Medical Decision Making

Background. Treatment decision making is often guided by evidence-based probabilities, which may be presented to patients during consultations. These probabilities are intrinsically imperfect and embody 2 types of uncertainties: aleatory uncertainty arising from the unpredictability of future events and epistemic uncertainty arising from limitations in the reliability and accuracy of probability estimates. Risk communication experts have recommended disclosing uncertainty. We examined whether uncertainty was discussed during cancer consultations and whether and how patients perceived uncertainty. Methods. Consecutive patient consultations with medical oncologists discussing adjuvant treatment in early-stage breast cancer were audiotaped, transcribed, and coded. Patients were interviewed after the consultation to gain insight into their perceptions of uncertainty. Results. In total, 198 patients were included by 27 oncologists. Uncertainty was disclosed in 49% (97/197) of consultations. In those 97 consultations, 23 allusions to epistemic uncertainty were made and 84 allusions to aleatory uncertainty. Overall, the allusions to the precision of the probabilities were somewhat ambiguous. Interviewed patients mainly referred to aleatory uncertainty if not prompted about epistemic uncertainty. Even when specifically asked about epistemic uncertainty, 1 in 4 utterances referred to aleatory uncertainty. When talking about epistemic uncertainty, many patients contradicted themselves. In addition, 1 in 10 patients seemed not to realize that the probabilities communicated during the consultation are imperfect. Conclusions. Uncertainty is conveyed in only half of patient consultations. When uncertainty is communicated, oncologists mainly refer to aleatory uncertainty. This is also the type of uncertainty that most patients perceive and seem comfortable discussing. Given that it is increasingly common for clinicians to discuss outcome probabilities with their patients, guidance on whether and how to best communicate uncertainty is urgently needed.

Background. Side effects prompt some patients to forego otherwise-beneficial therapies. This study explored which characteristics make side effects particularly aversive. Methods. We used a psychometric approach, originating from research on risk perception, to identify the factors (or components) underlying side effect perceptions. Women (N = 149) aged 40 to 74 years were recruited from a patient registry to complete an online experiment. Participants were presented with hypothetical scenarios in which an effective and necessary medication conferred a small risk of a single side effect (e.g., nausea, dizziness). They rated a broad range of side effects on several characteristics (e.g., embarrassing, treatable). In addition, we collected 4 measures of aversiveness for each side effect: choosing to take the medication, willingness to pay to avoid the side effect (WTP), negative affective attitude associated with the side effect, and how each side effect ranks among others in terms of undesirability. A principal components analysis (PCA) was used to identify the components underlying side effect perceptions. Then, for each aversiveness measure separately, regression analyses were used to determine which components predicted differences in aversiveness among the side effects. Results. The PCA revealed 4 components underlying side effect perceptions: affective challenge (e.g., frightening), social challenge (e.g., disfiguring), physical challenge (e.g., painful), and familiarity (e.g., common). Side effects perceived as affectively and physically challenging elicited the highest levels of aversiveness across all 4 measures. Conclusions. Understanding what side effect characteristics are most aversive may inform interventions to improve medical decisions and facilitate the translation of novel biomedical therapies into clinical practice.

Introduction. Recommendations for colorectal cancer screening encourage patients to choose among various screening methods based on individual preferences for benefits, risks, screening frequency, and discomfort. We devised a model to illustrate how individuals with varying tolerance for screening complications risk might decide on their preferred screening strategy. Methods. We developed a discrete-time Markov mathematical model that allowed hypothetical individuals to maximize expected lifetime utility by selecting screening method, start age, stop age, and frequency. Individuals could choose from stool-based testing every 1 to 3 years, flexible sigmoidoscopy every 1 to 20 years with annual stool-based testing, colonoscopy every 1 to 20 years, or no screening. We compared the life expectancy gained from the chosen strategy with the life expectancy available from a benchmark strategy of decennial colonoscopy. Results. For an individual at average risk of colorectal cancer who was risk neutral with respect to screening complications (and therefore was willing to undergo screening if it would actuarially increase life expectancy), the model predicted that he or she would choose colonoscopy every 10 years, from age 53 to 73 years, consistent with national guidelines. For a similar individual who was moderately averse to screening complications risk (and therefore required a greater increase in life expectancy to accept potential risks of colonoscopy), the model predicted that he or she would prefer flexible sigmoidoscopy every 12 years with annual stool-based testing, with 93% of the life expectancy benefit of decennial colonoscopy. For an individual with higher risk aversion, the model predicted that he or she would prefer 2 lifetime flexible sigmoidoscopies, 20 years apart, with 70% of the life expectancy benefit of decennial colonoscopy. Conclusion. Mathematical models may formalize how individuals with different risk attitudes choose between various guideline-recommended colorectal cancer screening strategies.

