Medical Decision Making最新文献

{"title":"How Should Doctors Frame the Risk of a Vaccine's Adverse Side Effects? It Depends on How Trustworthy They Are.","authors":"Marie Juanchich, Miroslav Sirota, Dawn Liu Holford","doi":"10.1177/0272989X231197646","DOIUrl":"10.1177/0272989X231197646","url":null,"abstract":"<p><strong>Background: </strong>How health workers frame their communication about vaccines' probability of adverse side effects could play an important role in people's intentions to be vaccinated (e.g., positive frame: side effects are <i>unlikely</i> v. negative frame: there is <i>a chance</i> of side effects). Based on the pragmatic account of framing as implicit advice, we expected that participants would report greater vaccination intentions when a trustworthy physician framed the risks positively (v. negatively), but we expected this effect would be reduced or reversed when the physician was untrustworthy.</p><p><strong>Design: </strong>In 4 online experiments (<i>n</i> = 191, snowball sampling and <i>n</i> = 453, 451, and 464 UK residents via Prolific; M_age≈ 34 y, 70% women, 84% White British), we manipulated the trustworthiness of a physician and how they framed the risk of adverse side effects in a scenario (i.e., a chance v. unlikely adverse side effects). Participants reported their vaccination intention, their level of distrust in health care systems, and COVID-19 conspiracy beliefs.</p><p><strong>Results: </strong>Physicians who were trustworthy (v. untrustworthy) consistently led to an increase in vaccination intention, but the way they described adverse side effects mattered too. A positive framing of the risks given by a trustworthy physician consistently led to increased vaccination intention relative to a negative framing, but framing had no effect or the opposite effect when given by an untrustworthy physician. The exception to this trend occurred in unvaccinated individuals in experiment 3, following serious concerns about one of the COVID vaccines. In that study, unvaccinated participants responded more favorably to the negative framing of the trustworthy physician.</p><p><strong>Conclusions: </strong>Trusted sources should use positive framing to foster vaccination acceptance. However, in a situation of heightened fears, a negative framing-attracting more attention to the risks-might be more effective.</p><p><strong>Highlights: </strong>How health workers frame their communication about a vaccine's probability of adverse side effects plays an important role in people's intentions to be vaccinated.In 4 experiments, we manipulated the trustworthiness of a physician and how the physician framed the risk of adverse side effects of a COVID vaccine.Positive framing given by a trustworthy physician promoted vaccination intention but had null effect or did backfire when given by an untrustworthy physician.The effect occurred over and above participants' attitude toward the health care system, risk perceptions, and beliefs in COVID misinformation.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10625727/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41122675","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":"Valuing the Dental Caries Utility Index in Australia.","authors":"Ruvini M Hettiarachchi, Sanjeewa Kularatna, Joshua Byrnes, Brendan Mulhern, Gang Chen, Paul A Scuffham","doi":"10.1177/0272989X231197149","DOIUrl":"10.1177/0272989X231197149","url":null,"abstract":"<p><strong>Introduction: </strong>The Dental Caries Utility Index (DCUI) is a new oral health-specific health state classification system for adolescents, consisting of 5 domains: pain/discomfort, difficulty eating food/drinking, worried, ability to participate in activities, and appearance. Each domain has 4 response levels. This study aims to generate an Australian-specific utility algorithm for the DCUI.</p><p><strong>Methods: </strong>An online survey was conducted using a representative sample of the adult Australian general population. The discrete choice experiment (DCE) was used to elicit the preferences on 5 domains. Then, the latent utilities were anchored onto the full health-dead scale using the visual analogue scale (VAS). DCE data were modeled using conditional logit, and 2 anchoring procedures were considered: anchor based on the worst health state and a mapping approach. The optimal anchoring procedure was selected based on the model parsimony and the mean absolute error (MAE).</p><p><strong>Results: </strong>A total of 995 adults from the Australian general population completed the survey. The conditional logit estimates on 5 dimensions and levels were monotonic and statistically significant, except for the second level of the \"worried\" and \"appearance\" domains. The mapping approach was selected based on a smaller MAE between the 2 anchoring procedures. The Australian-specific tariff of DCUI ranges from 0.1681 to 1.