Medical Decision Making最新文献

{"title":"Net Monetary Benefit Lines Augmented with Value-of-Information Measures to Present the Results of Economic Evaluations under Uncertainty.","authors":"Reza Yaesoubi, Natalia Kunst","doi":"10.1177/0272989X241262343","DOIUrl":"10.1177/0272989X241262343","url":null,"abstract":"<p><strong>Background: </strong>Methods to present the result of cost-effectiveness analyses under parameter uncertainty include cost-effectiveness planes (CEPs), cost-effectiveness acceptability curves/frontier (CEACs/CEAF), expected loss curves (ELCs), and net monetary benefit (NMB) lines. We describe how NMB lines can be augmented to present NMB values that could be achieved by reducing or resolving parameter uncertainty. We evaluated the ability of these methods to correctly 1) identify the alternative with the highest expected NMB and 2) communicate the magnitude of parameter and decision uncertainty.</p><p><strong>Methods: </strong>We considered 4 hypothetical decision problems representing scenarios with high variance or correlated cost and effect estimates and alternatives with similar cost-effectiveness ratios. We used these decision problems to demonstrate the limitations of existing methods and the potential of augmented NMB lines to resolve these issues.</p><p><strong>Results: </strong>CEPs and CEACs/CEAF could falsely imply the lack of sufficient evidence to identify the optimal option if cost and effect estimates have high variance, are correlated across alternatives, or when alternatives have similar cost-effectiveness ratios. The augmented NMB lines and ELCs can correctly identify the option with the highest expected NMB and communicate the potential benefit of resolving uncertainties. Like ELCs, the augmented NMB lines provide information about the value of resolving parameter uncertainties, but augmented NMB lines may be easier to interpret for decision makers.</p><p><strong>Conclusions: </strong>Our analysis supports recommending the augment NMB lines as an important method to present the results of economic evaluation studies under parameter uncertainty.</p><p><strong>Highlights: </strong>The results of cost-effectiveness analyses (CEAs) when the cost and effect estimates of alternatives are uncertain are commonly presented using cost-effectiveness planes (CEPs), cost-effectiveness acceptability curves/frontier (CEACs/CEAF), and expected loss curves (ELCs).Although currently not often used, net monetary benefit (NMB) lines could present the results of cost-effectiveness to identify the alternative with the highest expected NMB values given the current level of uncertainty. Furthermore, NMB lines can be augmented to 1) show metrics of value of information, which measure the value of additional research to reduce or eliminate the decision uncertainty, and 2) display the confidence intervals along the NMB lines to ensure that NMB values are estimated accurately using a sufficiently large number of parameter samples.Using several decision problems, we demonstrate the limitation of existing methods to present the results of CEAs under parameter uncertainty and how augmented NMB lines could resolve these issues.Our analysis supports recommending augmented NMB lines as an important method to present the results of CEA under uncertain","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"770-786"},"PeriodicalIF":3.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141762145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accurate EVSI Estimation for Nonlinear Models Using the Gaussian Approximation Method.","authors":"Linke Li, Hawre Jalal, Anna Heath","doi":"10.1177/0272989X241264287","DOIUrl":"10.1177/0272989X241264287","url":null,"abstract":"<p><strong>Background: </strong>The expected value of sample information (EVSI) measures the expected benefits that could be obtained by collecting additional data. Estimating EVSI using the traditional nested Monte Carlo method is computationally expensive, but the recently developed Gaussian approximation (GA) approach can efficiently estimate EVSI across different sample sizes. However, the conventional GA may result in biased EVSI estimates if the decision models are highly nonlinear. This bias may lead to suboptimal study designs when GA is used to optimize the value of different studies. Therefore, we extend the conventional GA approach to improve its performance for nonlinear decision models.</p><p><strong>Methods: </strong>Our method provides accurate EVSI estimates by approximating the conditional expectation of the benefit based on 2 steps. First, a Taylor series approximation is applied to estimate the conditional expectation of the benefit as a function of the conditional moments of the parameters of interest using a spline, which is fitted to the samples of the parameters and the corresponding benefits. Next, the conditional moments of parameters are approximated by the conventional GA and Fisher information. The proposed approach is applied to several data collection exercises involving non-Gaussian parameters and nonlinear decision models. Its performance is compared with the nested Monte Carlo method, the conventional GA approach, and the nonparametric regression-based method for EVSI calculation.