Medical Decision Making[JOURNAL]
Sun
200 papers
Optimizing Face Validity and Clinical Relevance of a Mathematical Population Cancer Epidemiology Model Using a Novel Advisory Group Approach.
Davies L, Fernandes-Taylor S, Arroyo N … +3 more , Zhang Y, Alagoz O, Francis DO
Med Decis Making · 2025 May · PMID 40165387 · Full text
BackgroundCancer simulation models can answer research and policy questions when prospective evidence is incomplete or not feasible. However, such models require incorporating unmeasureable inputs for which there is ofte...
BackgroundCancer simulation models can answer research and policy questions when prospective evidence is incomplete or not feasible. However, such models require incorporating unmeasureable inputs for which there is often not strong evidence, and model utility is limited if assumptions lack face validity or if the model is not clinically relevant. We systematically incorporated formal advisory input to mitigate these challenges as we developed a microsimulation model of papillary thyroid cancer (PApillary Thyroid CArcinoma Microsimulation model [PATCAM]).MethodsWe used a participatory action research approach incorporating focus group techniques and using principles of bidirectional learning.ResultsWe assembled a formal standing advisory group with representation by perspective (medical, patient, and payor), geography, and local practice culture to understand current and historical clinical beliefs and practices about thyroid cancer diagnosis and treatment. The group provided input on critical modeling assumptions and decisions: 1) the role of nodule size in biopsy decisions, 2) trends in provider biopsy behavior, 3) specialty propensity to biopsy, 4) population prevalence of thyroid cancer over time, 5) proportion of malignant tumors showing regression, and 6) cancer epidemiology and diagnostic practices by sex and age. Advisory group questions and concerns about model development will inform future research questions and strategies to communicate and disseminate model results.ConclusionsWe successfully used our advisory group to provide critical inputs on unmeasurable assumptions, increasing the face validity of our model. The use of a standing advisory group improved model transparency and contributed to future research plans and dissemination of model results so they can have maximum impact when guiding clinical decisions and policy.HighlightsUnfamiliarity with simulation modeling poses a threat to its acceptability and adoption. The effectiveness of these models is contingent on end-users' willingness to accept and adopt model results. The effectiveness of the models is further limited if they lack face validity to potential users or do not have clinical relevance.Several approaches to overcoming validity challenges have been advanced, such as collaborative modeling, which involves developing multiple models independently using common data sources. However, when only a single model exists, another approach is needed. We used an Advisory Group and "participatory modeling," which has been used in other settings but has not been previously reported in cancer modeling. We describe the methods used for and results of incorporating a formal advisory group into the development of a cancer microsimulation model.The use of a formal, standing advisory group (as opposed to one-off focus groups or interviews) strengthened our model by rigorously vetting modeling assumptions and model inputs with subject matter experts. The formal, ongoing structure promoted transparency. The group education in cancer modeling improved participant ability to provide useful input and may help with dissemination. The advisory group also provided critical feedback about how to effectively communicate model results and informed planned future research questions.
The Effect of a Surgeon Communication Strategy on Treatment Preference for Thyroid Cancer: A Randomized Trial.
Jensen CB, Sinco B, Saucke MC … +4 more , Bushaw KJ, Antunez AG, Voils CI, Pitt SC
Med Decis Making · 2025 May · PMID 40145501 · Full text
BackgroundCancer diagnosis causes emotional distress, which can influence patients' treatment choice. This study aimed to investigate the effect of increased emotionally supportive surgeon communication in a virtual sett...
BackgroundCancer diagnosis causes emotional distress, which can influence patients' treatment choice. This study aimed to investigate the effect of increased emotionally supportive surgeon communication in a virtual setting on treatment preference for thyroid cancer.DesignThis randomized trial (NCT05132478), conducted from November 2021 to February 2023, enrolled adults with ≤4-cm thyroid nodules not requiring surgery. Participants were randomized 1:1 to watch a virtual clinic visit depicting a patient-surgeon treatment discussion for low-risk thyroid cancer. Control and intervention videos were identical except for added emotionally supportive communication in the intervention. The primary outcome was treatment preference for total thyroidectomy or lobectomy. Secondary outcomes were perceived physician empathy, physician trust, decisional confidence, and disease-specific knowledge. An intention-to-treat analysis was performed using conditional regression to account for stratification by sex. Qualitative content analysis evaluated participants' open-ended responses about treatment choice and surgeon communication.ResultsOf 208 eligible patients, 118 (56.7%) participated. Participants were 85.6% female and 88.1% White. Overall, 89.0% ( = 105) of participants preferred lobectomy, which was similar between the intervention and control groups (90.0% v. 87.9%, respectively, = 0.72). Compared with control, participants who viewed the consultation with enhanced communication perceived higher levels of physician empathy (34.5 ± 5.8 v. 25.9 ± 9.1, < 0.001) and reported increased trust in the physician (12.0 ± 2.6 v. 10.4 ± 3.1, < 0.001). The groups were similar in decisional confidence (7.6 ± 2.1 v. 7.7 ± 1.9, = 0.74) and disease-specific knowledge. Prominent qualitative themes among participants choosing thyroid lobectomy included desire to avoid daily thyroid hormone ( = 53) and concerns about surgical complications ( = 25).ConclusionsIn this randomized controlled study, a significant proportion of participants preferred thyroid lobectomy if diagnosed with low-risk thyroid cancer. Participants perceived increased empathy when provided even in the virtual setting, which was associated with increased trust in the physician.HighlightsIn this single-site, randomized controlled trial, enhanced emotionally supportive surgeon communication had no effect on hypothetical treatment preference for low-risk thyroid cancer.Participants who experienced enhanced emotionally supportive surgeon communication perceived higher physician empathy and reported greater trust in the physician.The incorporation of empathetic communication during surgical consultation for low-risk thyroid cancer promotes patient trust and perception of empathy.
Health State Utility Values: The Implications of Patient versus Community Ratings in Assessing the Value of Care.