</p><p><strong>Conclusion: </strong>This study developed a utility algorithm for the DCUI. This value set will facilitate utility value calculations from the participants' responses for DCUI in economic evaluations of dental caries interventions targeted for adolescents.</p><p><strong>Highlights: </strong>Preference-based quality-of-life measures (PBMs), which consist of a health state classification system and a set of utility values (a scoring algorithm), are used to generate utility weights for economic evaluations.This study is the first to develop an Australian utility value set for the Dental Caries Utility Index (DCUI), a new oral health-specific classification system for adolescents.The availability of a utility value set will enable using DCUI in economic evaluations of oral health interventions targeted for adolescents and may ultimately lead to more effective and efficient planning of oral health care services.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10625724/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41157652","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":"A Prospective Cohort Study of Medical Decision-Making Roles and Their Associations with Patient Characteristics and Patient-Reported Outcomes among Patients with Heart Failure.","authors":"Semra Ozdemir, Jia Jia Lee, Khung Keong Yeo, Kheng Leng David Sim, Eric Andrew Finkelstein, Chetna Malhotra","doi":"10.1177/0272989X231201609","DOIUrl":"10.1177/0272989X231201609","url":null,"abstract":"<p><strong>Objective: </strong>Among patients with heart failure (HF), we examined 1) the evolution of patient involvement in decision making over 2 y, 2) the association of patient characteristics with decision-making roles, and 3) the association of decision-making roles with distress, spiritual well-being, and quality of physician communication.</p><p><strong>Methods: </strong>We administered the survey every 4 mo over 24 mo to patients with New York Heart Association class 3/4 symptoms recruited from inpatient clinics. The decision-making roles were categorized as no patient involvement, physician/family-led, joint (with family and/or physicians), patient-led, or patient-alone decision making. The associations between patient characteristics and decision-making roles were assessed using a mixed-effects ordered logistic regression, whereas those between patient outcomes and decision-making roles were investigated using mixed-effects linear regressions.</p><p><strong>Results: </strong>Of the 557 patients invited, 251 participated in the study. The most common roles in decision making at baseline assessment were \"no involvement\" (27.53%) and \"patient-alone decision making\" (25.10%). The proportions of different decision-making roles did not change over 2 y (<i>P</i> = 0.37). Older age (odds ratio [OR] = 0.97; <i>P</i> = 0.003) and being married (OR = 0.63; <i>P</i> = 0.035) were associated with lower involvement in decision making. Chinese ethnicity (OR = 1.91; <i>P</i> = 0.003), higher education (OR = 1.87; <i>P</i> = 0.003), awareness of terminal condition (OR = 2.00; <i>P</i> < 0.001), and adequate self-care confidence (OR = 1.74; <i>P</i> < 0.001) were associated with greater involvement. Compared with no patient involvement, joint (β = -0.58; <i>P</i> = 0.026) and patient-led (β = -0.59; <i>P</i> = 0.014) decision making were associated with lower distress, while family/physician-led (β = 4.37; <i>P</i> = 0.001), joint (β = 3.86; <i>P</i> < 0.001), patient-led (β = 3.46; <i>P</i> < 0.001), and patient-alone (β = 3.99; <i>P</i> < 0.001) decision making were associated with better spiritual well-being.</p><p><strong>Conclusion: </strong>A substantial proportion of patients was not involved in decision making. Patients should be encouraged to participate in decision making since it is associated with lower distress and better spiritual well-being.</p><p><strong>Highlights: </strong>The level of involvement in medical decision making did not change over time among patients with heart failure. A substantial proportion of patients were not involved in decision making throughout the 24-mo study period.Patients' involvement in decision making varied by age, ethnicity, education level, marital status, awareness of the terminal condition, and confidence in self-care.Compared with no patient involvement in decision making, joint and patient-led decision making were associated with lower distress, and any level of patient involvement in decision making","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41164523","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":"Economic Evaluation of Medically Assisted Reproduction: A Methodological Systematic Review.","authors":"Astrid Van Muylder, Thomas D'Hooghe, Jeroen Luyten","doi":"10.1177/0272989X231188129","DOIUrl":"10.1177/0272989X231188129","url":null,"abstract":"<p><strong>Background: </strong>Medically assisted reproduction (MAR) is a challenging application area for health economic evaluations, entailing a broad range of costs and outcomes, stretching out long-term and accruing to several parties.