</p><p><strong>Results: </strong>The proposed approach provides accurate EVSI estimates across different sample sizes when the parameters of interest are non-Gaussian and the decision models are nonlinear. The computational cost of the proposed method is similar to that of other novel methods.</p><p><strong>Conclusions: </strong>The proposed approach can estimate EVSI across sample sizes accurately and efficiently, which may support researchers in determining an economically optimal study design using EVSI.</p><p><strong>Highlights: </strong>The Gaussian approximation method efficiently estimates the expected value of sample information (EVSI) for clinical trials with varying sample sizes, but it may introduce bias when health economic models have a nonlinear structure.We introduce the spline-based Taylor series approximation method and combine it with the original Gaussian approximation to correct the nonlinearity-induced bias in EVSI estimation.Our approach can provide more precise EVSI estimates for complex decision models without sacrificing computational efficiency, which can enhance the resource allocation strategies from the cost-effective perspective.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"787-801"},"PeriodicalIF":3.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11492544/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141856992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning Methods to Estimate Individualized Treatment Effects for Use in Health Technology Assessment.","authors":"Yingying Zhang, Noemi Kreif, Vijay S Gc, Andrea Manca","doi":"10.1177/0272989X241263356","DOIUrl":"10.1177/0272989X241263356","url":null,"abstract":"<p><strong>Background: </strong>Recent developments in causal inference and machine learning (ML) allow for the estimation of individualized treatment effects (ITEs), which reveal whether treatment effectiveness varies according to patients' observed covariates. ITEs can be used to stratify health policy decisions according to individual characteristics and potentially achieve greater population health. Little is known about the appropriateness of available ML methods for use in health technology assessment.</p><p><strong>Methods: </strong>In this scoping review, we evaluate ML methods available for estimating ITEs, aiming to help practitioners assess their suitability in health technology assessment. We present a taxonomy of ML approaches, categorized by key challenges in health technology assessment using observational data, including handling time-varying confounding and time-to event data and quantifying uncertainty.</p><p><strong>Results: </strong>We found a wide range of algorithms for simpler settings with baseline confounding and continuous or binary outcomes. Not many ML algorithms can handle time-varying or unobserved confounding, and at the time of writing, no ML algorithm was capable of estimating ITEs for time-to-event outcomes while accounting for time-varying confounding. Many of the ML algorithms that estimate ITEs in longitudinal settings do not formally quantify uncertainty around the point estimates.</p><p><strong>Limitations: </strong>This scoping review may not cover all relevant ML methods and algorithms as they are continuously evolving.</p><p><strong>Conclusions: </strong>Existing ML methods available for ITE estimation are limited in handling important challenges posed by observational data when used for cost-effectiveness analysis, such as time-to-event outcomes, time-varying and hidden confounding, or the need to estimate sampling uncertainty around the estimates.</p><p><strong>Implications: </strong>ML methods are promising but need further development before they can be used to estimate ITEs for health technology assessments.</p><p><strong>Highlights: </strong>Estimating individualized treatment effects (ITEs) using observational data and machine learning (ML) can support personalized treatment advice and help deliver more customized information on the effectiveness and cost-effectiveness of health technologies.ML methods for ITE estimation are mostly designed for handling confounding at baseline but not time-varying or unobserved confounding. The few models that account for time-varying confounding are designed for continuous or binary outcomes, not time-to-event outcomes.Not all ML methods for estimating ITEs can quantify the uncertainty of their predictions.Future work on developing ML that addresses the concerns summarized in this review is needed before these methods can be widely used in clinical and health technology assessment-like decision making.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"756-769"},"PeriodicalIF":3.