Gidwani R, Saylor KW, Russell LB
Med Decis Making · 2025 May · PMID 40119777 · Full text
BackgroundHealth-state utility values (HSUVs) are key inputs into cost-utility analyses. There is debate over whether they are best derived from the community or patients, with concerns raised that community-derived pref...
BackgroundHealth-state utility values (HSUVs) are key inputs into cost-utility analyses. There is debate over whether they are best derived from the community or patients, with concerns raised that community-derived preferences may devalue benefits to ill, elderly, or disabled individuals. This tutorial compares the effects of using patient-derived HSUVs versus community-derived HSUVs on incremental cost-effectiveness ratios (ICERs) and shows their implications for policy.DesignWe review published studies that compared HSUVs derived from patients and the community. We then present equations for the gains in quality-adjusted life-years (QALYs) that would be estimated for an intervention using patient versus community HSUVs and discuss the implications of those QALY gains. We present a numerical example as another way of showing how ICERs change when using patient versus community HSUVs.ResultsPatient HSUVs are generally higher than community HSUVs for severe health states. When an intervention reduces , patient ratings yield more favorable ICERs than do community ratings. However, when the intervention reduces , patient ratings yield less favorable ICERs. For interventions that reduce both morbidity and mortality, the effect on ICERs of patient versus community HSUVs depends on the relative contribution of each to the resulting QALYs.ConclusionsThe use of patient HSUVs does not consistently favor treatments directed at those patients. Rather, the effect depends on whether the intervention reduces mortality, morbidity, or both. Since most interventions do both, using patient HSUVs has mixed implications for promoting investments for people with illness and disabilities. A nuanced discussion of these issues is necessary to ensure that policy matches the intent of the decision makers.HighlightsThe debate about whether health state utility values (HSUVs) are best derived from patients or the community rests in part on the presumption that using community values devalues interventions for disabled persons or those with chronic diseases.However, we show why the effect of using patient HSUVs depends on whether the intervention in question primarily reduces mortality or morbidity or has substantial effects on both.If the intervention reduces mortality, using patient HSUVs will make the intervention appear more cost-effective than using community HSUVs, but if it reduces morbidity, using patient HSUVs will make the intervention appear less cost-effective.If the intervention reduces both morbidity and mortality, a common situation, the effect of patient versus community HSUVs depends on the relative magnitudes of the gains in quality and length of life.
People Living with Chronic Pain Experience a High Prevalence of Decision Regret in Canada: A Pan-Canadian Online Survey.
Naye F, Tousignant-Laflamme Y, Sasseville M … +13 more , Cachinho C, Gérard T, Toupin-April K, Dubois O, Paquette JS, LeBlanc A, Gaboury I, Poitras MÈ, Li LC, Hoens AM, Poirier MD, Légaré F, Décary S
Med Decis Making · 2025 May · PMID 40119768 · Full text
Background(1) To estimate the prevalence of decision regret in chronic pain care, and (2) to identify factors associated with decision regret.DesignWe conducted a pan-Canadian cross-sectional online survey and reported t...
Background(1) To estimate the prevalence of decision regret in chronic pain care, and (2) to identify factors associated with decision regret.DesignWe conducted a pan-Canadian cross-sectional online survey and reported the results following the Checklist for Reporting of Survey Studies guidelines. We recruited a sample of adults experiencing chronic noncancer pain. We used a stratified proportional random sampling based on the population and chronic pain prevalence of each province. We measured decision regret with the Decision Regret Scale (DRS) and decisional needs with the Ottawa Decision Support Framework. We performed descriptive analysis to estimate the prevalence and level of decision regret and multilevel multivariable regression analysis to identify factors associated with regret according to the STRengthening Analytical Thinking for Observational Studies recommendations.ResultsWe surveyed 1,649 people living with chronic pain, and 1,373 reported a most difficult decision from the 10 prespecified ones, enabling the collection of a DRS score. On a scale ranging from 0 to 100 where 1 reflects the presence of decision regret and 25 constitutes important decision regret, the mean DRS score in our sample was 28.8 ( = 19.6). Eighty-four percent of respondents experienced some decision regret and 50% at an important level. We identified 15 factors associated with decision regret, including 4 personal and 9 decision-making characteristics, and 2 consequences of the chosen option. Respondents with low education level and higher decisional conflict experienced more decision regret when the decision was deemed difficult.ConclusionsThis pan-Canadian survey highlighted a high prevalence and level of decision regret associated with difficult decisions for pain care. Decision making in pain care could be enhanced by addressing factors that contribute to decision regret.HighlightsWe conducted an online pan-Canadian survey and collected responses from a wide diversity of people living with chronic pain.More than 84% of respondents experienced decision regret and approximately 50% at an important level.We identified 15 factors associated with decision regret, including 4 personal and 9 decision-making characteristics, and 2 consequences of the chosen option.Our pan-Canadian survey reveals an urgent need of a shared decision-making approach in chronic pain care that can be potentiated by targeting multiple factors associated with decision regret.
Immediate Death: Not So Bad If You Discount the Future but Still Worse than It Should Be.
Pullenayegum EM, Jonker MF, Bailey H … +1 more , Roudijk B
Med Decis Making · 2025 May · PMID 40110719 · Full text
ObjectivesDiscrete choice experiments (DCEs) as a valuation method require preferences to be anchored on the quality-adjusted life-year scale, usually through tasks involving choices between immediate death and various i...