</p><p><strong>Purpose: </strong>To systematically review which costs and outcomes are included in published economic evaluations of MAR and to compare these with health technology assessment (HTA) prescriptions about which cost and outcomes should be considered for different evaluation objectives.</p><p><strong>Data sources: </strong>HTA guidelines and systematic searches of PubMed Central, Embase, WOS CC, CINAHL, Cochrane (CENTRAL), HTA, and NHS EED.</p><p><strong>Study selection: </strong>All economic evaluations of MAR published from 2010 to 2022.</p><p><strong>Data extraction: </strong>A predetermined data collection form summarized study characteristics. Essential costs and outcomes of MAR were listed based on HTA and treatment guidelines for different evaluation objectives. For each study, included costs and outcomes were reviewed.</p><p><strong>Data synthesis: </strong>The review identified 93 cost-effectiveness estimates, of which 57% were expressed as cost-per-(healthy)-live-birth, 19% as cost-per-pregnancy, and 47% adopted a clinic perspective. Few adopted societal perspectives and only 2% used quality-adjusted life-years (QALYs). Broader evaluations omitted various relevant costs and outcomes related to MAR. There are several cost and outcome categories for which available HTA guidelines do not provide conclusive directions regarding inclusion or exclusion.</p><p><strong>Limitations: </strong>Studies published before 2010 and of interventions not clearly labeled as MAR were excluded. We focus on methods rather than which MAR treatments are cost-effective.</p><p><strong>Conclusions: </strong>Economic evaluations of MAR typically calculate a short-term cost-per-live-birth from a clinic perspective. Broader analyses, using cost-per-QALY or BCRs from societal perspectives, considering the full scope of reproduction-related costs and outcomes, are scarce and often incomplete. We provide a summary of costs and outcomes for future research guidance and identify areas requiring HTA methodological development.</p><p><strong>Highlights: </strong>The cost-effectiveness of MAR procedures can be exceptionally complex to estimate as there is a broad range of costs and outcomes involved, in principle stretching out over multiple generations and over many stakeholders.We list 21 key areas of costs and outcomes of MAR. Which of these needs to be accounted for alters for different evaluation objectives (determined by the type of economic evaluation, time horizon considered, and perspective).Published studies mostly investigate cost-effectiveness in the very short-term, from a clinic perspective, expressed as cost-per-live-birth. There is a lack of comprehensive economic evaluations that adopt a broader perspective w","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10064969","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":"Comparative Assessment of Reimbursement Recommendations by NICE and HAS for Oncology New Medicines Indicated for the Treatment of Solid Tumors from 2015 to 2021.","authors":"Jean-Baptiste Trouiller, Philippe Laramée","doi":"10.1177/0272989X231188073","DOIUrl":"10.1177/0272989X231188073","url":null,"abstract":"<p><strong>Objectives: </strong>This study aimed to compare reimbursement recommendations by the British National Institute for Health and Care Excellence (NICE) and the French National Authority for Health (Haute Autorité de Santé; HAS) for oncology new medicines indicated for the treatment of solid tumors.</p><p><strong>Methods: </strong>Public assessment reports published on NICE and HAS Web sites between January 1, 2015, and December 31, 2021, describing appraisals for public reimbursement for oncology new medicines indicated for the treatment of solid tumors were searched and systematically reviewed. Biosimilars and generic drugs were excluded from the analysis.</p><p><strong>Results: </strong>Overall, 119 public assessment reports published by NICE and 134 by HAS were identified, with 101 interventions assessed by both agencies. Of 101, 38 (38%) interventions received similar recommendations, 38 (38%) were recommended for different populations, and 25 (25%) were endorsed by one agency but rejected by the other. The main reason NICE would not recommend a drug was due to lack of cost-effectiveness, whereas HAS would primarily reject a drug on the grounds of insufficient clinical evidence.