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11505399/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141762143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Methods to Quantify the Importance of Parameters for Model Updating and Distributional Adaptation.","authors":"David Glynn, Susan Griffin, Nils Gutacker, Simon Walker","doi":"10.1177/0272989X241262037","DOIUrl":"10.1177/0272989X241262037","url":null,"abstract":"<p><strong>Purpose: </strong>Decision models are time-consuming to develop; therefore, adapting previously developed models for new purposes may be advantageous. We provide methods to prioritize efforts to 1) update parameter values in existing models and 2) adapt existing models for distributional cost-effectiveness analysis (DCEA).</p><p><strong>Methods: </strong>Methods exist to assess the influence of different input parameters on the results of a decision models, including value of information (VOI) and 1-way sensitivity analysis (OWSA). We apply 1) VOI to prioritize searches for additional information to update parameter values and 2) OWSA to prioritize searches for parameters that may vary by socioeconomic characteristics. We highlight the assumptions required and propose metrics that quantify the extent to which parameters in a model have been updated or adapted. We provide R code to quickly carry out the analysis given inputs from a probabilistic sensitivity analysis (PSA) and demonstrate our methods using an oncology case study.</p><p><strong>Results: </strong>In our case study, updating 2 of 21 probabilistic model parameters addressed 71.5% of the total VOI and updating 3 addressed approximately 100% of the uncertainty. Our proposed approach suggests that these are the 3 parameters that should be prioritized. For model adaptation for DCEA, 46.3% of the total OWSA variation came from a single parameter, while the top 10 input parameters were found to account for more than 95% of the total variation, suggesting efforts should be aimed toward these.</p><p><strong>Conclusions: </strong>These methods offer a systematic approach to guide research efforts in updating models with new data or adapting models to undertake DCEA. The case study demonstrated only very small gains from updating more than 3 parameters or adapting more than 10 parameters.</p><p><strong>Highlights: </strong>It can require considerable analyst time to search for evidence to update a model or to adapt a model to take account of equity concerns.In this article, we provide a quantitative method to prioritze parameters to 1) update existing models to reflect potential new evidence and 2) adapt existing models to estimate distributional outcomes.We define metrics that quantify the extent to which the parameters in a model have been updated or adapted.We provide R code that can quickly rank parameter importance and calculate quality metrics using only the results of a standard probabilistic sensitivity analysis.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"802-810"},"PeriodicalIF":3.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11490092/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141762144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Natural Frequencies Improve Public Understanding of Medical Test Results: An Experimental Study on Various Bayesian Inference Tasks with Multiple Scoring Methods and Non-Bayesian Reasoning Strategies","authors":"Soyun Kim","doi":"10.1177/0272989x241275191","DOIUrl":"https://doi.org/10.1177/0272989x241275191","url":null,"abstract":"BackgroundIt is well established that the natural frequencies (NF) format is cognitively more beneficial for Bayesian inference than the conditional probabilities (CP) format. However, empirical studies have suggested that the NF facilitation effect might be limited to specific groups of individuals. Unlike previous studies that focused on a limited number of Bayesian inference problems evaluated by a single scoring method, it was essential to examine multiple Bayesian problems using various scoring metrics. This study also explored the impact of numeracy on Bayesian inference and assessed non-Bayesian cognitive strategies using the numerical information in problem solving.MethodsIn a Web-based experimental survey, 175 South Korean adults were randomly assigned to 1 of 2 format groups (NF v. CP). After completing numeracy scales, participants were asked to estimate 4 Bayesian inference problems and document the numerical information used in their problem-solving process. Four scoring methods—strict rounding, loose rounding, absolute deviation, and 50-Split—were used to evaluate participants’ estimations.ResultsThe NF format generally outperformed the CP format across all problems, except in a chorionic villus sampling test problem when evaluated using the 50-Split method. In addition, numeracy levels significantly influenced Bayesian inference; participants with higher numeracy demonstrated better performance. In addition, participants