancer, driven predominantly by persistent high-risk human papillomavirus (HPV) infection, ranks as the fourth most common malignancy in women worldwide. China faces barriers to achieving the World Health Organization (WHO) 2030 elimination targets due to low vaccination rates and complex demographics. Strategic intervention optimization is critical for accelerating elimination.MethodsWe developed an age-stratified deterministic compartmental model integrating demographic data and HPV transmission dynamics, capturing heterogeneity in age, sex, sexual activity, and intervention efficacy. The model simulated cervical cancer natural history, including HPV infection, progression to precancerous lesions, and invasive cancer and was calibrated using epidemiological data from the Global Burden of Disease. We evaluated multiple vaccination scenarios (varying coverage rates, age groups, and durations) to project incidence trajectories, estimate elimination timelines, and calculate the reproduction number. Sensitivity analyses were conducted to assess parameter effects.ResultsWithout vaccination, HPV infection becomes endemic (R = 1.38), causing 2.92 million cervical cancer cases in China during 2021 to 2070. Maintaining the 2020 vaccination rate would prevent 1.01 million cases in this period. While prioritizing females aged 15 to 26 y maximizes the per-dose impact, expanding vaccination to all females aged ≥15 y is essential for achieving elimination before 2040. Even single-year vaccination would confer >50-y protection. A higher vaccination rate accelerates elimination: annual rates of 0.09, 0.15, and 0.21 among females aged ≥15 y achieve elimination by 2037, 2035, and 2034, respectively, accelerating timelines by 15 to 20 y compared with strategies targeting only 15- to 26-y-olds.ConclusionsHPV vaccination is pivotal for reducing cervical cancer burden in China, with prioritizing women aged 15 to 26 y as the optimal strategy. Expanding vaccination to all women aged ≥15 y can accelerate the achievement of WHO elimination targets.HighlightsAn age-stratified model simulates HPV transmission patterns and assesses cervical cancer interventions.Without intervention, HPV remains endemic (R = 1.38), causing 2.92 million cervical cancer cases in China (2021-2070).Prioritizing 15- to 26-y-olds maximizes the per-dose impact, but expanding to 15+ y cohorts is essential for elimination.Even a single year of vaccination offers >50 y of protection.Females ≥15 y vaccinated annually at rates of 0.09, 0.15, and 0.21 achieve elimination by 2037, 2035, and 2034, respectively.
Population Health and Health Inequality Impacts of the National Abdominal Aortic Aneurysm Screening Programme (NAAASP) in England.
Premji S, Walker SM, Koh J … +3 more , Glover M, Sweeting MJ, Griffin S
Med Decis Making · 2026 Feb · PMID 41230943 · Full text
PurposeWe conducted a distributional cost-effectiveness analysis (DCEA) using routinely collected data to estimate the population health and health inequality impacts of the National Abdominal Aortic Aneurysm Screening P...
PurposeWe conducted a distributional cost-effectiveness analysis (DCEA) using routinely collected data to estimate the population health and health inequality impacts of the National Abdominal Aortic Aneurysm Screening Programme (NAAASP) in England.MethodsAn existing discrete event simulation model of AAA screening was adapted to examine differences between socioeconomic groups defined by Index of Multiple Deprivation, obtained from an analysis of secondary data sources. We examined the distributional cost-effectiveness of being invited versus not invited at age 65 y to screen using a National Health Service perspective. Changes in inequality were valued using a measure of equally distributed equivalent health.ResultsThe net health benefits of population screening (317 quality-adjusted life-years [QALYs] gained) were disproportionately accounted for by the effects on those living in more advantaged areas. The NAAASP improved health on average compared with no screening, but the health opportunity cost of the programme exceeded the QALY gains for people living in the most deprived areas, resulting in a negative net health impact for this group (106 QALYs lost) that was driven by differences in the need for screening. Consequently, the NAAASP increased health inequality at the population level. Given current estimates for inequality aversion in England, screening for AAA remains the optimal strategy.ConclusionExamination of the distributional cost-effectiveness of the NAAASP in England using routinely collected data revealed a tradeoff between total population health and health inequality. Study findings suggest that the NAAASP provides value for money despite health impacts being disseminated to those who are more advantaged.HighlightsThis study examines the population health and health inequality effects of the National Abdominal Aortic Aneurysm Screening Programme (NAAASP) between socioeconomic groups defined by Index of Multiple Deprivation.Findings suggest a tradeoff between total population health and health inequality.Given current estimates for inequality aversion in England, screening remains the optimal strategy relative to not screening.Opportunities remain to reduce inequality effects for those most vulnerable through targeted approaches.
Eliciting Unreported Subgroup-Specific Survival from Aggregate Randomized Controlled Trial Data.
