Medical Decision Making最新文献

BackgroundAny sample of individuals has its own unique distribution of preferences for choices that they make. Discrete choice models try to capture these distributions. Mixed logits are by far the most commonly used choice model in health. Many parametric specifications for these models are available. We test a range of alternative assumptions and model averaging to test if or how model outputs are affected.DesignScoping review of current modeling practices. Seven alternative distributions and model averaging over all distributional assumptions were compared on 4 datasets: 2 were stated preference, 1 was revealed preference, and 1 was simulated. Analyses examined model fit, preference distributions, willingness to pay, and forecasting.ResultsAlmost universally, using normal distributions is the standard practice in health. Alternative distributional assumptions outperformed standard practice. Preference distributions and the mean willingness to pay varied significantly across specifications and were seldom comparable to those derived from normal distributions. Model averaging offered distributions allowing for greater flexibility and further gains in fit, reproduced underlying distributions in simulations, and mitigated against analyst bias arising from distribution selection. There was no evidence that distributional assumptions affected predictions from models.LimitationsOur focus was on mixed logit models since these models are the most common in health, although latent class models are also used.ConclusionsThe standard practice of using all normal distributions appears to be an inferior approach for capturing random preference heterogeneity. Implications. Researchers should test alternative assumptions to normal distributions in their models.HighlightsHealth modelers use normal mixing distributions for preference heterogeneity.Alternative distributions offer more flexibility and improved model fit.Model averaging offers yet more flexibility and improved model fit.Distributions and willingness to pay differ substantially across alternatives.

PurposeWhen using stochastic models for cost-effectiveness analysis (CEA), run-to-run outcome variability arising from model stochasticity can sometimes exceed the change in outcomes resulting from an intervention, especially when individual-level efficacy is small, leading to counterintuitive results. This issue is compounded for probabilistic sensitivity analyses (PSAs), in which stochastic noise can obscure the influence of parameter uncertainty. This study evaluates meta-modeling as a variance-reduction technique to mitigate stochastic noise while preserving parameter uncertainty in PSAs.MethodsWe applied meta-modeling to 2 simulation models: 1) a 4-state Sick-Sicker model and 2) an agent-based HIV transmission model among men who have sex with men (MSM). We conducted a PSA and applied 3 meta-modeling techniques-linear regression, generalized additive models, and artificial neural networks-to reduce stochastic noise. Model performance was assessed using R² and root mean squared error (RMSE) values on a validation dataset. We compared PSA results by examining scatter plots of incremental costs and quality-adjusted life-years (QALYs), cost-effectiveness acceptability curves (CEACs), and the occurrence of unintuitive results, such as interventions appearing to reduce QALYs due to stochastic noise.ResultsIn the Sick-Sicker model, stochastic noise increased variance in incremental costs and QALYs. Applying meta-modeling techniques substantially reduced this variance and nearly eliminated unintuitive results, with R² and RMSE values indicating good model fit. In the HIV agent-based model, all 3 meta-models effectively reduced outcome variability while retaining parameter uncertainty, yielding more informative CEACs with higher probabilities of being cost-effective for the optimal strategy.ConclusionsMeta-modeling effectively reduces stochastic noise in simulation models while maintaining parameter uncertainty in PSA, enhancing the reliability of CEA results without requiring an impractical number of simulations.HighlightsWhen using complex stochastic models for cost-effectiveness analysis (CEA), stochastic noise can overwhelm intervention effects and obscure the impact of parameter uncertainty on CEA outcomes in probabilistic sensitivity analysis (PSA).Meta-modeling offers a solution by effectively reducing stochastic noise in complex stochastic simulation models without increasing computational burden, thereby improving the interpretability of PSA results.