ObjectivesDiscrete choice experiments (DCEs) as a valuation method require preferences to be anchored on the quality-adjusted life-year scale, usually through tasks involving choices between immediate death and various impaired health states or between health states with varying durations of life. We sought to determine which anchoring approach aligns best with the composite time tradeoff (cTTO) method, with a view to informing a valuation protocol that uses DCEs in place of the cTTO.MethodsA total of 970 respondents from Trinidad and Tobago completed a DCE with duration survey. Tasks involved choosing between 2 lives with identical durations, followed by a third option, representing either full health for a number of years or immediate death. Data were analyzed using mixed logit models, both with and without exponential discounting for time preferences.ResultsAssuming linear time preferences, the estimated utility of immediate death was -2.1 (95% credible interval [CrI] -3.2 to -1.2) versus -0.28 (95% CrI -0.47, -0.10) when allowing for nonlinear time preferences. Under linear time preferences, the predicted health-state values anchored on duration had range (-1.03, 1) versus (0.34, 1) when anchored on immediate death. The ranges under nonlinear time preferences were (-0.54, 1) versus (-0.22, 1). The estimated discount parameter was 23% (95% CrI 22% to 25%).ConclusionsThe nonzero discount parameter indicates that time preferences were nonlinear. Nonlinear time preferences anchored on duration provided the closest match to the benchmark EQ-VT cTTO values in Trinidad and Tobago, whose range was (-0.6, 1). Thus, DCE with duration can provide similar values to cTTO provided that nonlinear time preferences are accounted for and anchoring is based on duration.HighlightsTime preferences for health states in Trinidad and Tobago were nonlinear.In discrete choice tasks, we show that immediate death has a utility less than zero.DCE utilities under nonlinear time preferences with anchoring on duration agreed well with cTTO utilities.
A Nonparametric Approach for Estimating the Effective Sample Size in Gaussian Approximation of Expected Value of Sample Information.
Li L, Jalal H, Heath A
Med Decis Making · 2025 May · PMID 40110682 · Full text
The effective sample size (ESS) measures the informational value of a probability distribution in terms of an equivalent number of study participants. The ESS plays a crucial role in estimating the expected value of samp...
The effective sample size (ESS) measures the informational value of a probability distribution in terms of an equivalent number of study participants. The ESS plays a crucial role in estimating the expected value of sample information (EVSI) through the Gaussian approximation approach. Despite the significance of ESS, except for a limited number of scenarios, existing ESS estimation methods within the Gaussian approximation framework are either computationally expensive or potentially inaccurate. To address these limitations, we propose a novel approach that estimates the ESS using the summary statistics of generated datasets and nonparametric regression methods. The simulation experiments suggest that the proposed method provides accurate ESS estimates at a low computational cost, making it an efficient and practical way to quantify the information contained in the probability distribution of a parameter. Overall, determining the ESS can help analysts understand the uncertainty levels in complex prior distributions in the probability analyses of decision models and perform efficient EVSI calculations.HighlightsEffective sample size (ESS) quantifies the informational value of probability distributions, essential for calculating the expected value of sample information (EVSI) using the Gaussian approximation approach. However, current ESS estimation methods are limited by high computational demands and potential inaccuracies.We propose a novel ESS estimation method that uses summary statistics and nonparametric regression models to efficiently and accurately estimate ESS.The effectiveness and accuracy of our method are validated through simulations, demonstrating significant improvements in computational efficiency and estimation accuracy.
Methodological Approaches for Incorporating Marginalized Populations into HPV Vaccine Modeling: A Systematic Review.
Spencer JC, Yanguela J, Spees LP … +8 more , Odebunmi OO, Ilyasova AA, Biddell CB, Hassmiller Lich K, Mills SD, Higgins CR, Ozawa S, Wheeler SB
Med Decis Making · 2025 May · PMID 40088125 · Full text
Delineation of historically marginalized populations in decision models can identify strategies to improve equity but requires assumptions in both model structure and stratification of input data. We sought to character...
Delineation of historically marginalized populations in decision models can identify strategies to improve equity but requires assumptions in both model structure and stratification of input data. We sought to characterize alternative methodological approaches for incorporating marginalized populations into human papillomavirus (HPV) vaccine decision-support models. We conducted a systematic search of PubMed, CINAHL, Scopus, and Embase from January 2006 through June 2022. We identified simulation models of HPV vaccination that refine any model input to specifically reflect a marginalized population. We extracted data on key methodological decisions across modeling approaches to incorporate marginalized populations, including stratification of inputs, model structure, attribution of prevaccine disparities, calibration, validation, and sensitivity analyses. We identified 30 models that stratified inputs by sexual behavior (i.e., men who have sex with men), HIV infection status, race, ethnicity, income, rurality, or combinations of these. We identified 5 common approaches used to incorporate marginalized groups. These included models based primarily on differences in sexual behavior (k = 6), HPV cancer incidence (k = 10), cancer screening and care access (k = 4), and HPV natural history (through either direct incorporation of data [k = 10] or calibration [k = 5]). Few models evaluated sensitivity around their conceptualization of the marginalized group, and only 5 models validated outcomes for the marginalized group. Evaluated studies reflected a variety of settings and research questions, making it difficult to evaluate the implications of differences across modeling approaches. Modelers should be explicit about the assumptions and theory driving their model structure and input parameters specific to key marginalized populations, such as the causes of prevaccination differences in outcomes. More emphasis is needed on model validation and rigorous sensitivity analysis.HighlightsWe identified 30 unique HPV vaccination models that incorporated marginalized populations, including populations living with HIV, low-income or rural populations, and individuals of a marginalized race, ethnicity, or sexual behavior.Methods for incorporating these populations, as well as the assumptions inherent in the modeling structure and parameter selections, varied substantially, with models explicitly or implicitly attributing prevaccine differences to alternative combinations of biological, behavioral, and societal mechanisms.Modelers seeking to incorporate marginalized populations should be transparent about assumptions underlying model structure and data and examine these assumptions in sensitivity analysis when possible.
Do Treatment Choices by Artificial Intelligence Correspond to Reality? Retrospective Comparative Research with Necrotizing Enterocolitis as a Use Case.
Verhoeven R, Mulia S, Kooi EMW … +1 more , Hulscher JBF
Med Decis Making · 2025 May · PMID 40071849 · Full text
BackgroundIn cases of surgical necrotizing enterocolitis (NEC), the choice between laparotomy (LAP) or comfort care (CC) presents a complex, ethical dilemma. A behavioral artificial intelligence technology (BAIT) decisio...