used various non-Bayesian strategies influenced by the format and the nature of the problems.ConclusionsThe NF facilitation effect was consistently observed across multiple Bayesian problems and scoring methods. Individuals with higher numeracy levels benefited more from the NF format. The use of various non-Bayesian strategies varied with the formats and nature of specific tasks.HighlightsThe natural frequencies (NF) format is known to foster understanding of medical test results compared with the conditional probabilities (CP) format, but some studies have reported that this benefit is either nonexistent or limited to specific groups. This study aims to replicate previous empirical studies using various Bayesian problems using multiple scoring methods. The NF format fosters understanding of medical test results across all Bayesian problems by all scoring methods, except in the CVS problem when using a 50-Split scoring method. Participants with high numeracy perform better Bayesian inference than those with lower numeracy. Particularly, higher numerates benefit more in the NF format than lower numerates do. In addition, the public tend to use various non-Bayesian reasoning strategies depending on the format and the nature of the tasks.","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":"119 1","pages":"272989X241275191"},"PeriodicalIF":3.6,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142267129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How Much Information Is Too Much? An Experimental Examination of How Information Disclosures May Unintentionally Encourage the Withholding of Health Information","authors":"Helen Colby, Deidre Popovich, Tony Stovall","doi":"10.1177/0272989x241275645","DOIUrl":"https://doi.org/10.1177/0272989x241275645","url":null,"abstract":"IntroductionInformation disclosures are used in medicine to provide patients with relevant information. This research examines whether patients are less likely to discuss medical conditions with their physicians after seeing an insurance information disclosure.MethodsThree experimental studies with nonprobability online samples (n<jats:sub>total</jats:sub> = 875 US adult participants) examined the impact of information disclosures on patients’ likelihood of disclosing symptoms to providers, using new symptoms and preexisting chronic conditions. The effects of insurance disclosures were also compared to those of pharmaceutical discount disclosures.ResultsThese studies demonstrate that information disclosures can result in unintended consequences for patients and providers. Results showed that information disclosures about insurance claims significantly negatively affected willingness to discuss health information with providers. This effect was consistent for both new health concerns, b = −0.661, P &lt; 0.001 (study 1, n = 250) and b = −0.893, P &lt; 0.001 (study 3, n = 375), as well as chronic conditions, b = −1.175, P &lt; .001 (study 2, n = 250); all studies were conducted in January 2023. Information provided to patients about pharmaceutical savings did not similarly affect willingness to discuss symptoms with providers.LimitationsThese were experimental studies with hypothetical scenarios. Future research is needed to understand how patients react to information disclosures in a physician’s office. Future research is also needed to examine the role of specific wording and tone used in information disclosures.ConclusionsPrior research has shown that patients prefer more information and to be involved in their medical decisions; however, these studies demonstrate that some information disclosures can discourage full communication between patients and physicians.ImplicationsThis research has important implications for the potential consequences of information disclosures in health care settings. Information disclosures should be presented in a way that will not discourage candid discussions of patient symptoms.HighlightsThis research found that information disclosures about insurance claims can negatively affect patient willingness to discuss health information with providers. Information disclosures may sometimes fall short of their intended purpose of aiding patient decisions with the goal of improved well-being. When information disclosures are focused on warning about potential new costs, patients may feel uncomfortable discussing new symptoms with their providers. Findings suggest patients may often be more concerned with costs than with addressing their ongoing health problems.","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":"20 1","pages":"272989X241275645"},"PeriodicalIF":3.6,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142267128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"General Population Mortality Adjustment in Survival Extrapolation of Cancer Trials: Exploring Plausibility and Implications for Cost-Effectiveness Analyses in HER2-Positive Breast Cancer in Sweden.","authors":"Kun