IntroductionThe potential for multicancer early detection (MCED) tests to detect cancer at earlier stages is currently being evaluated in screening clinical trials. Once trial evidence becomes available, modeling will be necessary to predict the effects on final outcomes (benefits and harms), account for heterogeneity in determining clinical and cost-effectiveness, and explore alternative screening program specifications. The natural history of disease (NHD) component will use statistical, mathematical, or calibration methods. This work aims to identify, review, and critically appraise the existing literature for alternative modeling approaches proposed for MCED that include an NHD component.MethodsModeling approaches for MCED screening that include an NHD component were identified from the literature, reviewed, and critically appraised. Purposively selected (non-MCED) cancer-screening models were also reviewed. The appraisal focused on the scope, data sources, evaluation approaches, and the structure and parameterization of the models.ResultsFive different MCED models incorporating an NHD component were identified and reviewed, alongside 4 additional (non-MCED) models. The critical appraisal highlighted several features of this literature. In the absence of trial evidence, MCED effects are based on predictions derived from test accuracy. These predictions rely on simplifying assumptions with unknown impacts, such as the stage-shift assumption used to estimate mortality impacts from predicted stage shifts. None of the MCED models fully characterized uncertainty in the NHD or examined uncertainty in the stage-shift assumption.ConclusionThere is currently no modeling approach for MCEDs that can integrate clinical study evidence. In support of policy, it is important that efforts are made to develop models that make the best use of data from the large and costly clinical studies being designed and implemented across the globe.HighlightsIn the absence of trial evidence, published estimates of the effects of multicancer early detection (MCED) tests are based on predictions derived from test accuracy.These predictions rely on simplifying assumptions, such as the stage-shift assumption used to estimate mortality effects from predicted stage shifts. The effects of such simplifying assumptions are mostly unknown.None of the existing MCED models fully characterize uncertainty in the natural history of disease; none examine uncertainty in the stage-shift assumption.Currently, there is no modeling approach that can integrate clinical study evidence.

ObjectivesTransgender (trans) people have disproportionately high HIV risk, yet adherence to preexposure prophylaxis (PrEP) remains low in this population. We aimed to determine which factors matter most in the decision of HIV-negative transgender adults to adhere to long-acting injectable PrEP (LA-PrEP), and the acceptability of providing incentives conditional on LA-PrEP program engagement.MethodsFrom March to April 2023, 385 trans adults in Washington State completed a discrete-choice experiment (DCE) eliciting preferences for a conditional economic incentive program that would provide free LA-PrEP and gender-affirming care during bimonthly visits. We used the best-best preference elicitation method across 2 hypothetical programs with an opt-out option. Program attributes included incentive format and amount, method for determining PrEP adherence, and type of hormone co-prescription. We used a rank-ordered mixed logit model for main results and estimated respondents' marginal willingness to accept each program attribute. We plotted the probability of choosing an incentivized LA-PrEP program over a range of respondent characteristics.ResultsThe optimal program design would 1) deliver incentives in cash, 2) confirm PrEP adherence via blood testing, 3) provide counseling in person, and 4) provide prescriptions for injectable gender-affirming hormones. From a maximum incentive amount of $1,200/year, respondents were willing to forgo up to $689 to receive incentives in cash (instead of voucher) and up to $547 to receive injectable (instead of oral) hormones. The probability of choosing a hypothetical program over no program waned as adults aged (>40 y) and as income increased (>$75,000/y).ConclusionsConditional economic incentives are likely acceptable and effective for improving LA-PrEP adherence, especially among younger trans adults with fewer financial resources. A randomized trial is needed to confirm the DCE's validity for predicting actual program uptake.HighlightsGender-related stigma, economic barriers, and medical concerns about hormone interactions can keep transgender (trans) adults from engaging in HIV prevention behaviors.Combining gender-affirming care with conditional economic incentives may help reduce present bias and increase trans persons' motivation to adhere to long-acting injectable preexposure prophylaxis (LA-PrEP).From a maximum yearly incentive of $1,200, trans discrete-choice experiment respondents were willing to forgo up to $689 to receive a cash (rather than voucher) incentive and up to $547 to receive co-prescriptions for injectable (rather than oral) hormones as part of a hypothetical HIV prevention program.The probability of choosing an LA-PrEP program over no program begins to wane as adults age (>40 y) and as annual income increases (>$75,000/year), such that incentivized LA-PrEP programs may be especially salient for younger trans adults with fewer financial resources.

