Getting it right with discrete choice experiments: Are we hot or cold?

Discrete Choice Experiments (DCEs) are widely employed survey-based methods to assess preferences for healthcare services and products. While they offer an experimental way to represent health-related decisions, the stylized representation of scenarios in DCEs may overlook contextual factors that could influence decision-making. The aim of this paper was to evaluate the predictive validity of preferences elicited through a DCE in decisions likely influenced by a hot-cold empathy gap, and compare it to another commonly used method, a direct-elicitation question. We focused on preferences for pain-relief modalities, especially for an epidural during childbirth - a context where direct-elicitation questions have shown a preference for or intention to have a natural birth (representing the "cold" state), yet individuals often opt for an epidural during labor (representing the "hot" state). Leveraging a unique dataset collected from 248 individuals, we incorporated both the stated preferences collected through a survey administered upon hospital admission for childbirth and the actual pain-relief modality usage data documented in medical records. The DCE allowed for the evaluation of scenarios outside of those expected by respondents to simulate decision-making during childbirth. When we compared the predicted epidural use with the actual epidural use during labor, we observed a choice concordance of 71-60%, depending on the model specification. The concordance rate between the predicted and actual choices increased to 77-76% when accounting for the initial use of other ineffective modalities. In contrast, the direct-elicitation choices, relying solely on respondents' baseline expectations, yielded a lower concordance rate of 58% with actual epidural use. These findings highlight the flexibility of the DCE method in simulating complex decision contexts, including those involving hot-cold empathy gaps. The DCE proves valuable in assessing nuanced preferences, providing a more accurate representation of the decision-making processes in healthcare scenarios.

A Quantitative Framework for Medication Non-Adherence: Integrating Patient Treatment Expectations and Preferences.

Introduction: Medication non-adherence remains a significant challenge in healthcare, impacting treatment outcomes and the overall effectiveness of medical interventions. This article introduces a novel approach to understanding and predicting medication non-adherence by integrating patient beliefs, efficacy expectations, and perceived costs. Existing theoretical models often fall short in quantifying the impact of barrier removal on medication adherence and struggle to address cases where patients consciously choose not to follow prescribed medication regimens. In response to these limitations, this study presents an empirical framework that seeks to provide a quantifiable model for both individual and population-level prediction of non-adherence under different scenarios. Methods: We present an empirical framework that includes a health production function, specifically applied to antihypertensive medications nonadherence. Data collection involved a pilot study that utilized a double-bound contingent-belief (DBCB) questionnaire. Through this questionnaire, participants could express how efficacy and side effects were affected by controlled levels of non-adherence, allowing for the estimation of sensitivity in health outcomes and costs. Results: Parameters derived from the DBCB questionnaire revealed that on average, patients with hypertension anticipated that treatment efficacy was less sensitive to non-adherence than side effects. Our derived health production function suggests that patients may strategically manage adherence to minimize side effects, without compromising efficacy. Patients' inclination to manage medication intake is closely linked to the relative importance they assign to treatment efficacy and side effects. Model outcomes indicate that patients opt for full adherence when efficacy outweighs side effects. Our findings also indicated an association between income and patient expectations regarding the health of antihypertensive medications. Conclusion: Our framework represents a pioneering effort to quantitatively link non-adherence to patient preferences. Preliminary results from our pilot study of patients with hypertension suggest that the framework offers a viable alternative for evaluating the potential impact of interventions on treatment adherence.

Did a bot eat your homework? An assessment of the potential impact of bad actors in online administration of preference surveys.

BACKGROUND: Online administration of surveys has a number of advantages but can also lead to increased exposure to bad actors (human and non-human bots) who can try to influence the study results or to benefit financially from the survey. We analyze data collected through an online discrete-choice experiment (DCE) survey to evaluate the likelihood that bad actors can affect the quality of the data collected. METHODS: We developed and fielded a survey instrument that included two sets of DCE questions asking respondents to select their preferred treatments for multiple myeloma therapies. The survey also included questions to assess respondents' attention while completing the survey and their understanding of the DCE questions. We used a latent-class model to identify a class associated with perverse preferences or high model variance, and the degree to which the quality checks included in the survey were correlated with class membership. Class-membership probabilities for the problematic class were used as weights in a random-parameters logit to recover population-level estimates that minimizes exposure to potential bad actors. RESULTS: Results show a significant proportion of respondents provided answers with a high degree of variability consistent with responses from bad actors. We also found that a wide-ranging selection of conditions in the survey screener is more consistent with choice patterns expected from bad actors looking to qualify for the study. The relationship between the number of incorrect answers to comprehension questions and problematic choice patterns peaked around 5 out of 10 questions. CONCLUSIONS: Our results highlight the need for a robust discussion around the appropriate way to handle bad actors in online preference surveys. While exclusion of survey respondents must be avoided under most circumstances, the impact of "bots" on preference estimates can be significant.