Alagoz O, Singh P, Dixon M … +1 more , Kurt M
Med Decis Making · 2026 Feb · PMID 41230837 · Publisher ↗
IntroductionSubgroup analyses are vital components of health technology assessments, but randomized controlled trials (RCTs) do not commonly report survival distributions for subgroups. This study developed an analytical...
IntroductionSubgroup analyses are vital components of health technology assessments, but randomized controlled trials (RCTs) do not commonly report survival distributions for subgroups. This study developed an analytical framework to elicit unreported subgroup-specific survival curves from aggregate RCT data.MethodsAssuming exponentially distributed subgroup survival durations, we developed an optimization model that approximates the restricted mean survival time (RMST) for the overall population via the weighted average of the RMSTs of 2 subgroups in each arm. Reported hazard ratios from the forest plots between the arms were used to enforce the relationship among subgroups' hazard rates in the model. The performance of the model was tested in a real-life test set of 8 RCTs in advanced-stage gastrointestinal tumors, which also reported KM curves for overall survival (OS) for 40 subgroups as well as in 42 synthetic test cases with 168 subgroups as a benchmark. For each subgroup, predicted median survival, OS rates, and the RMSTs were compared against their actual counterparts as well as their 95% confidence intervals (CIs).ResultsPredicted median survivals and RMSTs were within the 95% CIs of the reported values in 32 (80%) and 34 (85%) of 40 subgroups in real-life test cases and in 163 (97%) and 146 (87%) of 168 subgroups in synthetic test cases, respectively. Across all cases, on average, the predicted survival curves laid within the 95% CIs of reported KM curves 71% and 97% of the time in real-life and synthetic test cases, respectively.DiscussionOur study offers a useful and scalable method for extracting subgroup-specific survival from aggregate RCT data to enable subgroup-specific indirect comparisons, and cost-utility and meta-analyses.HiglightsMost randomized controlled trials report survival curves for the overall patient population but do not provide subgroup-specific survival curves, which are crucial for cost-effectiveness analyses and meta-analyses focusing on these subgroups.This study developed an optimization modeling approach to elicit unreported subgroup-specific survival curves from aggregate trial data.The proposed modeling approach accurately predicted the reported subgroup-specific survival curves in 42 simulated test cases with 168 subgroups overall, in which each subgroup-specific survival curve was assumed to followed an exponential distribution.The performance of the proposed modeling approach was sensitive to the assumptions when it was tested using a real-life test set of 8 oncology trials, which also reported survival curves for a total of 40 subgroups.
A Bayesian Model Leveraging Multiple External Data Sources to Improve the Reliability of Lifetime Survival Extrapolations in Metastatic Non-Small-Cell Lung Cancer.
Sharpe DJ, Yates G, Chaudhary MA … +2 more , Yuan Y, Lee A
Med Decis Making · 2026 Feb · PMID 41221583 · Publisher ↗
ObjectivesBayesian multiparameter evidence synthesis (B-MPES) can improve the reliability of long-term survival extrapolations by leveraging registry data. We extended the B-MPES framework to also incorporate historical...
ObjectivesBayesian multiparameter evidence synthesis (B-MPES) can improve the reliability of long-term survival extrapolations by leveraging registry data. We extended the B-MPES framework to also incorporate historical trial data and examined the impact of alternative external information sources on predictions from early data cuts for a trial in metastatic non-small-cell lung cancer (mNSCLC).MethodsB-MPES models were fitted to survival data from the phase III CheckMate 9LA study of nivolumab plus ipilimumab plus 2 cycles of chemotherapy (NIVO+IPI+CHEMO, v. 4 cycles of CHEMO) in first-line mNSCLC, with 1 y of minimum follow-up. Trial observations were supplemented by registry data from the Surveillance, Epidemiology, and End Results program, general population data, and, optionally, historical trial data with extended follow-up for first-line NIVO+IPI (v. CHEMO) and/or second-line NIVO monotherapy in advanced NSCLC, via estimated 1-y conditional survival. Predictions from the 3 alternative B-MPES models were compared with those from standard parametric models (SPMs).ResultsB-MPES models better anticipated the emergent survival plateau with NIVO+IPI+CHEMO that was apparent in the 4-y data cut compared with SPMs, for which short-term extrapolations in both treatment arms were overly conservative. However, the B-MPES model incorporating NIVO+IPI data slightly overestimated 4-y NIVO+IPI+CHEMO survival owing to a confounding effect on estimated hazards that could not be accounted for a priori until later data cuts of CheckMate 9LA. Extrapolations were relatively robust to the choice of external data sources provided that the prior data had been adjusted to attenuate confounding.ConclusionsIncorporating historical trial data into survival models can improve the plausibility and interpretability of lifetime extrapolations for studies of novel therapies in metastatic cancers when data are immature, and B-MPES provides an appealing method for this purpose.HighlightsLeveraging historical trial data with extended follow-up to extrapolate survival from early study data cuts in a Bayesian evidence synthesis framework can realize anticipated longer-term effects that are characteristic of a novel therapy or class thereof.Using moderately confounded external data sources can improve the reliability of survival extrapolations from B-MPES models provided that the prior information is adjusted and rescaled appropriately, but it is essential to rationalize the implicit assumptions surrounding longer-term treatment effects in the current study.B-MPES models are an attractive option to conduct informed lifetime survival extrapolations based on transparent clinical assumptions via leveraging multiple external data sources, but model flexibility and a priori confidence in external data must be specified carefully to avoid overfitting.