Introduction. Calibration, a critical step in the development of simulation models, involves adjusting unobservable parameters to ensure that the outcomes of the model closely align with observed target data. This process is particularly vital in cancer simulation models with a natural history component, where direct data to inform natural history parameters are rarely available. Methods. We conducted a scoping review of studies published from 1980 to August 11, 2024, using keyword searches in PubMed and Web of Science. Eligible studies included cancer simulation models with a natural history component that used calibration methods for parameter estimation. Results. A total of 117 studies met the inclusion criteria. Nearly all studies (n = 115) specified calibration targets, while most studies (n = 91) described the parameter search algorithms used. Goodness-of-fit metrics (n = 87), acceptance criteria (n = 53), and stopping rule (n = 46) were reported less frequently. The most commonly used calibration targets were incidence, mortality, and prevalence, typically drawn from cancer registries and observational studies. Mean squared error was the most commonly used goodness-of-fit measure. Random search was the predominant method for parameter search, followed by the Bayesian approach and the Nelder-Mead method. Discussion. Despite recent advances in machine learning, such algorithms remain underutilized in the calibration of cancer simulation models. Further research is needed to compare the efficiency of different parameter search algorithms used for calibration.HighlightsThis work reviewed the literature of cancer simulation models with a natural history component and identified the calibration approaches used in these models with respect to the following attributes: cancer type, calibration target data source, calibration target type, goodness-of-fit metrics, search algorithms, acceptance criteria, stopping rule, computational time, modeling approach, and model stochasticity.Random search has been the predominant method for parameter search, followed by Bayesian approach and Nelder-Mead method.Machine learning-based algorithms, despite their fast advancement in the recent decade, have been underutilized in the cancer simulation models. Furthermore, more research is needed to compare different parameter search algorithms used for calibration.

Background. Observational studies are used to evaluate the effect of breast cancer screening programs, but their validity depends on use of different study designs. One of these is the evaluation model, which extends follow-up after screening only if women have been diagnosed with breast cancer during the screening program. However, to avoid lead-time bias, the inclusion of risk time should be based on screening invitation and not breast cancer diagnosis. The aim of this study is to investigate potential bias induced by the evaluation model. Methods. We used large-scale simulated datasets to investigate the evaluation model. Simulation model parameters for age-dependent breast cancer incidence, survival, breast cancer mortality, and all-cause mortality were obtained from Norwegian registries. Data were restricted to women aged 48 to 90 y and a period before screening implementation, 1986 to 1995. Simulation parameters were estimated for each of 2 periods (1986-1990 and 1991-1995). For the simulated datasets, 50% were randomly assigned to screening and 50% were not. Simulation scenarios depended on the magnitude of screening effect and level of overdiagnosis. For each scenario, we applied 2 study designs, the evaluation model and ordinary incidence-based mortality, to estimate breast cancer mortality rates for the screening and nonscreening groups. For each design, these rates were compared to assess potential bias. Results. In scenarios with no screening effect and no overdiagnosis, the evaluation model estimated 6% to 8% reductions in breast cancer mortality due to lead-time bias. Bias increased with overdiagnosis. Conclusions. The evaluation model was biased by lead time, especially in scenarios with overdiagnosis. Thus, the attempt to capture more of the screening effect using the evaluation model comes at the risk of introducing bias.HighlightsThe validity of observational studies of breast cancer screening programs depends on their study design being able to eliminate lead-time bias.The evaluation model has been used to evaluate breast cancer screening in recent studies but introduces a study design based on breast cancer diagnosis that may introduce lead-time bias.We used large-scale simulated datasets to compare study designs used to evaluate screening.We found that the evaluation model was biased by lead time and estimated reductions in breast cancer mortality in scenarios with no screening effect.