Background. Patients with advanced-stage chronic obstructive pulmonary disease (COPD) may suffer severe respiratory exacerbations and need to decide between accepting life-sustaining treatments versus foregoing these treatments (choosing comfort care only). We designed the InformedTogether decision aid to inform this decision and describe results of a pilot study to assess usability focusing on participants’ trust in the content of the decision aid, acceptability, recommendations for improvement, and emotional reactions to this emotionally laden decision. Methods. Study participants (N = 26) comprising clinicians, patients, and surrogates viewed the decision aid, completed usability tasks, and participated in interviews and focus groups assessing comprehension, trust, perception of bias, and perceived acceptability of InformedTogether. Mixed methods were used to analyze results. Results. Almost all participants understood the gist (general meaning) of InformedTogether. However, many lower literacy participants had difficulty answering the more detailed questions related to comprehension, especially when interpreting icon arrays, and many were not aware that they had misunderstood the information. Qualitative analysis showed a range of emotional reactions to the information. Participants with low verbatim comprehension frequently referenced lived experiences when answering knowledge questions, which we termed “alternative knowledge.” Conclusions. We found a range of emotional reactions to the information and frequent use of alternative knowledge frameworks for deriving meaning from the data. These observations led to insights into the impact of lived experiences on the uptake of biomedical information presented in decision aids. Communicating prognostic information could potentially be improved by eliciting alternative knowledge as a starting point to build communication, in particular for low literacy patients. Decision aids designed to facilitate shared decision making should elicit this knowledge and help clinicians tailor information accordingly.

Background. Order and amount of information influence patients’ risk perceptions, but most studies have evaluated patients’ reactions to written materials. The objective of this study was to examine the effect of 4 communication strategies, varying in their order and/or amount of information, on judgments related to an audible description of a new medication and among patients who varied in subjective numeracy. Methods. We created 5 versions of a hypothetical scenario describing a new medication. The versions were composed to elucidate whether order and/or amount of the information describing benefits and adverse events influenced how subjects valued a new medication. After listening to a randomly assigned version, perceived medication value was measured by asking subjects to choose one of the following statements: the risks outweigh the benefits, the risks and benefits are equally balanced, or the benefits outweigh the risks. Results. Of the 432 patients contacted, 389 participated in the study. Listening to a brief description of benefits followed by an extended description of adverse events resulted in a greater likelihood of perceiving that the medication’s benefits outweighed the risks compared with 1) presenting the extended adverse events description before the benefits, 2) giving a greater amount of information related to benefits, and 3) sandwiching the adverse events between benefits. These associations were only observed among subjects with average or higher subjective numeracy. Conclusion. If confirmed in future studies, our results suggest that, for patients with average or better subjective numeracy, perceived medication value is highest when a brief presentation of benefits is followed by an extended description of adverse events.

The effect of a patient’s age on the social valuation of health services remains controversial, with empirical results varying in magnitude and implying a different age-value profile. This article employs a new methodology to re-examine these questions. Data were obtained from 2 independent Web-based surveys that administered the Relative Social Willingness to Pay instrument. In the first survey, the age of the patient receiving a life-saving service was varied. Patients were left with either poor mental or physical health. In the second survey, patient age was varied for a service that fully cured the patient’s poor mental or physical health. In total, therefore, 4 sets of age weights were obtained: weights for life-extending services with poor physical or mental health outcomes and weights for quality-of-life improvement for patients in poor mental or physical health. Results were consistent. Increasing age was associated in each case with a monotonic decrease in the social valuation of the services. The decrease in value was quantitatively small until age 60 years. By age 80 years, the social value of services had declined by about 50%. The decline commenced at an earlier age in the context of physical health, although the magnitude of the decrement by age 80 years was unrelated to the type of service. With 1 exception, there was little difference in the valuation of services by the age of the survey respondent. Respondents aged >60 years placed a lower, not higher, value on quality-of-life improvement for elderly individuals than other respondents. There was no difference in the valuation of life-extending services.