</p><p><strong>Conclusion: </strong>The major divergence between agencies is the key criterion used for reimbursement recommendations. NICE mainly considers cost-effectiveness, whereas HAS primarily focuses on the clinical value of interventions. This contributes to the variability in reimbursement recommendations and leads to differential access to oncology new medicines indicated for the treatment of solid tumors among patients in France, and England and Wales.</p><p><strong>Highlights: </strong>Both the National Institute for Health and Care Excellence (NICE) and the National Authority for Health (Haute Autorité de Santé; HAS) have established formal health technology assessment (HTA) processes and offer universal public health care coverage. However, both agencies diverge in the weight given to different elements of evidence during HTA evaluations. NICE uses cost-effectiveness as key criterion for recommendations on drug reimbursement, while HAS mostly limits its assessment to clinical value.For oncology new medicines indicated for treating solid tumors between 2015 and 2021, recommendations differed 62% of the time between NICE and HAS, primarily due to the distinct key decision-making criteria each HTA agency uses.For 4 interventions not endorsed by NICE, HAS saw these drugs as providing a substantial enhancement in clinical value over existing treatments, potentially providing an edge in price negotiations. Conversely, NICE deemed these drugs as not delivering adequate value for money in comparison with current therapies.A key difference between the 2 agencies is HAS's insistence on methodological rigor in efficacy results, compared with NICE's more flexible approach, considering descriptive efficacy results in cost-effectiveness modeling.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10209401","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":"Persistent Challenges to a Single Diagnosis.","authors":"Donald A Redelmeier, Eldar Shafir","doi":"10.1177/0272989X231197828","DOIUrl":"10.1177/0272989X231197828","url":null,"abstract":"","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10233905","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 a Sample Size Calculation Framework for Clinical Prediction Models When Developing and Selecting Mapping Algorithms Based on Linear Regression.","authors":"Yasuhiro Hagiwara","doi":"10.1177/0272989X231188134","DOIUrl":"10.1177/0272989X231188134","url":null,"abstract":"<p><strong>Purpose: </strong>To propose using a framework for calculating the sample size for clinical prediction models when developing and selecting mapping algorithms from a health-related quality-of-life (HRQOL) measure onto the score of a preference-based measure (PBM) using linear regression.</p><p><strong>Methods: </strong>The framework was summarized for health economics researchers. Mapping studies that mapped the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 onto the EQ-5D-3L index using linear regression were evaluated in terms of sample size. The required sample size for each study was calculated using 4 criteria: global shrinkage factor ≥ 0.9, difference between the apparent and adjusted <i>R</i>² ≤ 0.05, multiplicative margin of error in the estimated residual standard deviation ≤ 1.1, and absolute margin of error in the estimated model intercept ≤ 0.025.</p><p><strong>Results: </strong>Ten mapping studies were identified. The information required to calculate the sample size was successfully extracted from previous mapping studies. Four of 10 mapping studies did not have sufficient sample sizes.</p><p><strong>Limitations: </strong>Further extension of this framework to other regression approaches used in mapping studies is necessary.</p><p><strong>Conclusions: </strong>The sample size should be considered when developing and selecting a mapping algorithm based on linear regression.</p><p><strong>Highlights: </strong>No recommendation or guidance is available for the sample size to develop and select a mapping algorithm from a health-related quality-of-life measure onto the score of a preference-based measure.This research proposes using a framework for calculating the sample size for clinical prediction models in sample size consideration for mapping algorithms using linear regression.A survey showed that the information required to calculate the sample size could be successfully extracted from previous mapping studies and that 4 of 10 mapping studies did not have sufficient sample sizes.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9840860","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":"Development and Validation of the US Diabetes, Obesity, Cardiovascular Disease Microsimulation (DOC-M) Model: Health Disparity and Economic Impact Model.","authors":"David D Kim, Lu Wang, Brianna N Lauren, Junxiu Liu, Matti Marklund, Yujin Lee, Renata Micha, Dariush Mozaffarian, John B Wong","doi":"10.1177/0272989X231196916","DOIUrl":"10.1177/0272989X231196916","url":null,"abstract":"<p><strong>Background: </strong>Few simulation models have incorporated the interplay of diabetes, obesity, and cardiovascular disease (CVD); their upstream lifestyle and biological risk factors; and their downstream effects on health disparities and economic consequences.