Do Worse than Dead Values Add Relevant Information in (Composite) Time-Tradeoff Valuations?
Stalmeier PFM, Roudijk B
Med Decis Making · 2026 Feb · PMID 41214847 · Publisher ↗
I30, J17.
I30, J17.
Network Meta-Analysis with Class Effects: A Practical Guide and Model Selection Algorithm.
Perren SJ, Pedder H, Welton NJ … +1 more , Phillippo DM
Med Decis Making · 2026 Apr · PMID 41204833 · Full text
Network meta-analysis (NMA) synthesizes data from randomized controlled trials to estimate the relative treatment effects among multiple interventions. When treatments can be grouped into classes, class effect NMA models...
Network meta-analysis (NMA) synthesizes data from randomized controlled trials to estimate the relative treatment effects among multiple interventions. When treatments can be grouped into classes, class effect NMA models can be used to inform recommendations at the class level and can also address challenges with sparse data and disconnected networks. Despite the potential of NMA class effects models and numerous applications in various disease areas, the literature lacks a comprehensive guide outlining the range of class effect models, their assumptions, practical considerations for estimation, model selection, checking assumptions, and presentation of results. In addition, there is no implementation available in standard software for NMA. This article aims to provide a modeling framework for class effect NMA models, propose a systematic approach to model selection, and provide practical guidance on implementing class effect NMA models using the multinma R package. We describe hierarchical NMA models that include random and fixed treatment-level effects and exchangeable and common class-level effects. We detail methods for testing assumptions of heterogeneity, consistency, and class effects, alongside assessing model fit to identify the most suitable models. A model selection strategy is proposed to guide users through these processes and assess the assumptions made by the different models. We illustrate the framework and structured approach for model selection using an NMA of 41 interventions from 17 classes for social anxiety.HighlightsProvides a practical guide and modelling framework for network meta-analysis (NMA) with class effects.Proposes a model selection strategy to guide researchers in choosing appropriate class effect models.Illustrates the strategy using a large case study of 41 interventions for social anxiety.
Do Patient Preferences and Treatment Beliefs Explain Patterns of Antihypertensive Medication Nonadherence? A Discrete Choice Experiment.
Tan SNG, Muiruri C, Gonzalez Sepulveda JM
Med Decis Making · 2026 Jan · PMID 41204832 · Full text
BackgroundMedication adherence is a critical factor in hypertension management, which remains a challenge for public health systems.MethodsGraded-pair questions were used to quantify the perception of how much nonadheren...
BackgroundMedication adherence is a critical factor in hypertension management, which remains a challenge for public health systems.MethodsGraded-pair questions were used to quantify the perception of how much nonadherence to antihypertensives increases the risk of serious cardiovascular events. A discrete-choice experiment was used to quantify the relative importance of medication outcomes (e.g., reduction in cardiovascular event risk and medication side effects). Rating questions were used to assess perspectives of the effect of treatment nonadherence on treatment side effects. Results were combined to assess how preferences and outcome expectations influence adherence.ResultsPatients perceived treatment adherence as the most significant contributor to cardiovascular event risk. A reduction in cardiovascular risk was the most significant consideration when choosing medication. Missing consecutive (v. alternate) doses was associated with greater perceived cardiovascular risk and fewer side effects. The differences between complete adherence and any level of nonadherence were significantly larger for side effects than for changes in the risk of cardiovascular events, suggesting that side effects are perceived to be more sensitive to nonadherence than treatment efficacy.LimitationsOur study relied on hypothetical scenarios, which may not fully capture real-world decision making. While our findings shed light on the relationship between adherence patterns and treatment perceptions, it is essential to recognize the complexity of adherence behavior.ConclusionsPatients believe that they can manage medication side effects by skipping doses without compromising the efficacy to the same degree and that they can offset compromises in efficacy by avoiding missing consecutive doses for prolonged periods.ImplicationsHealth care providers should understand the importance of patient education and counseling to address misconceptions and promote realistic expectations regarding treatment efficacy and the consequences of nonadherence.HighlightsThe average patient believes that they can manage medication side effects by skipping doses without compromising the efficacy to the same degree.There is a belief that patients can offset some of the impact of nonadherence on their cardiovascular event risk, particularly if they avoid missing consecutive doses for prolonged periods of time.This highlights the importance of patient education and counseling to address misconceptions and promote realistic expectations regarding treatment efficacy and the consequences of nonadherence.
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