Kim,Michael Sweeting,Nils Wilking,Linus Jönsson","doi":"10.1177/0272989x241275969","DOIUrl":"https://doi.org/10.1177/0272989x241275969","url":null,"abstract":"BACKGROUNDIn economic evaluations of novel therapies, assessing lifetime effects based on trial data often necessitates survival extrapolation, with the choice of model affecting outcomes. The aim of this study was to assess accuracy and variability between alternative approaches to survival extrapolation.METHODSData on HER2-positive breast cancer patients from the Swedish National Breast Cancer Register were used to fit standard parametric distribution (SPD) models and excess hazard (EH) models adjusting the survival projections based on general population mortality (GPM). Models were fitted using 6-y data for stage I and II, 4-y data for stage III, and 2-y data for stage IV cancer reflecting an early data cutoff while maintaining sufficient events for comparison of model estimates with actual long-term outcomes. We compared model projections of 15-y survival and restricted mean survival time (RMST) to 15-y registry data and explored the variability between models in extrapolations of long-term survival.RESULTSAmong 11,224 patients compared with the observed registry 15-y RMST estimates across the disease stages, EH cure models provided the most accurate estimates in patients with stage I to III cancer, whereas EH models without cure most closely matched survival in patients with stage IV cancer, in which cure assumption was less plausible. The Akaike information criterion-averaged model projections varied as follows: -8.2% to +5.3% for SPD models, -4.9% to +5.2% for the EH model without a cure assumption, and -19.3% to -0.2% for the EH model with a cure assumption. EH models significantly reduced between-model variance in the predicted RMSTs over a 50-y time horizon compared with SPD models.CONCLUSIONSEH models may be considered as alternatives to SPD models to produce more accurate and plausible survival extrapolation that accounts for general population mortality.HIGHLIGHTSExcess hazard (EH) methods have been suggested as an approach to incorporate background mortality rates in economic evaluation using survival extrapolation.We highlight that EH models with or without a cure assumption can produce more accurate survival projections and significantly reduce between-model variability in comparison with standard parametric distribution models across cancer stages.EH models may be a preferred modeling method to reduce model uncertainty in health economic modeling since models that would otherwise have produced implausible extrapolations are constrained by the EH framework.Reduced uncertainty in economic evaluations will enhance the application of evidence-based health care decision making.","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":"4 1","pages":"272989X241275969"},"PeriodicalIF":3.6,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142200923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptation and Validation of the Psychological Consequences of Screening Questionnaire (PCQ) for Cognitive Screening in Primary Care.","authors":"Rebecca M Lovett,Sarah Filec,Jeimmy Hurtado,Mary Kwasny,Alissa Sideman,Stephen D Persell,Katherine Possin,Michael Wolf","doi":"10.1177/0272989x241275676","DOIUrl":"https://doi.org/10.1177/0272989x241275676","url":null,"abstract":"BACKGROUNDContext-specific measures with adequate external validity are needed to appropriately determine psychosocial effects related to screening for cognitive impairment.METHODSTwo-hundred adults aged ≥65 y recently completing routine, standardized cognitive screening as part of their Medicare annual wellness visit were administered an adapted version of the Psychological Consequences of Screening Questionnaire (PCQ), composed of negative (PCQ-Neg) and positive (PCQ-Pos) scales. Measure distribution, acceptability, internal consistency, factor structure, and external validity (construct, discriminative, criterion) were analyzed.RESULTSParticipants had a mean age of 73.3 y and were primarily female and socioeconomically advantaged. Most had a normal cognitive screening result (99.5%, n = 199). Overall PCQ scores were low (PCQ-Neg: x¯= 1.27, possible range 0-36; PCQ-Pos: x¯ = 7.63, possible range 0-30). Both scales demonstrated floor effects. Acceptability was satisfactory, although the PCQ-Pos had slightly more item missingness. Both scales had Cronbach alphas >0.80 and a single-factor structure. Spearman correlations between the PCQ-Neg with general measures of psychological distress (Impacts of Events Scale-Revised, Perceived Stress Scale, Kessler Distress Scale) ranged from 0.26 to 0.37 (P's < 0.001); the correlation with the World Health Organization-Five Well-Being Index was -0.19 (P < 0.01). The PCQ-Neg discriminated between those with and without a self-reported subjective cognitive complaint (x¯ = 2.73 v. 0.89, P < 0.001) and was associated with medical visit satisfaction (r = -0.24, P < 0.001) on the Patient Satisfaction Questionnaire. The PCQ-Pos predicted self-reported willingness to engage in future screening (x¯ = 8.00 v. 3.00, P = 0.03).CONCLUSIONSThe adapted PCQ-Neg is an overall valid measure of negative psychological consequences of cognitive screening; findings for the PCQ-Pos were more variable. Future studies should address measure performance among diverse samples and those with abnormal screening results.HIGHLIGHTSThe PCQ scale is an overall valid measure of psychological dysfunction related to cognitive screening in older adults receiving normal screen results.PCQ scale performance should be further validated in diverse populations and those with abnormal cognitive screening results.The adapted PCQ may be useful to both health research and policy stakeholders seeking improved assessment of psychological impacts of cognitive screening.","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":"28 1","pages":"272989X241275676"},"PeriodicalIF":3.6,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142200972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Medical Homo Ignorans, Shared Decision Making, and Affective Paternalism: Balancing Emotion and Analysis in Health Care Choices.","authors":"Gustav Tinghög, Emil Persson, Daniel Västfjäll","doi":"10.1177/0272989X241263001","DOIUrl":"10.1177/0272989X241263001","url":null,"abstract":"","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"611-613"},"PeriodicalIF":3.1,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141447408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using Separate Single-Outcome Risk Presentations Instead of Integrated Multioutcome Formats Improves Comprehension in Discrete Choice Experiments.","authors":"Matthew J Wallace, E Hope Weissler, Jui-Chen Yang, Laura Brotzman, Matthew A Corriere, Eric A Secemsky, Jessie Sutphin, F Reed Johnson, Juan Marcos Gonzalez, Michelle E Tarver, Anindita Saha, Allen L Chen, David J Gebben, Misti Malone, Andrew Farb, Olufemi Babalola, Eva M Rorer, Brian J Zikmund-Fisher, Shelby D Reed","doi":"10.1177/0272989X241258466","DOIUrl":"10.1177/0272989X241258466","url":null,"abstract":"<p><strong>Introduction: </strong>Despite decades of research on risk-communication approaches, questions remain about the optimal methods for conveying risks for different outcomes across multiple time points, which can be necessary in applications such as discrete choice experiments (DCEs). We sought to compare the effects of 3 design factors: 1) separated versus integrated presentations of the risks for different outcomes, 2) use or omission of icon arrays, and 3) vertical versus horizontal orientation of the time dimension.</p><p><strong>Methods: </strong>We conducted a randomized study among a demographically diverse sample of 2,242 US adults recruited from an online panel (mean age 59.8 y, <i>s</i> = 10.4 y; 21.9% African American) that compared risk-communication approaches that varied in the 3 factors noted above. The primary outcome was the number of correct responses to 12 multiple-choice questions asking survey respondents to identify specific numbers, contrast options to recognize dominance (larger v. smaller risks), and compute differences. We used linear regression to test the effects of the 3 design factors, controlling for health literacy, graph literacy, and numeracy. We also measured choice consistency in a subsequent DCE choice module.</p><p><strong>Results: </strong>Mean comprehension varied significantly across versions (<i>P</i> < 0.001), with higher comprehension in the 3 versions that provided separated risk information for each risk. In the multivariable regression, separated risk presentation was associated with 0.58 more correct responses (<i>P</i> < 0.001; 95% confidence interval: 0.39, 0.77) compared with integrated risk information. Neither providing icon arrays nor using vertical versus horizontal time formats affected comprehension rates, although participant understanding did correlate with DCE choice consistency.</p><p><strong>Conclusions: </strong>In presentations of multiple risks over multiple time points, presenting risk information separately for each health outcome appears to increase understanding.</p><p><strong>Highlights: </strong>When conveying information about risks of different outcomes at multiple time points, separate presentations of single-outcome risks resulted in higher comprehension than presentations that combined risk information for different outcomes.We also observed benefits of presenting single-outcome risks separately among respondents with lower numeracy and graph literacy.Study participants who scored higher on risk understanding were more internally consistent in their responses to a discrete choice experiment.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"649-660"},"PeriodicalIF":3.1,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141433191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}