Discrete-Choice Experiment to Understand the Preferences of Patients with Hormone-Sensitive Prostate Cancer in the USA, Canada, and the UK.

BACKGROUND: Treatment options for patients with metastatic hormone-sensitive prostate cancer (mHSPC) have broadened, and treatment decisions can have a long-lasting impact on patients' quality of life. Data on patient preferences can improve therapeutic decision-making by helping physicians suggest treatments that align with patients' values and needs. OBJECTIVE: This study aims to quantify patient preferences for attributes of chemohormonal therapies among patients with mHSPC in the USA, Canada, and the UK. METHODS: A discrete-choice experiment survey instrument was developed and administered to patients with high- and very-high-risk localized prostate cancer and mHSPC. Patients chose between baseline androgen-deprivation therapy (ADT) alone and experimentally designed, hypothetical treatment alternatives representing chemohormonal therapies. Choices were analyzed using logit models to derive the relative importance of attributes for each country and to evaluate differences and similarities among patients across countries. RESULTS: A total of 550 respondents completed the survey (USA, 200; Canada, 200; UK, 150); the mean age of respondents was 64.3 years. Treatment choices revealed that patients were most concerned with treatment efficacy. However, treatment-related convenience factors, such as route of drug administration and frequency of monitoring visits, were as important as some treatment-related side effects, such as skin rash, nausea, and fatigue. Patient preferences across countries were similar, although patients in Canada appeared to be more affected by concomitant steroid use. CONCLUSION: Patients with mHSPC believe the use of ADT alone is insufficient when more effective treatments are available. Efficacy is the most significant driver of patient choices. Treatment-related convenience factors can be as important as safety concerns for patients.

How Much Better is Faster? Value Adjustments for Health-Improvement Sequences.

While the quality-adjusted life-year construct has advantages of simplicity and consistency, simplicity requires strong assumptions. In particular, standard assumptions result in health-state utility functions that are unrealistically linear and separable in risk and duration. Consequently, sequencing of a series of health improvements has no effect on the total value of the sequence because each increment is assessed independently of previous increments. Utility functions in nearly all other areas of applied economics are assumed to be nonlinear with diminishing marginal utility so it matters where an improvement occurs in a sequence. We construct a conceptual framework that that demonstrates how diminishing marginal utility for health improvements could affect preferences for different sequence patterns. Using this framework, we derive conditions for which the sum of conventional health-state utilities understates, overstates, or approximates the sequence-sensitive value of health improvements. These patterns suggest the direction and magnitude of possible adjustments to conventional value calculations. We provide numerical examples and identify recent studies whose results are consistent with the conceptual model.

Use of Patient Preferences Data Regarding Multiple Risks to Inform Regulatory Decisions.

Background and Objectives. Risk-tolerance measures from patient-preference studies typically focus on individual adverse events. We recently introduced an approach that extends maximum acceptable risk (MAR) calculations to simultaneous maximum acceptable risk thresholds (SMART) for multiple treatment-related risks. We extend these methods to include the computation and display of confidence intervals and apply the approach to 3 published discrete-choice experiments to evaluate its utility to inform regulatory decisions. Methods. We generate MAR estimates and SMART curves and compare them with trial-based benefit-risk profiles of select treatments for depression, psoriasis, and thyroid cancer. Results. In the depression study, SMART curves with 70% to 95% confidence intervals portray which combinations of 2 adverse events would be considered acceptable. In the psoriasis example, the asymmetric confidence intervals for the SMART curve indicate that relying on independent MARs versus SMART curves when there are nonlinear preferences can lead to decisions that could expose patients to greater risks than they would accept. The thyroid cancer application shows an example in which the clinical incidence of each of 3 adverse events is lower than the single-event MARs for the expected treatment benefit, yet the collective risk profile surpasses acceptable levels when considered jointly. Limitations. Nonrandom sample of studies. Conclusions. When evaluating conventional MARs in which the observed incidences are near the estimated MARs or where preferences demonstrate diminishing marginal disutility of risk, conventional MAR estimates will overstate risk acceptance, which could lead to misinformed decisions, potentially placing patients at greater risk of adverse events than they would accept. Implications. The SMART method, herein extended to include confidence intervals, provides a reproducible, transparent evidence-based approach to enable decision makers to use data from discrete-choice experiments to account for multiple adverse events. Highlights: Estimates of maximum acceptable risk (MAR) for a defined treatment benefit can be useful to inform regulatory decisions; however, the conventional metric considers one adverse event at a time.This article applies a new approach known as SMART (simultaneous maximum acceptable risk thresholds) that accounts for multiple adverse events to 3 published discrete-choice experiments.Findings reveal that conventional MARs could lead decision makers to accept a treatment based on individual risks that would not be acceptable if multiple risks are considered simultaneously.