BackgroundIt is still a matter of debate whether a reduction in cancer-specific mortality due to cancer screening fully translates into a reduction in all-cause mortality and thus into a gain in life expectancy. Nevertheless, decision-analytic models simulating the health consequences of screening compared with no screening predict substantial gains in life expectancy.PurposeThe aim of this review was to systematically assess methodological competing mortality risk features that affect the translation of cancer-specific mortality reductions into gains in life expectancy in decision-analytic screening models for prostate, lung, breast, and colorectal cancer.Data SourcesLiterature databases were systematically searched for clinical and economic decision-analytic models evaluating the effect of screening for prostate, lung, breast, and colorectal cancer compared with no screening.Study SelectionForty-two clinical and economic decision-analytic models were included for narrative synthesis.Data ExtractionBasic information and specific methodological features of the included decision-analytic models were extracted using a standardized approach.Data SynthesisCharacteristics and methodological features of the identified studies were summarized in evidence tables.LimitationsThe review focused on models that reported undiscounted outcomes of life-years gained for standard screening strategies.ConclusionsThis review highlights key modeling features related to competing mortality risks that should be considered in decision-analytic models assessing the effects of cancer screening. All included models predicted gains in life expectancy with screening, although the magnitude of these gains varied both within and across cancer types. Models that considered competing mortality risks tended to predict smaller lifetime gains from screening interventions. Future studies should prioritize the use of advanced modeling approaches that account for competing mortality risks to improve the accuracy of benefit-harm assessments in cancer screening.HighlightsThis is the first systematic assessment of methodological competing mortality risk features of decision-analytic screening models across 4 cancer types.Models vary greatly regarding predicted gains in life expectancy, natural history assumptions (onset and progression rates), methodological model features, and screening strategies.Models that considered competing mortality risks or adjusted life expectancy for comorbidities predicted smaller lifetime gains for screening compared with no screening.

BackgroundParenthood is a key life goal for many, but infertility affects about 1 in 6 globally. While fertility treatments offer solutions, their high costs limit access. Many health systems provide public funding, yet budget constraints prevent fully funded access, often leaving patients with significant out-of-pocket costs. Policy makers face the challenge of prioritizing individuals for publicly funded treatments, but how to do this remains unclear and underresearched. Worldwide, funding policies vary widely, often adopting controversial access criteria.MethodsWe investigated Belgian population preferences for prioritizing in vitro fertilization (IVF) funding through a discrete-choice experiment with a representative sample of 3,000 Belgians. Attributes included maternal and partner age, infertility cause, civil status, prior biological children, and treatment cost. Using a Bayesian D-optimal design and panel mixed logit model, we assessed criteria relevance. The resulting multiattribute utility function created a priority ranking of couples, which we compared to the ranking under the current Belgian policy, which focuses only on maternal age (<43 y).ResultsAnalysis of 29,670 prioritization choices identified maternal age, infertility cause, and prior biological children as key criteria. Maternal age of 35 y was prioritized highest, age 25 y as high as 40 y, followed by declining priority until 55 y. Biomedical malfunctions were prioritized over same-sex relationships or unhealthy lifestyles, with the latter prioritized lowest. Having no prior biological children was prioritized categorically higher than having 1, 2, or 3 children, all prioritized equally. Preferences were homogeneous across sociodemographic groups.ConclusionsHow to set IVF funding priorities remains a matter of debate. Our study shows that the Belgian population considers multiple criteria beyond maternal age to prioritize couples, calling for further discussion on ethical justifiability and access implications.HighlightsParenthood is a key life goal to many, but about 1 in 6 are affected by infertility. However, in most countries, public funding for fertility treatment is not provided to everyone who could benefit, and hard choices are inevitable.This study used a discrete-choice experiment in a representative sample of the Belgian population to investigate which criteria should be used for prioritization.Results indicated that maternal age, cause of infertility, and the number of prior biological children were the most significant factors in determining public support for IVF funding. Partner age, civil status of the couple, and cost of IVF treatment were not important.People use multiple criteria to set IVF funding priorities, beyond maternal age (the only criterion used in the current Belgian funding policy). Future research should explore the ethical justifiability and practical implications of using cause of infertility and number of prior children as additional