Background. Health state preferences vary among countries, and country-specific value sets are important in health care reimbursement decisions. When decisions are made at the regional level, regional variation in health state preferences may be important. We propose that shrinkage analysis and Bland-Altman plots can be a helpful way to investigate regional variation. Methods. The presence of regional variation can be investigated by introducing interactions between regions and the regression coefficients in the scoring algorithm. When variation is present, regional scoring algorithms can be derived through shrinkage analysis. The impact of using regional algorithms in place of the national algorithm can be investigated using simulation and illustrated using Bland-Altman plots. We applied this methodological approach to the Canadian EQ-5D-5L valuation study, which used time-tradeoff (TTO) tasks to elicit health state preferences from 1073 participants from 4 regions (Alberta, British Columbia, Ontario, and Quebec). Results. There were statistically significant interactions between the fixed effects of the scoring algorithm and region. On computing regional scoring algorithms and applying them to the EQ-5D-5L health states reported by our population, the mean utility using the Canada-wide scoring algorithm was 0.87 (standard error, 0.0013), compared to 0.85 (0.0013) on using the algorithm for Alberta, 0.80 (0.0013) on using the algorithm for British Columbia, 0.91 (0.0013) for Ontario, and 0.89 (0.0014) for Quebec. Conclusions. When health care falls under regional jurisdiction, shrinkage estimators can be used to generate regional scoring algorithms for the EQ-5D-5L and Bland-Altman plots used to assess the importance of regional variation in health state preferences. Our results suggest that mean health state preferences vary among Canada’s regions and make a sizable impact on estimates of population mean utility.

Background. Parameter uncertainty in value sets of multiattribute utility-based instruments (MAUIs) has received little attention previously. This false precision leads to underestimation of the uncertainty of the results of cost-effectiveness analyses. The aim of this study is to examine the use of multiple imputation as a method to account for this uncertainty of MAUI scoring algorithms. Method. We fitted a Bayesian model with random effects for respondents and health states to the data from the original US EQ-5D-3L valuation study, thereby estimating the uncertainty in the EQ-5D-3L scoring algorithm. We applied these results to EQ-5D-3L data from the Commonwealth Fund (CWF) Survey for Sick Adults (n = 3958), comparing the standard error of the estimated mean utility in the CWF population using the predictive distribution from the Bayesian mixed-effect model (i.e., incorporating parameter uncertainty in the value set) with the standard error of the estimated mean utilities based on multiple imputation and the standard error using the conventional approach of using MAUI (i.e., ignoring uncertainty in the value set). Result. The mean utility in the CWF population based on the predictive distribution of the Bayesian model was 0.827 with a standard error (SE) of 0.011. When utilities were derived using the conventional approach, the estimated mean utility was 0.827 with an SE of 0.003, which is only 25% of the SE based on the full predictive distribution of the mixed-effect model. Using multiple imputation with 20 imputed sets, the mean utility was 0.828 with an SE of 0.011, which is similar to the SE based on the full predictive distribution. Conclusion. Ignoring uncertainty of the predicted health utilities derived from MAUIs could lead to substantial underestimation of the variance of mean utilities. Multiple imputation corrects for this underestimation so that the results of cost-effectiveness analyses using MAUIs can report the correct degree of uncertainty.

There is recent interest in using discrete choice experiments (DCEs) to derive health state utility values, and results can differ from time tradeoff (TTO). Clearly, DCE is “choice based,” whereas TTO is generally considered a “matching” task. We explore whether procedural adaptations to the TTO, which make the method more closely resemble a DCE, make TTO and choice converge. In particular, we test whether making the matching procedure in TTO less “transparent” to the respondent reduces disparities between TTO and DCE. We designed an interactive survey that was hosted on the Internet, and 2022 interviews were achieved in the United Kingdom in a representative sample of the population. We found a marked divergence between TTO and DCE, but this was not related to the “transparency” of the TTO procedure. We conclude that a difference in the error structure between TTO and choice and that factors other than differences in utility are affecting choices is driving the divergence. The latter has fundamental implications for the way choice data are analyzed and interpreted.