How Does the Public Evaluate Vaccines for Low-Incidence, Severe-Outcome Diseases? A General-Population Choice Experiment.

BACKGROUND: Because immunizing large numbers of healthy people could be required to reduce a relatively small number of infections, disease incidence has a large impact on cost effectiveness, even if the infection is associated with very serious health outcomes. In addition to cost effectiveness, the US Advisory Committee on Immunization Practices requires evidence of stakeholders' values and preferences to help inform vaccine recommendations. This study quantified general-population preferences for vaccine trade-offs among disease severity, disease incidence, and other vaccine features. METHODS: We developed a best-practice discrete choice experiment survey and administered it to 1185 parents of children aged 12-23 years and 1203 young adults aged 18-25 years from a national opt-in consumer panel. The data were analyzed using exploded-logit latent-class analysis. RESULTS: Latent-class analysis identified two classes with similar relative-importance weights in both samples. One of the two classes represented about half the samples and had preferences consistent with well-structured, logically ordered, and acceptably precise stated-preference utility. Preferences for the other half of the samples were poorly defined over the ranges of vaccine and disease attributes evaluated. Both parents and young adults in the first class evaluated protection from a disease with 1 in 100 incidence and full recovery at home as having statistically the same preference utility as a disease with 1 in 1 million incidence requiring hospitalization and resulting in permanent deafness. CONCLUSIONS: The results suggest that vaccines that protect against low-incidence, severe-outcome diseases, provide 'peace of mind' benefits not captured by standard health-outcome metrics. The fact that half the respondents had poorly defined vaccine preferences is a reminder of the challenges of implementing patient-centric vaccine decision making.

Method for Calculating the Simultaneous Maximum Acceptable Risk Threshold (SMART) from Discrete-Choice Experiment Benefit-Risk Studies.

BACKGROUND: Medical decisions require weighing expected benefits of treatment against multiple adverse outcomes under uncertainty (i.e., risks) that must be accepted as a bundle. However, conventional maximum acceptable risk (MAR) estimates derived from discrete-choice experiment benefit-risk studies evaluate the acceptance of individual risks, assuming other risks are fixed, potentially leading decision makers to misinterpret levels of risk acceptance. DESIGN: Using simulations and a published discrete-choice experiment, we demonstrate a method for identifying multidimensional risk-tolerance measures given a treatment level of benefit. RESULTS: Simultaneous Maximum Acceptable Risk Thresholds (SMART) represents combinations of risks that would be jointly accepted in exchange for specific treatment benefits. The framework shows how the expectation of utility associated with treatments that involve multiple risks are related even when preferences for potential adverse events are independent. We find that the form of the marginal effects of adverse-event probabilities on the expected utility of treatment determines the magnitude of differences between SMART and conventional single-outcome MAR estimates. LIMITATIONS: Preferences for potential adverse events not considered in a study or preferences for adverse-event attributes held constant in risk-tolerance calculations may affect estimated risk tolerance. Further research is needed to understand the right balance between realistically reflecting clinical treatments with many potential adverse events and the cognitive burden of evaluating risk-risk tradeoffs in research and in practice. CONCLUSIONS AND IMPLICATIONS: SMART analysis should be considered in preference studies evaluating the joint acceptance of multiple potential adverse events. HIGHLIGHTS: Conventional approaches to calculate maximum-acceptable risk (MAR) using discrete-choice experiment data account for 1 adverse-event risk at a time, requiring that decision makers infer the acceptability of treatments when patients are exposed to multiple risks simultaneously.The Simultaneous Maximum Acceptable Risk Threshold (SMART) maps combinations of adverse-event risks that would be jointly acceptable given a specific treatment benefit and provides a transparent and precise portrayal of acceptance of multiple risks.Risk levels that would be accepted using individual MAR estimates might not be acceptable when simultaneous risks are considered, especially when marginal expected disutility of risk is decreasing nonlinearly with risk probabilities.Preference researchers should calculate SMARTs in any discrete-choice study in which 2 or more adverse-event risks are presented, particularly if risk preferences are nonlinear.