BackgroundIn cases of surgical necrotizing enterocolitis (NEC), the choice between laparotomy (LAP) or comfort care (CC) presents a complex, ethical dilemma. A behavioral artificial intelligence technology (BAIT) decision aid was trained on expert knowledge, providing an output as " percentage of experts advise laparotomy for this patient." This retrospective study aims to compare this output to clinical practice.DesignVariables required for the decision aid were collected of preterm patients with NEC for whom the decision of LAP or CC had been made. These data were used in 2 BAIT model versions: one center specific, built on the input of experts from the same center as the patients, and a nationwide version, incorporating the input of additional experts. The Mann-Whitney test compared the model output for the 2 groups (LAP/CC). In addition, model output was classified as advice for LAP or CC, after which the chi-square test assessed correspondence with observed decisions.ResultsForty patients were included in the study (20 LAP). Model output ( percentage of experts advising LAP) was higher in the LAP group than in the CC group (median 95.1% v. 46.1% in the center-specific version and 97.3% v. 67.5% in the nationwide version, both < 0.001). With an accuracy of 85.0% by the center-specific and 80.0% by the nationwide version, both showed significant correspondence with observed decisions ( < 0.001).LimitationsWe are merely examining a proof of concept of the decision aid using a small number of participants from 1 center.ConclusionsThis retrospective study demonstrates that treatment choices by artificial intelligence align with clinical practice in at least 80% of cases.ImplicationsFollowing prospective validation and ongoing refinements, the decision aid may offer valuable support to practitioners in future NEC cases.HighlightsThis study assesses the output of behavioral artificial intelligence technology in deciding between laparotomy and comfort care in surgical necrotizing enterocolitis.The model output aligns with clinical practice in at least 80% of patient cases.Following prospective validation, the decision aid may offer valuable support to physicians working at the neonatal intensive care unit.
Changing Time Representation in Microsimulation Models.
Wong EK, Isaranuwatchai W, Sale JEM … +3 more , Tricco AC, Straus SE, Naimark DMJ
Med Decis Making · 2025 Apr · PMID 39995284 · Full text
BackgroundIn microsimulation models of diseases with an early, acute phase requiring short cycle lengths followed by a chronic phase, fixed short cycles may lead to computational inefficiency. Examples include epidemic o...
BackgroundIn microsimulation models of diseases with an early, acute phase requiring short cycle lengths followed by a chronic phase, fixed short cycles may lead to computational inefficiency. Examples include epidemic or resource constraint models with early short cycles where long-term economic consequences are of interest for individuals surviving the epidemic or ultimately obtaining the resource. In this article, we demonstrate methods to improve efficiency in such scenarios. Furthermore, we show that care must be taken when applying these methods to epidemic or resource constraint models to avoid bias.MethodsTo demonstrate efficiency, we compared the model runtime among 3 versions of a microsimulation model: with short fixed cycles for all states (FCL), with dynamic cycle length (DCL) defined locally for each state, and with DCL features plus a discrete-event-like hybrid component. To demonstrate bias mitigation, we compared discounted lifetime costs for 3 versions of a resource constraint model: with a fixed horizon where simulation stops, with a fixed entry horizon beyond which new individuals could not enter the model, and with a fixed entry horizon plus a mechanism to maintain a constant level of competition for the resource after the horizon.ResultsThe 3 versions of the microsimulation model had average runtimes of 515 (95% credible interval [CI]: 477 to 545; FCL), 2.70 (95% CI: 1.48 to 2.92; DCL), and 1.45 (95% CI: 1.26 to 2.61; DCL-pseudo discrete event simulation) seconds, respectively. The first 2 resource constraint versions underestimated costs relative to the constant competition version: $20,055 (95% CI: $19,000 to $21,120), $27,030 (95% CI: $24,680 to $29,412), and $33,424 (95% CI: $27,510 to $44,484), respectively.LimitationsThe magnitude of improvements in efficiency and reduction in bias may be model specific.ConclusionChanging time representation in microsimulation may offer computational advantages.HighlightsShort cycle lengths may be required to model the acute phase of an illness but lead to computational inefficiency in a subsequent chronic phase in microsimulation models.A solution is to create state-specific cycle lengths so that cycle lengths change dynamically as the simulation progresses.Computational efficiency can be enhanced further by using a hybrid model containing discrete-event-simulation-like features.Hybrid models can efficiently handle events subsequent to exit from an epidemic or resource constraint model provided steps are taken to mitigate potential bias.
Segmenting the Population and Estimating Transition Probabilities Using Data on Health and Health-Related Social Service Needs from the US Health and Retirement Study.
Duminy L
Med Decis Making · 2025 Apr · PMID 39991900 · Publisher ↗
BackgroundSimulation modeling is a promising tool to help policy makers and providers make evidence-based decisions when evaluating integrated care programs. The functionality of such models, however, depends on 2 prereq...
BackgroundSimulation modeling is a promising tool to help policy makers and providers make evidence-based decisions when evaluating integrated care programs. The functionality of such models, however, depends on 2 prerequisites: 1) the analytical segmentation of populations to capture both health and health-related social service (HASS) needs and 2) the precise estimation of transition probabilities among the various states of need.MethodsWe took a validated instrument for segmenting the population by HASS needs and adapted it to the Health and Retirement Study, a nationally representative survey dataset from the US population older than 50 y. We then estimated the transition probabilities across all 10 need states and death using multistate modeling. A need state was defined as a combination of any of the 5 ordinal global impression segments and a complicating factor status.ResultsKaplan-Meier survival curves, log-rank tests, and c-indices were used to assess predictive validity in relation to mortality. The Markov traces, using the estimated transition probability to replicate 2 closed cohorts, resembled the proportion of individuals per health state across subsequent waves well enough to indicate adequate fit of the estimated transition probabilities.ConclusionsThis article provides a population segmentation approach that incorporates HASS needs for the US population and 1-y transition probabilities across HASS need states and death. This is the first application of HASS segmentation that can estimate transitions between all 10 HASS need states, facilitating novel analysis of policy decisions related to integrated care.ImplicationsOur results will be used as input for a simulation model that performs scenario analysis on the long-term effects of various integrated care policies on population health.HighlightsWe took a validated tool for segmenting the population according to health and health-related social service (HASS) needs and adapted it to the Health and Retirement Study, a nationally representative survey dataset from the US population over the age of 50 y.We estimated the 1-y transition probabilities across all 10 HASS segments and death.This is the first application of a version of this HASS segmentation tool that includes HASSs in the various need states when estimating transition probabilities.Our results will be used as input for a simulation model that performs scenario analysis on the long-term effects of various integrated care policies on population health.
