Medical Decision Making最新文献

{"title":"Attitudes on Equal Health Care Access versus Efficient Clinical Decisions across a Not-for-Profit Health Care System.","authors":"Ganeev Singh, Laura Corlin, Paul R Beninger, Peter J Neumann, Marcia M Boumil, Shreya Mehta, Deeb N Salem","doi":"10.1177/0272989X231206750","DOIUrl":"10.1177/0272989X231206750","url":null,"abstract":"<p><strong>Background: </strong>Professional roles within a hospital system may influence attitudes behind clinical decisions.</p><p><strong>Objective: </strong>To determine participants' preferences about clinical decisions that either value equal health care access or efficiency.</p><p><strong>Design: </strong>Deidentified survey asking participants to choose between offering a low-cost screening test to a whole population (\"equal access\") or a more sensitive, expensive test that could be given to only half of the population but resulting in 10% more avoided deaths (\"efficient\"). Data collection took place from August 18, 2021, to January 24, 2022. Study 1644 was determined to be exempt by Tufts Health Sciences Institutional Review Board (IRB).</p><p><strong>Setting: </strong>Tufts Medicine Healthcare System.</p><p><strong>Participants: </strong>Approximately 15,000 hospital employees received an e-mail from the Tufts Medicine Senior Vice President of Academic Integration.</p><p><strong>Measurements: </strong>Analysis of survey responses with chi-square and 1-sample <i>t</i> tests to determine the proportion who chose each option. Logistic regression models fit to examine relationships between professional role and test choice.</p><p><strong>Results: </strong>A total of 1,346 participants completed the survey (∼9.0% response rate). Overall, approximately equal percentages of respondents chose the \"equal access\" (48%) and \"efficient\" option (52%). However, gender, professional role (categorical), and clinical role (dichotomous) were significantly associated with test choice. For example, among those in nonclinical roles, women were more likely than men to choose equal health care access. In multivariable analyses, having clinical roles was significantly associated with 1.73 times the likelihood of choosing equal access (95% confidence interval = 1.33-2.25).</p><p><strong>Limitations: </strong>Generalizability concerns and survey question wording limit the study results.</p><p><strong>Conclusion: </strong>Clinicians were more likely than nonclinicians to choose the equal health care access option, and health care administrators were more likely to choose efficiency. These differing attitudes can affect patient care and health care quality.</p><p><strong>Highlights: </strong>Divergent preferences of valuing equal health care access and efficiency may be in conflict during clinical decision making.In this cross-sectional study that included 1,346 participants, approximately equal percentages of respondents chose the \"equal access\" (48%) and \"efficient\" option (52%), a nonsignificant difference. However, gender, professional role (categorical), and clinical role (dichotomous) were significantly associated with test choiceSince clinicians were more likely than nonclinicians to choose the equal health care access option and health care administrators were more likely to choose efficiency, these differing attitudes can affect patient care and health ca","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"18-27"},"PeriodicalIF":3.1,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50159082","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":"The Blurred Lines of HTA Agency Decision Making.","authors":"Grace Mitchell, Sreeram V Ramagopalan","doi":"10.1177/0272989X231208443","DOIUrl":"10.1177/0272989X231208443","url":null,"abstract":"","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"3-4"},"PeriodicalIF":3.1,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72211626","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":"Patient Reasoning: Patients' and Care Partners' Perceptions of Diagnostic Accuracy in Emergency Care.","authors":"Vadim Dukhanin, Kathryn M McDonald, Natalia Gonzalez, Kelly T Gleason","doi":"10.1177/0272989X231207829","DOIUrl":"10.1177/0272989X231207829","url":null,"abstract":"<p><strong>Objectives: </strong>In the context of validating a measure of patient report specific to diagnostic accuracy in emergency department or urgent care, this study investigates patients' and care partners' perceptions of diagnoses as accurate and explores variations in how they reason while they assess accuracy.</p><p><strong>Methods: </strong>In February 2022, we surveyed a national panel of adults who had an emergency department or urgent care visit in the past month to test a patient-reported measure. As part of the survey validation, we asked for free-text responses about why the respondents indicated their (dis)agreement with 2 statements comprising patient-reported diagnostic accuracy: 1) the explanation they received of the health problem was true and 2) the explanation described what to expect of the health problem. Those paired free-text responses were qualitatively analyzed according to themes created inductively.