Patient Preferences for Waiting Time and Kidney Quality.

BACKGROUND AND OBJECTIVES: Approximately 20% of deceased donor kidneys are discarded each year in the United States. Some of these kidneys could benefit patients who are waitlisted. Understanding patient preferences regarding accepting marginal-quality kidneys could help more of the currently discarded kidneys be transplanted. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: This study uses a discrete choice experiment that presents a deceased donor kidney to patients who are waiting for, or have received, a kidney transplant. The choices involve trade-offs between accepting a kidney today or a future kidney. The options were designed experimentally to quantify the relative importance of kidney quality (expected graft survival and level of kidney function) and waiting time. Choices were analyzed using a random-parameters logit model and latent-class analysis. RESULTS: In total, 605 participants completed the discrete choice experiment. Respondents made trade-offs between kidney quality and waiting time. The average respondent would accept a kidney today, with 6.5 years of expected graft survival (95% confidence interval, 5.9 to 7.0), to avoid waiting 2 additional years for a kidney, with 11 years of expected graft survival. Three patient-preference classes were identified. Class 1 was averse to additional waiting time, but still responsive to improvements in kidney quality. Class 2 was less willing to accept increases in waiting time for improvements in kidney quality. Class 3 was willing to accept increases in waiting time even for small improvements in kidney quality. Relative to class 1, respondents in class 3 were likely to be age ≤61 years and to be waitlisted before starting dialysis, and respondents in class 2 were more likely to be older, Black, not have a college degree, and have lower Karnofsky performance status. CONCLUSIONS: Participants preferred accepting a lower-quality kidney in return for shorter waiting time, particularly those who were older and had lower functional status.

Accounting for Preference Heterogeneity in Discrete-Choice Experiments: An ISPOR Special Interest Group Report.

OBJECTIVES: Discrete choice experiments (DCEs) are increasingly used to elicit preferences for health and healthcare. Although many applications assume preferences are homogenous, there is a growing portfolio of methods to understand both explained (because of observed factors) and unexplained (latent) heterogeneity. Nevertheless, the selection of analytical methods can be challenging and little guidance is available. This study aimed to determine the state of practice in accounting for preference heterogeneity in the analysis of health-related DCEs, including the views and experiences of health preference researchers and an overview of the tools that are commonly used to elicit preferences. METHODS: An online survey was developed and distributed among health preference researchers and nonhealth method experts, and a systematic review of the DCE literature in health was undertaken to explore the analytical methods used and summarize trends. RESULTS: Most respondents (n = 59 of 70, 84%) agreed that accounting for preference heterogeneity provides a richer understanding of the data. Nevertheless, there was disagreement on how to account for heterogeneity; most (n = 60, 85%) stated that more guidance was needed. Notably, the majority (n = 41, 58%) raised concern about the increasing complexity of analytical methods. Of the 342 studies included in the review, half (n = 175, 51%) used a mixed logit with continuous distributions for the parameters, and a third (n = 110, 32%) used a latent class model. CONCLUSIONS: Although there is agreement about the importance of accounting for preference heterogeneity, there are noticeable disagreements and concerns about best practices, resulting in a clear need for further analytical guidance.

Primary Immune Deficiency: Patients' Preferences for Replacement Immunoglobulin Therapy.