Leveling up: Treating Uptake as Endogenous May Increase the Value of Screening Programs.
Robles-Zurita JA, Hawkins N, Bouttell J
Med Decis Making · 2025 Apr · PMID 39989263 · Publisher ↗
BackgroundWe aimed to illustrate that health economists should consider individual heterogeneity when solving the problem of finding the optimal combination of sensitivity and specificity that maximizes the average healt...
BackgroundWe aimed to illustrate that health economists should consider individual heterogeneity when solving the problem of finding the optimal combination of sensitivity and specificity that maximizes the average health utility of a target population in a screening program.MethodsA theoretical framework compares the solution under standard economics of diagnoses to the optimal combination under an endogenous uptake analysis, where screening participation is given by heterogenous health preferences. An applied example used calibrated parameters with real data from the bowel cancer screening program in the United Kingdom. Scenario analyses show how the results would change with parameter values, if disease risk and health utilities were not independent and if screening uptake were not completely determined by health preferences.ResultsA general theoretical result states that the endogenous uptake analysis leads to a weakly higher true- and false-positive rate than would be optimal under the standard approach. In the same way, the endogenous solution would lead to a lower uptake rate. The base-case scenario of the applied example illustrates that a screening program using the endogenous solution would generate 21.1% more quality-adjusted life-years than when using the standard solution. The scenario analyses show when the endogenous analysis is most valued and that the general result applies for a wide range of situations when theoretical assumptions are relaxed.LimitationsThe results obtained are valid under the assumptions made. Analysts should evaluate if those could hold in the applied screening context.ConclusionsIndividual heterogeneity and uptake decisions are relevant factors to consider in the problem of finding an optimal combination of sensitivity and specificity for a screening test.HighlightsThe value of screening programs can be higher if heterogeneity of preferences in the target population is considered.The optimal operation of a screening test depends on health utilities of the target population and on the heterogeneity of these health utilities.Under heterogeneity of health utilities, the optimal operation of a screening test does not maximize screening uptake.A general theoretical result states that the endogenous uptake analysis leads to a weakly higher true- and false-positive rate than would be optimal under a standard approach; this is true for a wide range of situations.
Development of a Decision Model to Estimate the Outcomes of Treatment Sequences in Advanced Melanoma.
de Groot S, Blommestein HM, Leeneman B … +15 more , Uyl-de Groot CA, Haanen JBAG, Wouters MWJM, Aarts MJB, van den Berkmortel FWPJ, Blokx WAM, Boers-Sonderen MJ, van den Eertwegh AJM, de Groot JWB, Hospers GAP, Kapiteijn E, van Not OJ, van der Veldt AAM, Suijkerbuijk KPM, van Baal PHM
Med Decis Making · 2025 Apr · PMID 39985400 · Full text
BackgroundA decision model for patients with advanced melanoma to estimate outcomes of a wide range of treatment sequences is lacking.ObjectivesTo develop a decision model for advanced melanoma to estimate outcomes of tr...
BackgroundA decision model for patients with advanced melanoma to estimate outcomes of a wide range of treatment sequences is lacking.ObjectivesTo develop a decision model for advanced melanoma to estimate outcomes of treatment sequences in clinical practice with the aim of supporting decision making. The article focuses on methodology and long-term health benefits.MethodsA semi-Markov model with a lifetime horizon was developed. Transitions describing disease progression, time to next treatment, and mortality were estimated from real-world data (RWD) as a function of time since starting treatment or disease progression and patient characteristics. Transitions were estimated separately for melanoma with and without a BRAF mutation and for patients with favorable and intermediate prognostic factors. All transitions can be adjusted using relative effectiveness of treatments derived from a network meta-analysis of randomized controlled trials (RCTs). The duration of treatment effect can be adjusted to obtain outcomes under different assumptions.ResultsThe model distinguishes 3 lines of systemic treatment for melanoma with a BRAF mutation and 2 lines of systemic treatment for melanoma without a BRAF mutation. Life expectancy ranged from 7.8 to 12.0 years in patients with favorable prognostic factors and from 5.1 to 8.7 years in patients with intermediate prognostic factors when treated with sequences consisting of targeted therapies and immunotherapies. Scenario analyses illustrate how estimates of life expectancy depend on the duration of treatment effect.ConclusionThe model is flexible because it can accommodate different treatments and treatment sequences, and the duration of treatment effects and the transitions influenced by treatment can be adjusted. We show how using RWD and data from RCTs can harness advantages of both data sources, guiding the development of future decision models.HighlightsThe model is flexible because it can accommodate different treatments and treatment sequences, and the duration of treatment effects as well as the transitions that are influenced by treatment can be adjusted.The long-term health benefits of treatment sequences depend on the place of different therapies within a treatment sequence.Assumptions about the duration of relative treatment effects influence the estimates of long-term health benefits.We show how the use of real-world data and data from randomized controlled trials harness the advantages of both data sources, guiding the development of future decision models.
Health Utilities in People with Hepatitis C Virus Infection: A Study Using Real-World Population-Level Data.
Saeed YA, Mitsakakis N, Feld JJ … +3 more , Krahn MD, Kwong JC, Wong WWL
Med Decis Making · 2025 Apr · PMID 39985398 · Full text
BackgroundHepatitis C virus (HCV) infection is associated with reduced quality of life and health utility. It is unclear whether this is primarily due to HCV infection itself or commonly co-occurring patient characterist...