</p><p><strong>Methods: </strong>We developed and validated a US Diabetes, Obesity, Cardiovascular Disease Microsimulation (DOC-M) model that incorporates demographic, clinical, and lifestyle risk factors to jointly predict overall and racial-ethnic groups-specific obesity, diabetes, CVD, and cause-specific mortality for the US adult population aged 40 to 79 y at baseline. An individualized health care cost prediction model was further developed and integrated. This model incorporates nationally representative data on baseline demographics, lifestyle, health, and cause-specific mortality; dynamic changes in modifiable risk factors over time; and parameter uncertainty using probabilistic distributions. Validation analyses included assessment of 1) population-level risk calibration and 2) individual-level risk discrimination. To illustrate the application of the DOC-M model, we evaluated the long-term cost-effectiveness of a national produce prescription program.</p><p><strong>Results: </strong>Comparing the 15-y model-predicted population risk of primary outcomes among the 2001-2002 National Health and Nutrition Examination Survey (NHANES) cohort with the observed prevalence from age-matched cross-sectional 2003-2016 NHANES cohorts, calibration performance was strong based on observed-to-expected ratio and calibration plot analysis. In most cases, Brier scores fell below 0.0004, indicating a low overall prediction error. Using the Multi-Ethnic Study of Atherosclerosis cohorts, the c-statistics for assessing individual-level risk discrimination were 0.85 to 0.88 for diabetes, 0.93 to 0.95 for obesity, 0.74 to 0.76 for CVD history, and 0.78 to 0.81 for all-cause mortality, both overall and in three racial-ethnic groups. Open-source code for the model was posted at https://github.com/food-price/DOC-M-Model-Development-and-Validation.</p><p><strong>Conclusions: </strong>The validated DOC-M model can be used to examine health, equity, and the economic impact of health policies and interventions on behavioral and clinical risk factors for obesity, diabetes, and CVD.</p><p><strong>Highlights: </strong>We developed a novel microsimula'tion model for obesity, diabetes, and CVD, which intersect together and - critically for prevention and treatment interventions - share common lifestyle, biologic, and demographic risk factors.Validation analyses, including assessment of (1) population-level risk calibration and (2) individual-level risk discrimination, showed strong performance across the overall population and three major racial-ethnic groups for 6 outcomes (obesity, diabetes, CVD, and all-cause mortality, CVD- and DM-cause mortality)This paper provides a thorough explanation and document","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10625721/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41240338","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":"The Lure of Beauty: People Select Representations of Statistical Information Largely Based on Attractiveness, Not Comprehensibility.","authors":"Wolfgang Gaissmaier, Kevin E Tiede, Rocio Garcia-Retamero","doi":"10.1177/0272989X231201579","DOIUrl":"10.1177/0272989X231201579","url":null,"abstract":"<p><strong>Objective: </strong>People differ in whether they understand graphical or numerical representations of statistical information better. However, assessing these skills is often not feasible when deciding which representation to select or use. This study investigates whether people choose the representation they understand better, whether this choice can improve risk comprehension, and whether results are influenced by participants' skills (graph literacy and numeracy).</p><p><strong>Methods: </strong>In an experiment, 160 participants received information about the benefits and side effects of painkillers using either a numerical or a graphical representation. In the \"no choice\" condition, the representation was randomly assigned to each participant. In the \"choice\" condition, participants could select the representation they would like to receive. The study assessed gist and verbatim knowledge (immediate comprehension and recall), accessibility of the information, attractiveness of the representation, as well as graph literacy and numeracy.</p><p><strong>Results: </strong>In the \"choice\" condition, most (62.5%) chose the graphical format, yet there was no difference in graph literacy or numeracy (nor age or gender) between people who chose the graphical or the numerical format. Whereas choice slightly increased verbatim knowledge, it did not improve gist or overall knowledge compared with random assignment. However, participants who chose a representation rated the representation as more attractive, and those who chose graphs rated them as more accessible than those without a choice.