Purpose: Immunoglobulin (Ig) replacement therapy is an important life-saving treatment modality for patients with primary antibody immune deficiency disorders (PAD). IVIG and SCIg are suitable alternatives to treat patients with PAD but vary in key ways. Existing evidence on patient preferences for Ig treatments given the complexities associated with IVIG and SCIg treatment is limited and fails to account for variations in preferences across patients. For this reason, we sought to evaluate PAD patient preferences for features of IVIG and SCIg across different patient characteristics. Materials and Methods: 119 PAD patients completed a discrete-choice experiment (DCE) survey. The DCE asked respondents to make choices between carefully constructed treatment alternatives described in terms of generic treatment features. Choices from the DCE were analyzed to determine the relative influence of attribute changes on treatment preferences. We used subgroup analysis to evaluate systematic variations in preferences by patients' age, gender, time since diagnosis, and treatment experience. Results: Patients were primarily concerned about the duration of treatment side effects, but preferences were heterogeneous. This was particularly true around administration features. Time since diagnosis was associated with an increase in patients' concerns with the number of needles required per infusion. Also, patients appear to prefer the kind of therapy they are currently using which could be the result of properly aligned patient preferences or evidence of patient adaptive behavior. Conclusions: Heterogeneity in preferences for Ig replacement treatments suggests that a formal shared decision making process could have an important role in improving patient care.

Quantifying Benefit-Risk Preferences for Heart Failure Devices: A Stated-Preference Study.

BACKGROUND: Regulatory and clinical decisions involving health technologies require judgements about relative importance of their expected benefits and risks. We sought to quantify heart-failure patients' acceptance of therapeutic risks in exchange for improved effectiveness with implantable devices. METHODS: Individuals with heart failure recruited from a national web panel or academic medical center completed a web-based discrete-choice experiment survey in which they were randomized to one of 40 blocks of 8 experimentally controlled choice questions comprised of 2 device scenarios and a no-device scenario. Device scenarios offered an additional year of physical functioning equivalent to New York Heart Association class III or a year with improved (ie, class II) symptoms, or both, with 30-day mortality risks ranging from 0% to 15%, in-hospital complication risks ranging from 0% to 40%, and a remote adjustment device feature. Logit-based regression models fit participants' choices as a function of health outcomes, risks and remote adjustment. RESULTS: Latent-class analysis of 613 participants (mean age, 65; 49% female) revealed that two-thirds were best represented by a pro-device, more risk-tolerant class, accepting up to 9% (95% CI, 7%-11%) absolute risk of device-associated mortality for a one-year gain in improved functioning (New York Heart Association class II). Approximately 20% were best represented by a less risk-tolerant class, accepting a maximum device-associated mortality risk of 3% (95% CI, 1%-4%) for the same benefit. The remaining class had strong antidevice preferences, thus maximum-acceptable risk was not calculated. CONCLUSIONS: Quantitative evidence on benefit-risk tradeoffs for implantable heart-failure device profiles may facilitate incorporating patients' views during product development, regulatory decision-making, and clinical practice.

Quantifying Value of Hope.

BACKGROUND: 'Hope' is a construct in patient-centered value frameworks, but few studies have attempted to measure the value of hope separately from treatment-related gains in quality of life and survival to support its application in economic evaluation. OBJECTIVE: To generate quantitative information on the "value of hope". METHODS: We designed a discrete-choice experiment in which treatment alternatives varied the probability of achieving 10-year survival, expected survival as the weighted sum of short-term and long-term survival, health status, and out-of-pocket cost. Two-hundred patients with cancer or history of cancer recruited by Cancer Support Community each completed 10 choice questions. We used mixed-logit and latent-class models to analyze the choice data. RESULTS: Relative to fixed survival periods of two, three or five years with 0% chance of 10-year survival, participants positively valued treatments with 5% and 10% chances of 10-year survival. However, participants negatively valued a 20% chance of 10-year survival that required an offsetting 80% chance of shorter survival. This finding was particularly strong when expected survival was two years. Compared to a 0% chance, dollar-equivalent values of 5% and 10% chances of long-term survival were $5,975 and $12,421, respectively, independent of health status or expected survival. The corresponding value for 20% versus 0% chance of long-term survival was negative. Latent-class analysis revealed 4 groups with distinct preference patterns. CONCLUSIONS: Our findings affirm positive value for hope independent of expected survival and health status. However, this finding does not universally hold in all situations nor across all groups.

Who Would Pay Higher Taxes for Better Mental Health? Results of a Large-Sample National Choice Experiment.