BackgroundHepatitis C virus (HCV) infection is associated with reduced quality of life and health utility. It is unclear whether this is primarily due to HCV infection itself or commonly co-occurring patient characteristics such as low income and mental health issues. This study aims to estimate and separate the effects of HCV infection on health utility from the effects of clinical and sociodemographic factors using real-world population-level data.MethodsWe conducted a cross-sectional retrospective cohort study to estimate health utilities in people with and without HCV infection in Ontario, Canada, from 2000 to 2014 using linked survey data from the Canadian Community Health Survey and health administrative data. Utilities were derived from the Health Utilities Index Mark 3 instrument. We used propensity score matching and multivariable linear regression to examine the impact of HCV infection on utility scores while adjusting for clinical and sociodemographic factors.ResultsThere were 7,102 individuals with hepatitis C status and health utility data available (506 HCV-positive, 6,596 HCV-negative). Factors associated with marginalization were more prevalent in the HCV-positive cohort (e.g., household income <$20,000: 36% versus 15%). Propensity score matching resulted in 454 matched pairs of HCV-positive and HCV-negative individuals. HCV-positive individuals had substantially lower unadjusted utilities than HCV-negative individuals did (mean ± standard error: 0.662 ± 0.016 versus 0.734 ± 0.015). The regression model showed that HCV positivity (coefficient: -0.066), age, comorbidity, mental health history, and household income had large impacts on health utility.ConclusionsHCV infection is associated with low health utility even after controlling for clinical and sociodemographic variables. Individuals with HCV infection may benefit from additional social services and supports alongside antiviral therapy to improve their quality of life.HighlightsHepatitis C virus (HCV) infection is associated with reduced quality of life and health utility. There is debate in the literature on whether this is primarily due to HCV infection itself or commonly co-occurring patient characteristics such as low income and mental health issues.We showed that individuals with HCV infection have substantially lower health utilities than uninfected individuals do even after controlling for clinical and sociodemographic variables, based on a large, real-world population-level dataset. Socioeconomically marginalized individuals with HCV infection had particularly low health utilities.In addition to improving access to HCV testing and treatment, it may be beneficial to provide social services such as mental health and financial supports to improve the quality of life and health utility of people living with HCV.
The Effect of Patient Decision Aid Attributes on Patient Outcomes: A Network Meta-Analysis of a Systematic Review.
Stacey D, Carley M, Gunderson J … +6 more , Hsieh SC, Kelly SE, Lewis KB, Smith M, Volk RJ, Wells G
Med Decis Making · 2025 May · PMID 39968925 · Full text
BackgroundPatient decision aids (PtDAs) are effective interventions to help people participate in health care decisions. Although there are quality standards, PtDAs are complex interventions with variability in their att...
BackgroundPatient decision aids (PtDAs) are effective interventions to help people participate in health care decisions. Although there are quality standards, PtDAs are complex interventions with variability in their attributes.PurposeTo determine and compare the effects of PtDA attributes (e.g., content elements, delivery timing, development) on primary outcomes for adults facing health care decisions.Data SourcesA systematic review of randomized controlled trials (RCTs) comparing PtDAs to usual care.Study SelectionEligible RCTs measured at least 1 primary outcome: informed values choice, knowledge, accurate risk perception, decisional conflict subscales, and undecided.Data AnalysisA network meta-analysis evaluated direct and indirect effects of PtDA attributes on primary outcomes.Data SynthesisOf 209 RCTs, 149 reported eligible outcomes. There was no difference in outcomes for PtDAs using implicit compared with explicit values clarification. Compared with PtDAs with probabilities, PtDAs without probabilities were associated with poorer patient knowledge (mean difference [MD] -3.86; 95% credible interval [CrI] -7.67, -0.03); there were no difference for other outcomes. There was no difference in outcomes when PtDAs presented information in ways that decrease cognitive demand and mixed results when PtDAs used strategies to enhance communication. Compared with PtDAs delivered in preparation for consultations, PtDAs used during consultations were associated with poorer knowledge (MD -4.34; 95% CrI -7.24, -1.43) and patients feeling more uninformed (MD 5.07; 95% CrI 1.06, 9.11). Involving patients in PtDA development was associated with greater knowledge (MD 6.56; 95% CrI 1.10, 12.03) compared with involving health care professionals alone.LimitationsThere were no direct comparisons between PtDAs with/without attributes.ConclusionsImprovements in knowledge were influenced by some PtDA content elements, using PtDA content before the consultation, and involving patients in development. There were few or no differences on other outcomes.HighlightsThis is the first known network meta-analysis conducted to determine the contributions of the different attributes of patient decision aids (PtDAs) on patient outcomes.There was no difference in outcomes when PtDAs used implicit compared with explicit values clarification.There were greater improvements in knowledge when PtDAs included information on probabilities, PtDAs were used in preparation for the consultation or development included patients on the research team.There was no difference in outcomes when PtDAs presented information in ways that decrease cognitive demand and mixed results when PtDAs used strategies to enhance communication.
Expected Value of Sample Information Calculations for Risk Prediction Model Validation.
Sadatsafavi M, Vickers AJ, Lee TY … +2 more , Gustafson P, Wynants L
Med Decis Making · 2025 Apr · PMID 39963746 · Full text
BackgroundThe purpose of external validation of a risk prediction model is to evaluate its performance before recommending it for use in a new population. Sample size calculations for such validation studies are currentl...