</p><p><strong>Results: </strong>A total of 1,116 patients and care partners provided 982 responses coded into 10 themes, which were further grouped into 3 reasoning types. Almost one-third (32%) of respondents used only corroborative reasoning in assessing the accuracy of the health problem explanation (alignment of the explanation with either test results, patients' subsequent health trajectory, their medical knowledge, symptoms, or another doctor's opinion), 26% used only perception-based reasoning (perceptions of diagnostic process, uncertainty around the explanation received, or clinical team's attitudes), and 27% used both types of reasoning. The remaining 15% used general beliefs or nonexplicated logic (used only about accurate diagnoses) and combinations of general reasoning with perception-based and corroborative.</p><p><strong>Conclusions: </strong>Patients and care partners used multifaceted reasoning in their assessment of diagnostic accuracy.</p><p><strong>Implications: </strong>As health care shifts toward meaningful diagnostic co-production and shared decision making, in-depth understanding of variations in patient reasoning and mental models informs use in clinical practice.</p><p><strong>Highlights: </strong>An analysis of 982 responses examined how patients and care partners reason about the accuracy of diagnoses they received in emergency or urgent care.In reasoning, people used their perception of the process and whether the diagnosis matched other factual information they have.We introduce \"patient reasoning\" in the diagnostic measurement context as an area of further research to inform diagnostic shared decision making and co-production of health.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"102-111"},"PeriodicalIF":3.1,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10712203/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"107592592","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":"Assessing and Understanding Reactance, Self-Exemption, Disbelief, Source Derogation and Information Conflict in Reaction to Overdiagnosis in Mammography Screening: Scale Development and Preliminary Validation.","authors":"Laura D Scherer, Krithika Suresh, Carmen L Lewis, Kirsten J McCaffery, Jolyn Hersch, Joseph N Cappella, Brad Morse, Channing E Tate, Bridget S Mosley, Sarah Schmiege, Marilyn M Schapira","doi":"10.1177/0272989X231195603","DOIUrl":"10.1177/0272989X231195603","url":null,"abstract":"<p><strong>Purpose: </strong>Overdiagnosis is a concept central to making informed breast cancer screening decisions, and yet some people may react to overdiagnosis with doubt and skepticism. The present research assessed 4 related reactions to overdiagnosis: reactance, self-exemption, disbelief, and source derogation (REDS). The degree to which the concept of overdiagnosis conflicts with participants' prior beliefs and health messages (information conflict) was also assessed as a potential antecedent of REDS. We developed a scale to assess these reactions, evaluated how those reactions are related, and identified their potential implications for screening decision making.</p><p><strong>Methods: </strong>Female participants aged 39 to 49 years read information about overdiagnosis in mammography screening and completed survey questions assessing their reactions to that information. We used a multidimensional theoretical framework to assess dimensionality and overall domain-specific internal consistency of the REDS and Information Conflict questions. Exploratory and confirmatory factor analyses were performed using data randomly split into a training set and test set. Correlations between REDS, screening intentions, and other outcomes were evaluated.</p><p><strong>Results: </strong>Five-hundred twenty-five participants completed an online survey. Exploratory and confirmatory factor analyses identified that Reactance, Self Exemption, Disbelief, Source Derogation, and Information Conflict represent unique constructs. A reduced 20-item scale was created by selecting 4 items per construct, which showed good model fit. Reactance, Disbelief, and Source Derogation were associated with lower intent to use information about overdiagnosis in decision making and the belief that informing people about overdiagnosis is unimportant.</p><p><strong>Conclusions: </strong>REDS and Information Conflict are distinct but correlated constructs that are common reactions to overdiagnosis. Some of these reactions may have negative implications for making informed screening decisions.</p><p><strong>Highlights: </strong>Overdiagnosis is a concept central to making informed breast cancer screening decisions, and yet when provided information about overdiagnosis, some people are skeptical.This research developed a measure that assessed different ways in which people might express skepticism about overdiagnosis (reactance, self-exemption, disbelief, source derogation) and also the perception that overdiagnosis conflicts with prior knowledge and health messages (information conflict).These different reactions are distinct but correlated and are common reactions when people learn about overdiagnosis.Reactance, disbelief, and source derogation are associated with lower intent to use information about overdiagnosis in decision making as well as the belief that informing people about overdiagnosis is unimportant.