Policy Points  Public funding for mental health programs must compete with other funding priorities in limited state budgets.  Valuing state-funded mental health programs in a policy-relevant context requires consideration of how much benefit from other programs the public is willing to forgo to increase mental health program benefits and how much the public is willing to be taxed for such program benefits.  Taxpayer resistance to increased taxes to pay for publicly funded mental health programs and perceived benefits of such programs vary with state population size.  In all states, taxpayers seem to support increased public funding for mental health programs such as state Medicaid services, suggesting such programs are underfunded from the perspective of the average taxpayer. CONTEXT: The direct and indirect impacts of serious mental illness (SMI) on health care systems and communities represents a significant burden. However, the value that community members place on alleviating this burden is not known, and SMI treatment must compete with a long list of other publicly funded priorities. This study defines the value of public mental health interventions as what the public would accept, either in the form of higher taxes or in reductions in nonhealth programs, in return for increases in the number of mental health program beneficiaries. METHODS: We developed and fielded a best-practice discrete-choice experiment survey to quantify respondents' willingness to be taxed for increased spending among several competing programs, including a program for treating severe mental health conditions. A realistic decision frame was used to elicit respondents' willingness to support expanded state budgets for mental health programs if that expansion required either cuts in the competing publicly financed programs or tax increases. The survey was administered to a general population national sample of 10,000 respondents. FINDINGS: Nearly half the respondents in our sample either chose "no budget increase" for all budget scenarios or had preferences that were too disordered to estimate trade-off values. Including zero values for those respondents, we found that the mean (median) amount that all respondents were willing to be taxed annually for public mental health programs ranged between $156 ($99) per year for large-population states and $343 ($181) per year for small-population states. Respondents would accept reductions of between 1.6 and 3.4 beneficiaries in other programs in return for 1 additional mental health program beneficiary. CONCLUSIONS: Our results are consistent with findings that a substantial portion of the US public is unwilling to pay higher taxes. Nevertheless, even including the substantial number of respondents who opposed any tax increase, the willingness of both the mean and median respondent to be taxed for mental health program expansions implies that programs providing mental health services such as state Medicaid are underfunded.

The preferences of women with ovarian cancer for oral versus intravenous recurrence regimens.

OBJECTIVE: To assess preferences of women with ovarian cancer regarding features of available anti-cancer regimens for platinum-resistant, biomarker-positive disease, with an emphasis on oral PARP inhibitor and standard intravenous (IV) chemotherapy regimens. METHODS: A discrete-choice-experiment preferences survey was designed, tested, and administered to women with ovarian cancer, with 11 pairs of treatment profiles defined using seven attributes (levels/ranges): regimen (oral daily, IV weekly, IV monthly); probability of progression-free (PFS) at 6 months (40%-60%); probability of PFS at 2 years (10%-20%); nausea (none, moderate); peripheral neuropathy (none, mild, moderate); memory problems (none, mild); and total out-of-pocket cost ($0 to $10,000). RESULTS: Of 123 participants, 38% had experienced recurrence, 25% were currently receiving chemotherapy, and 18% were currently taking a PARP inhibitor. Given attributes and levels, the relative importance weights (sum 100) were: 2-year PFS, 28; cost, 27; 6-month PFS, 19; neuropathy,14; memory problems, nausea, and regimen, all ≤5. To accept moderate neuropathy, participants required a 49% (versus 40%) chance of PFS at 6 months or 14% (versus 10%) chance at 2 years. Given a 3-way choice where PFS and cost were equal, 49% preferred a monthly IV regimen causing mild memory problems, 47% preferred an oral regimen causing moderate nausea, and 4% preferred a weekly IV regimen causing mild memory and mild neuropathy. CONCLUSIONS: These findings challenge the assumption that oral anti-cancer therapies are universally preferred by patients and demonstrate that there is no "one size fits all" regimen that is preferable to women with ovarian cancer when considering recurrence treatment regimens.

Heterogeneity in Parent Preferences for Peanut Desensitization Therapy.