BackgroundThe purpose of external validation of a risk prediction model is to evaluate its performance before recommending it for use in a new population. Sample size calculations for such validation studies are currently based on classical inferential statistics around metrics of discrimination, calibration, and net benefit (NB). For NB as a measure of clinical utility, the relevance of inferential statistics is doubtful. Value-of-information methodology enables quantifying the value of collecting validation data in terms of expected gain in clinical utility.MethodsWe define the validation expected value of sample information (EVSI) as the expected gain in NB by procuring a validation sample of a given size. We propose 3 algorithms for EVSI computation and compare their face validity and computation time in simulation studies. In a case study, we use the non-US subset of a clinical trial to create a risk prediction model for short-term mortality after myocardial infarction and calculate validation EVSI at a range of sample sizes for the US population.ResultsComputation methods generated similar EVSI values in simulation studies, although they differed in numerical accuracy and computation times. At 2% risk threshold, procuring 1,000 observations for external validation, had an EVSI of 0.00101 in true-positive units or 0.04938 in false-positive units. Scaled by heart attack incidence in the United States, the population EVSI was 806 in true positives gained, or 39,500 in false positives averted, annually. Validation studies with >4,000 observations had diminishing returns, as the EVSIs were approaching their maximum possible value.ConclusionValue-of-information methodology quantifies the return on investment from conducting an external validation study and can provide a value-based perspective when designing such studies.HighlightsIn external validation studies of risk prediction models, the finite size of the validation sample leads to uncertain conclusions about the performance of the model. This uncertainty has hitherto been approached from a classical inferential perspective (e.g., confidence interval around the c-statistic).Correspondingly, sample size calculations for validation studies have been based on classical inferential statistics. For measures of clinical utility such as net benefit, the relevance of this approach is doubtful.This article defines the expected value of sample information (EVSI) for model validation and suggests algorithms for its computation. Validation EVSI quantifies the return on investment from conducting a validation study.Value-based approaches rooted in decision theory can complement contemporary study design and sample size calculation methods in predictive analytics.
Recalibrating an Established Microsimulation Model to Capture Trends and Projections of Colorectal Cancer Incidence and Mortality.
Lew JB, Luo Q, Worthington J … +7 more , Ge H, He E, Steinberg J, Caruana M, O'Connell DL, Feletto E, Canfell K
Med Decis Making · 2025 Apr · PMID 39915917 · Publisher ↗
BackgroundChanging colorectal cancer (CRC) incidence rates, including recent increases for people younger than 50 y, need to be considered in planning for future cancer control and screening initiatives. Reliable estimat...
BackgroundChanging colorectal cancer (CRC) incidence rates, including recent increases for people younger than 50 y, need to be considered in planning for future cancer control and screening initiatives. Reliable estimates of the impact of changing CRC trends on the National Bowel Cancer Screening Program (NBCSP) are essential for programmatic planning in Australia. An existing microsimulation model of CRC, , was updated to reproduce Australian CRC trends data and provide updated projections of CRC- and screening-related outcomes to inform clinical practice guidelines for the prevention of CRC.Methods was recalibrated to reproduce statistical age-period-cohort model trends and projections of CRC incidence for 1995-2045 in the absence of the NBCSP as well as published data on CRC incidence trends, stage distribution, and survival in 1995-2020 in Australia. The recalibrated predictions were validated by comparison with published Australian CRC mortality trends for 1995-2015 and statistical projections to 2040. Metamodels were developed to aid the calibration process and significantly reduce the computational burden.Results was recalibrated, and best-fit parameter sets were identified for lesion incidence, CRC stage progression rates, detection rates, and survival rates by age, sex, bowel location, cancer stage, and birth year. The recalibrated model was validated and successfully reproduced observed CRC mortality rates for 1995-2015 and statistical projections for 2016-2030.ConclusionThe recalibrated model captures significant additional detail on the future incidence and mortality burden of CRC in Australia. This is particularly relevant as younger cohorts with higher CRC incidence rates approach screening ages to inform decision making for these groups. The metamodeling approach allows fast recalibration and makes regular updates to incorporate new evidence feasible.HighlightsIn Australia, colorectal cancer incidence rates are increasing for people younger than 50 y but decreasing for people older than 50 y, and colorectal cancer survival is improving as new treatment technologies emerge.To evaluate the future health and economic impact of screening and inform policy, modeling must include detailed trends and projections of colorectal cancer incidence, mortality, and diagnosis stage.We used novel techniques including integrative age-period cohort projections and metamodel calibration to update , a detailed microsimulation of colorectal cancer and screening in Australia.
Development of a Microsimulation Model to Project the Future Prevalence of Childhood Cancer in Ontario, Canada.
Moskalewicz A, Gupta S, Nathan PC … +1 more , Pechlivanoglou P
Med Decis Making · 2025 Apr · PMID 39902754 · Publisher ↗
BackgroundEstimates of the future prevalence of childhood cancer are informative for health system planning but are underutilized. We describe the development of a pediatric oncology microsimulation model for prevalence...
BackgroundEstimates of the future prevalence of childhood cancer are informative for health system planning but are underutilized. We describe the development of a pediatric oncology microsimulation model for prevalence (POSIM-Prev) and illustrate its application to produce projections of incidence, survival, and limited-duration prevalence of childhood cancer in Ontario, Canada, until 2040.MethodsPOSIM-Prev is a population-based, open-cohort, discrete-time microsimulation model. The model population was updated annually from 1970 to 2040 to account for births, deaths, net migration, and incident cases of childhood cancer. Prevalent individuals were followed until death, emigration, or the last year of simulation. Median population-based outcomes with 95% credible intervals (CrIs) were generated using Monte Carlo simulation. The methodology to derive model inputs included generalized additive modeling of cancer incidence, parametric survival modeling, and stochastic population forecasting. Individual-level data from provincial cancer registries for years 1970 to 2019 informed cancer-related model inputs and internal validation.ResultsThe number of children (aged 0-14 y) diagnosed with cancer in Ontario is projected to rise from 414 (95% CrI: 353-486) in 2020 to 561 (95% CrI: 481-653) in 2039. The 5-y overall survival rate for 2030-2034 is estimated to reach 90% (95% CrI: 88%-92%). By 2040, 24,088 (95% CrI: 22,764-25,648) individuals with a history of childhood cancer (diagnosed in Ontario or elsewhere) are projected to reside in the province. The model accurately reproduced historical trends in incidence, survival, and prevalence when validated.ConclusionsThe rising incidence and prevalence of childhood cancer will create increased demand for both acute cancer care and long-term follow-up services in Ontario. The POSIM-Prev model can be used to support long-range health system planning and future health technology assessments in jurisdictions that have access to similar model inputs.HighlightsThis article describes the development of a population-based, discrete-time microsimulation model that can simulate incident and prevalent cases of childhood cancer in Ontario, Canada, until 2040.Use of an open cohort framework allowed for estimation of the potential impact of net migration on childhood cancer prevalence.In addition to supporting long-term health system planning, this model can be used in future health technology assessments, by providing a demographic profile of incident and prevalent cases for model conceptualization and budget impact purposes.This modeling framework is adaptable to other jurisdictions and disease areas where individual-level data for incidence and survival are available.