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"789-802"},"PeriodicalIF":3.6,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10843591/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10579294","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":"Collecting Physicians' Preferences on Medical Devices: Are We Doing It Right? Evidence from Italian Orthopedists Using 2 Different Stated Preference Methods.","authors":"Patrizio Armeni, Michela Meregaglia, Ludovica Borsoi, Giuditta Callea, Aleksandra Torbica, Francesco Benazzo, Rosanna Tarricone","doi":"10.1177/0272989X231201805","DOIUrl":"10.1177/0272989X231201805","url":null,"abstract":"<p><strong>Objectives: </strong>Physician preference items (PPIs) are high-cost medical devices for which clinicians express firm preferences with respect to a particular manufacturer or product. This study aims to identify the most important factors in the choice of new PPIs (hip or knee prosthesis) and infer about the existence of possible response biases in using 2 alternative stated preference techniques.</p><p><strong>Methods: </strong>Six key attributes with 3 levels each were identified based on a literature review and clinical experts' opinions. An online survey was administered to Italian hospital orthopedists using type 1 best-worst scaling (BWS) and binary discrete choice experiment (DCE). BWS data were analyzed through descriptive statistics and conditional logit model. A mixed logit regression model was applied to DCE data, and willingness-to-pay (WTP) was estimated. All analyses were conducted using Stata 16.</p><p><strong>Results: </strong>A sample of 108 orthopedists were enrolled. In BWS, the most important attribute was \"clinical evidence,\" followed by \"quality of products,\" while the least relevant items were \"relationship with the sales representative\" and \"cost.\" DCE results suggested instead that orthopedists prefer high-quality products with robust clinical evidence, positive health technology assessment recommendation and affordable cost, and for which they have a consolidated experience of use and a good relationship with the sales representative.</p><p><strong>Conclusions: </strong>The elicitation of preferences for PPIs using alternative methods can lead to different results. The BWS of type 1, which is similar to a ranking exercise, seems to be more affected by acquiescent responding and social desirability than the DCE, which introduces tradeoffs in the choice task and is likely to reveal more about true preferences.</p><p><strong>Highlights: </strong>Physician preference items (PPIs) are medical devices particularly exposed to physicians' choice with regard to type of product and supplier.Some established techniques of collecting preferences can be affected by response biases such as acquiescent responding and social desirability.Discrete choice experiments, introducing more complex tradeoffs in the choice task, are likely to mitigate such biases and reveal true physicians' preferences for PPIs.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"886-900"},"PeriodicalIF":3.6,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10848602/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41218005","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":"Pricing Treatments Cost-Effectively when They Have Multiple Indications: Not Just a Simple Threshold Analysis.","authors":"Jeremy D Goldhaber-Fiebert, Lauren E Cipriano","doi":"10.1177/0272989X231197772","DOIUrl":"10.1177/0272989X231197772","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Economic evaluations of treatments increasingly employ price-threshold analyses. When a treatment has multiple indications, standard price-threshold analyses can be overly simplistic. We examine how rules governing indication-specific prices and reimbursement decisions affect value-based price analyses.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We analyze a 2-stage game between 2 players: the therapy's manufacturer and the payer purchasing it for patients. First, the manufacturer selects a price(s) that may be indication specific. Then, the payer decides whether to provide reimbursement at the offered price(s). We assume known indication-specific demand. The manufacturer seeks to maximize profit. The payer seeks to maximize total population incremental net monetary benefit and will not pay more than their willingness-to-pay threshold. We consider game variants defined by constraints on the manufacturer's ability to price and payer's ability to provide reimbursement differentially by indication.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;When both the manufacturer and payer can make indication-specific decisions, the problem simplifies to multiple single-indication price-threshold analyses, and the manufacturer captures all the consumer surplus. When the manufacturer is restricted to one price and the payer must make an all-or-nothing reimbursement decision, the selected price is a weighted average of indication-specific threshold prices such that reimbursement of more valuable indications subsidizes reimbursement of less valuable indications. With a single price and indication-specific coverage decisions, the manufacturer may select a high price where fewer patients receive treatment because the payer restricts reimbursement to the set of indications providing value commensurate with the high price. However, the manufacturer may select a low price, resulting in reimbursement for more indications and positive consumer surplus.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;When treatments have multiple indications, economic evaluations including price-threshold analyses should carefully consider jurisdiction-specific rules regarding pricing and reimbursement decisions.