</p><p><strong>Limitations: </strong>The sample consisted of highly educated undergraduate students with higher graph literacy than the general population. The task was inconsequential for participants in terms of their health.</p><p><strong>Conclusions: </strong>When people can choose between representations, they fail to identify what they comprehend better but largely base that choice on how attractive the representation is for them.</p><p><strong>Highlights: </strong>People differ systematically in whether they understand graphical or numerical representations of statistical information better. However, assessing these underlying skills to get the right representation to the right people is not feasible in practice. A simple and efficient method to achieve this could be to let people choose among representations themselves.However, our study showed that allowing participants to choose a representation (numerical v. graphical) did not improve overall or gist knowledge compared with determining the representation randomly, even though it did slightly improve verbatim knowledge.Rather, participants largely chose the representation they found more attractive. Most preferred the graphical representation, including those with low graph literacy.It would therefore be important to develop graphical representations that are not only attractive but also comprehen","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10625725/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49693441","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":"Survival Extrapolation Incorporating General Population Mortality Using Excess Hazard and Cure Models: A Tutorial.","authors":"Michael J Sweeting, Mark J Rutherford, Dan Jackson, Sangyu Lee, Nicholas R Latimer, Robert Hettle, Paul C Lambert","doi":"10.1177/0272989X231184247","DOIUrl":"10.1177/0272989X231184247","url":null,"abstract":"<p><strong>Background: </strong>Different parametric survival models can lead to widely discordant extrapolations and decision uncertainty in cost-effectiveness analyses. The use of excess hazard (EH) methods, which incorporate general population mortality data, has the potential to reduce model uncertainty. This review highlights key practical considerations of EH methods for estimating long-term survival.</p><p><strong>Methods: </strong>Demonstration of methods used a case study of 686 patients from the German Breast Cancer Study Group, followed for a maximum of 7.3 y and divided into low (1/2) and high (3) grade cancers. Seven standard parametric survival models were fit to each group separately. The same 7 distributions were then used in an EH framework, which incorporated general population mortality rates, and fitted both with and without a cure parameter. Survival extrapolations, restricted mean survival time (RMST), and difference in RMST between high and low grades were compared up to 30 years along with Akaike information criterion goodness-of-fit and cure fraction estimates. The sensitivity of the EH models to lifetable misspecification was investigated.</p><p><strong>Results: </strong>In our case study, variability in survival extrapolations was extensive across the standard models, with 30-y RMST ranging from 7.5 to 14.3 y. Incorporation of general population mortality rates using EH cure methods substantially reduced model uncertainty, whereas EH models without cure had less of an effect. Long-term treatment effects approached the null for most models but at varying rates. Lifetable misspecification had minimal effect on RMST differences.</p><p><strong>Conclusions: </strong>EH methods may be useful for survival extrapolation, and in cancer, EHs may decrease over time and be easier to extrapolate than all-cause hazards. EH cure models may be helpful when cure is plausible and likely to result in less extrapolation variability.</p><p><strong>Highlights: </strong>In health economic modeling, to help anchor long-term survival extrapolation, it has been recommended that survival models incorporate background mortality rates using excess hazard (EH) methods.We present a thorough description of EH methods with and without the assumption of cure and demonstrate user-friendly software to aid researchers wishing to use these methods.EH models are applied to a case study, and we demonstrate that EHs are easier to extrapolate and that the use of the EH cure model, when cure is plausible, can reduce extrapolation variability.EH methods are relatively robust to lifetable misspecification.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10422853/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10155889","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}