Directed Acyclic Graphs in Decision-Analytic Modeling: Bridging Causal Inference and Effective Model Design in Medical Decision Making.
Dijk SW, Korf M, Labrecque JA … +6 more , Pandya A, Ferket BS, Hallsson LR, Wong JB, Siebert U, Hunink MGM
Med Decis Making · 2025 Apr · PMID 39846352 · Full text
Decision-analytic models (DAMs) are essentially informative yet complex tools for solving questions in medical decision making. When their complexity grows, the need for causal inference techniques becomes evident as cau...
Decision-analytic models (DAMs) are essentially informative yet complex tools for solving questions in medical decision making. When their complexity grows, the need for causal inference techniques becomes evident as causal relationships between variables become unclear. In this methodological commentary, we argue that graphical representations of assumptions on such relationships, directed acyclic graphs (DAGs), can enhance the transparency of decision models and aid in parameter selection and estimation through visually specifying backdoor paths (i.e., potential biases in parameter estimates) and visually clarifying structural modeling choices of frontdoor paths (i.e., the effect of the model structure on the outcome). This commentary discusses the benefit of integrating DAGs and DAMs in medical decision making and in particular health economics with 2 applications: the first examines statin use for prevention of cardiovascular disease, and the second considers mindfulness-based interventions for students' stress. Despite the potential application of DAGs in the decision science framework, challenges remain, including simplicity, defining the scope of a DAG, unmeasured confounding, noncausal aspects, and limited data availability or quality. Broader adoption of DAGs in decision science requires full-model applications and further debate.HighlightsOur commentary proposes the application of directed acyclic graphs (DAGs) in the design of decision-analytic models, offering researchers a valuable and structured tool to enhance transparency and accuracy by bridging the gap between causal inference and model design in medical decision making.The practical examples in this article showcase the transformative effect DAGs can have on model structure, parameter selection, and the resulting conclusions on effectiveness and cost-effectiveness.This methodological article invites a broader conversation on decision-modeling choices grounded in causal assumptions.
A Fast Nonparametric Sampling Method for Time to Event in Individual-Level Simulation Models.
Garibay-Treviño DU, Jalal H, Alarid-Escudero F
Med Decis Making · 2025 Feb · PMID 39757494 · Full text
The nonparametric sampling method is generic and can sample times to an event from any discrete (or discretizable) hazard without requiring any parametric assumption.The method is showcased with 5 commonly used distribut...
The nonparametric sampling method is generic and can sample times to an event from any discrete (or discretizable) hazard without requiring any parametric assumption.The method is showcased with 5 commonly used distributions in discrete-event simulation models.The method produced very similar expected times to events, as well as their probability distribution, compared with analytical results.We provide a multivariate categorical sampling function for R and Python programming languages to sample times to events from processes with different hazards simultaneously.
Changes in Risk Tolerance for Ovarian Cancer Prevention Strategies during the COVID-19 Pandemic: Results of a Discrete Choice Experiment.
Egleston BL, Daly MB, Lew K … +8 more , Bealin L, Husband AD, Stopfer JE, Przybysz P, Tchuvatkina O, Wong YN, Garber JE, Rebbeck TR
Med Decis Making · 2025 Feb · PMID 39722532 · Full text
BACKGROUND: Prior to COVID-19, little was known about how risks associated with such a pandemic would compete with and influence patient decision making regarding cancer risk reducing medical decision making. We investig...
BACKGROUND: Prior to COVID-19, little was known about how risks associated with such a pandemic would compete with and influence patient decision making regarding cancer risk reducing medical decision making. We investigated how the pandemic affected preferences for medical risk-reducing strategies among women at elevated risk of breast or ovarian cancer. METHODS: We conducted a discrete choice experiment. Women about to undergo genetic testing and counseling at 2 medical centers participated. Enrollment occurred between 2019 and 2022, allowing us to investigate changes in preferences from before the pandemic to after the pandemic. Women chose from permuted scenarios that specified type of surgery, age of menopause, quality of menopausal symptoms, and risk of ovarian cancer, heart disease, or osteoporosis. RESULTS: A total of 355 women, with a median age of 36 y, participated. In 2019, women were less likely to choose prevention scenarios with higher ovarian cancer risk (odds ratio [OR] = 0.42 per 10-point increase in risk, 95% confidence interval [CI] 0.22-0.61). In June 2020, the effect of higher ovarian cancer risk scenarios on choice was attenuated (OR = 0.86, 95% CI 0.68-1.04), with the effect becoming more salient again by July 2021 (OR = 0.59, 95% CI 0.52-0.67) ( = 0.039 for test of temporal interaction). No other attribute demonstrated a temporal trend. CONCLUSION: The risks associated with the COVID-19 pandemic may have attenuated the impact of risk of ovarian cancer on choice of risk-reducing prevention strategies for ovarian cancer. The maximum attenuation occurred at the beginning of the pandemic when access to risk-reducing surgery was most restricted. Our findings highlight how individuals evaluate competing health risks and adjust their uptake of cancer prevention strategies when faced with a future pandemic or similar global crisis. HIGHLIGHTS: In this discrete choice experiment, women were much less likely to choose prevention scenarios that had higher ovarian cancer risk prior to the COVID-19 pandemic than after the pandemic.The attenuation of preferences may have persisted through 2022.COVID-19 may have altered the relative importance of factors that motivate women to undergo risk-reducing surgeries.
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