IntroductionThis study introduces a simulation-optimization framework designed to optimize the services of opioid-focused harm reduction vending machines (HRVMs). Given the rising rates of overdose deaths and increased potential for infectious diseases among persons who inject drugs (PWID), HRVMs can become an important harm reduction (HR) strategy by providing essential supplies that mitigate health risks.MethodsWe developed and validated an agent-based simulation-optimization framework to model the impact of HRVM-item allocation on the burden of opioid-related harms, accounting for demand dynamics, item restocking, and regional characteristics. The model evaluated health outcomes-cases of HIV, HCV, and fatal and nonfatal overdose-using disability-adjusted life-years (DALYs). Scenario-based analyses were conducted for different HRVM configurations, considering current legal limits on safer-injection supplies, fentanyl's growing role as a drug of choice, and potential future policy changes.ResultsThe base scenario estimated optimal HRVM capacity allocation at approximately 48.5% fentanyl test strips (FTS), 26.2% naloxone, and 25.3% safer injection kits. However, sensitivity analyses showed significant variations based on fentanyl prevalence and willingness to use FTS. In scenarios of intentional fentanyl use with high FTS utilization, allocation favored FTS, while scenarios with low FTS utilization prioritized naloxone and injection kits. Adding addiction treatment referral services to HRVMs further reduced DALYs and societal costs, primarily by preventing fatal overdoses. Safer injection kits consistently reduced blood-borne infections compared with scenarios without these kits.ConclusionsThe framework could aid in HRVMãrelated service planning and evaluation, highlighting the importance of strategic inventory management and linkages to addiction care for enhanced health outcomes. HRVMs show potential as scalable, cost-effective HR interventions, warranting further research on their impact on service accessibility and health outcomes.HighlightsA novel simulation-optimization framework for designing and evaluating harm reduction vending machines (HRVMs) is presented.Optimal baseline allocation for products in the HRVMs included fentanyl test strips (48.5%), naloxone (26.2%), and safer injection kits (25.3%).Sensitivity analysis indicated optimal allocations vary substantially by local fentanyl prevalence and by individual harm reduction behaviors surrounding the use of fentanyl test strips.HRVM implementation reduces societal costs and disability-adjusted life-years associated with substance use-related harms.

ObjectivesAboriginal and Torres Strait Islander children and adolescents are at higher risk of overweight and obesity, highlighting an inequitable public health concern. The aim of this study was to estimate transition probabilities and validate a model predicting the epidemiologic trajectory of overweight and obesity in Australian Aboriginal and Torres Strait Islander children.MethodsAn individual-level state-transition Markov model was developed to model transitions between healthy weight, overweight, and obesity for Aboriginal and Torres Strait Islander children aged between 2 and 14 y. Age-specific annual transition probabilities were derived from semi-parametric survival analyses using the Longitudinal Study of Indigenous Children. The model used annual cycles over a 12-y time horizon, and the epidemiological predictions of the model were validated using both internal and external data, according to best practice guidelines. The starting age of the model was 2 to 4 y and 4 to 5 y for the internal and external validation, respectively. Aboriginal and Torres Strait Islander children from the Longitudinal Study of Australian Children were used as the external validation cohort.ResultsA total of 1,643 children with 11,514 complete anthropometric measurements were used to estimate transition probabilities. The model predictions showed both good internal and external validity, with most predictions falling within the 95% confidence intervals of measured data. The model was able to reliably capture the epidemiology of overweight and obesity prevalence in early childhood.ConclusionsOur model predictions showed good internal and external validity, ensuring our model is fit for purpose to use to evaluate Aboriginal and Torres Strait Islander-led programs to achieve a healthy weight.HighlightsAboriginal and Torres Strait Islander children experience high rates of overweight and obesity; hence, there is a need for high-quality evidence on both effectiveness and cost-effectiveness of Aboriginal and Torres Strait Islander-led childhood obesity prevention programs to ensure they offer value for money.This is the first study to develop and validate a predictive model using anthropometric data from Aboriginal and Torres Strait Islander children to inform decision making on childhood obesity programs.Our model predictions showed good internal and external validity, ensuring our model is fit for purpose to use to evaluate Aboriginal and Torres Strait Islander-led programs to achieve a healthy weight.The model provides a framework to assist policy makers in identifying when best to intervene in childhood as well as the most effective approaches for maintaining a healthy weight for Aboriginal and Torres Strait Islander children.