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Highlights: &lt;/strong&gt;With treatment prices rising, economic evaluations increasingly employ price-threshold analyses to identify value-based prices. Standard price-threshold analyses can be overly simplistic when treatments have multiple indications.Jurisdiction-specific rules governing indication-specific prices and reimbursement decisions affect value-based price analyses.When the manufacturer is restricted to one price for all indications and the payer must make an all-or-nothing reimbursement decision, the selected price is a weighted average of indication-specific threshold prices such that reimbursement of the more valuable indications subsidize reimbursement of the less valuable indications.With a single price and indication-specific coverage decisions, the manu","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"914-929"},"PeriodicalIF":3.6,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10625719/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10213102","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":"Constrained Optimization for Decision Making in Health Care Using Python: A Tutorial.","authors":"K H Benjamin Leung, Nasrin Yousefi, Timothy C Y Chan, Ahmed M Bayoumi","doi":"10.1177/0272989X231188027","DOIUrl":"10.1177/0272989X231188027","url":null,"abstract":"<p><strong>Highlights: </strong>This tutorial provides a user-friendly guide to mathematically formulating constrained optimization problems and implementing them using Python.Two examples are presented to illustrate how constrained optimization is used in health applications, with accompanying Python code provided.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"760-773"},"PeriodicalIF":3.6,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10625722/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10227647","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":"How Do People Process Different Representations of Statistical Information? Insights into Cognitive Effort, Representational Inconsistencies, and Individual Differences.","authors":"Kevin E Tiede, Wolfgang Gaissmaier","doi":"10.1177/0272989X231202505","DOIUrl":"10.1177/0272989X231202505","url":null,"abstract":"<p><strong>Background: </strong>Graphical representation formats (e.g., icon arrays) have been shown to lead to better understanding of the benefits and risks of treatments compared to numbers. We investigate the cognitive processes underlying the effects of format on understanding: how much cognitive effort is required to process numerical and graphical representations, how people process inconsistent representations, and how numeracy and graph literacy affect information processing.</p><p><strong>Methods: </strong>In a preregistered between-participants experiment, 665 participants answered questions about the relative frequencies of benefits and side effects of 6 medications. First, we manipulated whether the medical information was represented numerically, graphically (as icon arrays), or inconsistently (numerically for 3 medications and graphically for the other 3). Second, to examine cognitive effort, we manipulated whether there was time pressure or not. In an additional intervention condition, participants translated graphical information into numerical information before answering questions. We also assessed numeracy and graph literacy.</p><p><strong>Results: </strong>Processing icon arrays was more strongly affected by time pressure than processing numbers, suggesting that graphical formats required more cognitive effort. Understanding was lower when information was represented inconsistently (v. consistently) but not if there was a preceding intervention. Decisions based on inconsistent representations were biased toward graphically represented options. People with higher numeracy processed quantitative information more efficiently than people with lower numeracy did. Graph literacy was not related to processing efficiency.</p><p><strong>Limitations: </strong>Our study was conducted with a nonpatient sample, and the medical information was hypothetical.</p><p><strong>Conclusions: </strong>Although graphical (v. numerical) formats have previously been found to lead to better understanding, they may require more cognitive effort. Therefore, the goal of risk communication may play an important role when choosing how to communicate medical information.</p><p><strong>Highlights: </strong>This article investigates the cognitive processes underlying the effects of representation format on the understanding of statistical information and individual differences therein.Processing icon arrays required more cognitive effort than processing numbers did.When information was represented inconsistently (i.e., partly numerically and partly graphically), understanding was lower than with consistent representation, and decisions were biased toward the graphically represented options.People with higher numeracy processed quantitative information more efficiently than people with lower numeracy did.