Background: Discrete choice experiments incorporating duration can be used to derive health state values for EQ-5D-5L. Yet, methodological issues relating to the duration attribute and the optimal way to select health states remain. The aims of this study were to: test increasing the number of duration levels and choice sets where duration varies (aim 1); compare designs with zero and non-zero prior values (aim 2); and investigate a novel, two-stage design to incorporate prior values (aim 3). Methods: Informed by zero and non-zero prior values, two efficient designs were developed, each consisting of 120 EQ-5D-5L health profile pairs with one of six duration levels (aims 1 and 2). Another 120 health state pairs were selected, with one of six duration levels allocated in a second stage based on existing estimated utility of the states (aim 3). An online sample of 2,002 members of the UK general population completed 10 choice sets each. Differences across the regression coefficients from the three designs were assessed. Results: The zero prior value design produced a model with coefficients that were generally logically ordered, but the non-zero prior value design resulted in a set of less ordered coefficients where some differed significantly. The two-stage design resulted in ordered and significant coefficients. The non-zero prior value design may include more “difficult” choice sets, based on the proportions choosing each profile. Conclusions: There is some indication of compromised “respondent efficiency”, suggesting that the use of non-zero prior values will not necessarily result in better overall precision. It is feasible to design discrete choice experiments in two stages by allocating duration values to EQ-5D-5L health state pairs based on estimates from prior studies.

Background. The use of qualitative research (QR) methods is recommended as good practice in discrete choice experiments (DCEs). This study investigated the use and reporting of QR to inform the design and/or interpretation of healthcare-related DCEs and explored the perceived usefulness of such methods. Methods. DCEs were identified from a systematic search of the MEDLINE database. Studies were classified by the quantity of QR reported (none, basic, or extensive). Authors (n = 91) of papers reporting the use of QR were invited to complete an online survey eliciting their views about using the methods. Results. A total of 254 healthcare DCEs were included in the review; of these, 111 (44%) did not report using any qualitative methods; 114 (45%) reported “basic” information; and 29 (11%) reported or cited “extensive” use of qualitative methods. Studies reporting the use of qualitative methods used them to select attributes and/or levels (n = 95; 66%) and/or pilot the DCE survey (n = 26; 18%). Popular qualitative methods included focus groups (n = 63; 44%) and interviews (n = 109; 76%). Forty-four studies (31%) reported the analytical approach, with content (n = 10; 7%) and framework analysis (n = 5; 4%) most commonly reported. The survey identified that all responding authors (n = 50; 100%) found that qualitative methods added value to their DCE study, but many (n = 22; 44%) reported that journals were uninterested in the reporting of QR results. Conclusions. Despite recommendations that QR methods be used alongside DCEs, the use of QR methods is not consistently reported. The lack of reporting risks the inference that QR methods are of little use in DCE research, contradicting practitioners’ assessments. Explicit guidelines would enable more clarity and consistency in reporting, and journals should facilitate such reporting via online supplementary materials.

Background. Children’s preferences for health states represent an important perspective when comparing the value of alternative health care interventions related to pediatric medicine, and are fundamental to comparative effectiveness research. However, there is debate over whether these preference data can be collected and used. Purpose. The purpose of this study was to establish psychometric properties of eliciting preferences for health states from children using direct methods. Data Sources. Ovid Medline, PsycINFO, Scopus, EconLit. Study Selection. English studies, published after 1990, were identified using Medical Subject Headings or keywords. Results were reviewed to confirm that the study was based on: 1) a sample of children, and 2) preferences for health states. Data Extraction. Standardized data collection forms were used to record the preference elicitation method used, and any reported evidence regarding the validity, reliability, or feasibility of the method. Data Synthesis. Twenty-six studies were ultimately included in the analysis. The standard gamble and time tradeoff were the most commonly reported direct preference elicitation methods. Seven studies reported validity, four reported reliability, and nine reported feasibility. Of the validity reports, construct validity was assessed most often. Reliability reports typically involved interclass correlation coefficient. For feasibility, four studies reported completion rates. Limitations. The search was limited to four databases and restricted to English studies published after 1990. Only evidence available in published studies were considered; measurement properties may have been tested in pilot or pre-studies but were not published, and are not included in this review. Conclusion. The few studies found through this systematic review demonstrate that there is little empirical evidence on which to judge the use of direct preference elicitation methods with children regarding health states.