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"803-820"},"PeriodicalIF":3.6,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10625726/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41240339","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":"Development of a Naturalness Preference Scale.","authors":"Shawna F Bayerman, Meng Li, Adnan Syed, Laura D Scherer","doi":"10.1177/0272989X231189494","DOIUrl":"10.1177/0272989X231189494","url":null,"abstract":"<p><strong>Objective: </strong>Naturalness preference can influence important health decisions. However, the literature lacks a reliable way to measure individual differences in naturalness preferences. We fill this gap by designing and validating a scale to measure individual differences in naturalness preference.</p><p><strong>Methods: </strong>We conducted 3 studies among Amazon Mechanical Turk participants. In study 1 (<i>N</i> = 451), we created scale items through an iterative process that measured naturalness preference in hypothesized domains. We conducted exploratory factor analysis (EFA) to identify items that assess the naturalness preference construct. In study 2 (<i>N</i> = 448), we conducted confirmatory factor analysis (CFA) and tests of criterion, discriminant, convergent, and incremental validity. In study 3 (<i>N</i> = 607), we confirmed test-retest reliability of the scale and performed additional validity tests.</p><p><strong>Results: </strong>EFA revealed 3 correlated factors consistent with naturalness preference in medicine, food, and household products. The CFA confirmed the 3-factor structure and led to the decision to drop reverse-coded items. The finalized Naturalness Preference Scale (NPS) consists of 20 items and 3 subscales: NPS-medicine, NPS-food, and NPS-household products. The NPS demonstrated good test-retest reliability, and subscales had good validity in their respective domains. The NPS-medicine subscale was predictive of the uptake of a hypothetical COVID-19 vaccine (<i>r</i> = -0.45) and belief in unproven natural COVID remedies and treatments (<i>r</i> = 0.29).</p><p><strong>Conclusions: </strong>The NPS will allow researchers to better assess individual differences in naturalness preference and how they influence decision making and health behaviors.</p><p><strong>Highlights: </strong>This research created and validated a scale to measure individual differences in naturalness preference in 3 domains: medicine, food, and household products.This study confirms that the strength of the naturalness preference differs in different domains.An important and timely finding is that higher scores in the naturalness preference medical subscale are associated with belief in COVID-19 misinformation and reluctance toward COVID-19 vaccination.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"821-834"},"PeriodicalIF":3.6,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9922833","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":"Re-revisiting the Utilities of Health States Worse than Dead: The Problem Remains.","authors":"Michał Jakubczyk","doi":"10.1177/0272989X231201147","DOIUrl":"10.1177/0272989X231201147","url":null,"abstract":"<p><strong>Background: </strong>In valuation studies of the EQ-5D-5L instrument, the composite time tradeoff method (cTTO) is often used to elicit preferences. In cTTO, some health states are considered worse than dead (WTD) and are assigned negative utility values. However, these negative values correlate poorly with state severity, which suggests that cTTO is insufficiently sensitive. A recent threshold explanation has been offered to account for the lack of correlation: because the severity threshold beyond which a state is considered WTD differs between respondents, the correlation should be studied for individual respondents clustered by the number of WTD states. The results obtained in such a threshold approach were interpreted to disprove the insensitivity of the cTTO method.</p><p><strong>Aim: </strong>To scrutinize the threshold explanation and test whether it indeed refutes the insensitivity of cTTO.</p><p><strong>Methods: </strong>The study uses data from the EQ-5D-5L Polish valuation study, which includes cTTO responses from 1,510 participants, each of whom evaluated 10 EQ-5D-5L states. The correlation analysis and threshold approach are repeated to confirm the results from previous studies. The data are then modified in 2 contrasting ways. First, negative utilities are randomly reshuffled to test whether the threshold approach can capture cTTO insensitivity. Second, individual-level regressions are used to simulate negative values to ensure they correlate with severity at the individual respondent level, verifying whether the overall severity-utility correlation should be observed.</p><p><strong>Results: </strong>First, reshuffling negative utilities does not change the results of the threshold approach. Hence, the threshold explanation fails to prove cTTO sensitivity. Second, when sensitivity was introduced on an individual level, a significant overall correlation between severity and negative utility arose.</p><p><strong>Conclusion: </strong>cTTO is insensitive to severity for WTD states.</p><p><strong>Highlights: </strong>For the composite time tradeoff method, the utility values of health states worse than dead correlate poorly with state severity, which suggests that cTTO has insufficient sensitivity.Recently, a so-called threshold explanation was offered for the lack of correlation.I show why the threshold explanation fails and why the composite time tradeoff is indeed insensitive for worse-than-dead states.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"875-885"},"PeriodicalIF":3.6,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41240340","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}