BACKGROUND: Recently developed peanut desensitization treatment reduces the incidence of allergic reactions, the anxiety associated with the risk of accidental exposure, and the burden of precautionary behavior. Eliciting parent preferences for tradeoffs involving treatment effectiveness, tolerability, costs, and convenience quantifies the burden of juvenile peanut allergy and the perceived value of peanut desensitization therapies. OBJECTIVE: To understand heterogeneity in parents' treatment preferences and the role of personal characteristics in explaining differences. METHODS: An Internet-based, discrete-choice experiment survey was administered to a national sample of 500 parents of children aged 4 to 17 years with peanut allergy to quantify parents' preferences for peanut desensitization therapies for their children. Latent-class, mixed-logit analysis estimated relative importance coefficients for groups of participants with distinctly different preferences. RESULTS: Parents' choice patterns fell into 1 of 4 preference subgroups: (1) Cost-sensitive, (2) Protreatment (but Side-Effect-Averse), (3) Trader, and (4) Inconsistent. Mode of administration had little relative importance across all subgroups. Characteristics associated with belonging to a given preference subgroup included parent age, child age, income, parent perception of child risk and ability to manage allergic reactions, past allergic reactions, and changes in precautionary behaviors posttreatment. CONCLUSIONS: We found distinct differences in parent preferences for tradeoffs involving effectiveness, tolerability, and costs of peanut desensitization treatments. Parents' treatment preferences help quantify the burden of juvenile peanut allergy and the perceived value of new therapies. Such information can inform patient-centric clinical and regulatory decision making.

The Impact of the Risk Functional Form Assumptions on Maximum Acceptable Risk Measures.

BACKGROUND: Discrete-choice experiments (DCEs) are increasingly conducted to quantify risk tolerance by computing maximum acceptable risk (MAR) for improvements in efficacy or other benefits gained from new medical treatments. To compute MARs from DCE data, respondents are asked to make choices under uncertainty between treatments. Specific treatment-related harms are included in the choice questions as probabilistic adverse events (AEs), and choice variation with the probability of these outcomes is assumed to indicate their effect on the expected utility of treatments. With a limited number of comparisons between profiles, calculation of MARs requires understanding how outcome probabilities that are not explicitly considered in the DCE can change the value of medical technologies. This study aims to examine how various assumptions on the expected disutility of these excluded probabilities can result in different MAR measures. METHODS: We summarize commonly used empirical specifications for the expected disutility of AEs and derive the resulting MAR functions. We then discuss an empirical application on treatments to delay bone metastases in oncology patients with solid tumors. RESULTS: A total of 187 respondents completed the DCE. Results show the impact of making various assumptions about the expected disutility of AEs, and the resulting MAR values for specific health benefits. As expected, different assumptions resulted in variations in MAR values for specific health benefits. Even with small differences in MAR measures, our results suggest that the assumptions evaluated here can lead to different conclusions about the acceptability of a medical technology. CONCLUSION: Results show possible systematic variations in MARs caused by the assumed form of the effect of changes in the probability of AEs. Furthermore, we find that different assumptions can lead to different conclusions about the acceptability of a medical technology, even when MAR distributions overlap. This result suggests that researchers should evaluate the assumptions they are making for these effects and use sensitivity analysis to evaluate the robustness of risk-tolerance measures from stated-preference data.

Patient Preferences for Outcomes Following DCIS Management Strategies: A Discrete Choice Experiment.

PURPOSE: Ductal carcinoma in situ (DCIS), a nonobligate precursor of breast cancer, is often aggressively managed with multimodal therapy. However, there is limited research on patients' preferences for trade-offs among treatment-related outcomes such as breast appearance, side effects, and future cancer risk. We sought to investigate whether women consider treatment features aside from cancer risk when making treatment choices for ductal carcinoma in situ and if so, to what degree other features influence these decisions. METHODS: A discrete choice experiment was administered to participants in a comprehensive cancer screening mammography clinic. The experimental design was used to generate constructed health profiles resulting from different management strategies. Health profiles were defined by breast appearance, severity of infection within the first year, chronic pain, hot flashes, and risk of developing or dying from breast cancer within 10 years. RESULTS: One hundred ninety-four women without a personal history of breast cancer completed the choice task. Across 10 choice questions, 29% always selected the health profile with a lower risk of invasive breast cancer (ie, dominated on cancer risk), regardless of the effects of treatment. For nonrisk dominators, breast cancer risk remained the most important factor but was closely followed by chronic pain (24% [95% CI, 20 to 28]) and infection (22% [95% CI, 18 to 25]). Depending on treatment outcomes, the tolerable increase in breast cancer risk was as high as 3.4%. CONCLUSION: Most women were willing to make some trade-offs between invasive cancer risk and treatment-related outcomes. Our findings highlight the importance of shared decision-making weighing risks and benefits between patient and provider management of low-risk disease.

Surgical treatment for recurrent shoulder instability: factors influencing surgeon decision making.

BACKGROUND: The optimal surgical approach for recurrent anterior shoulder instability remains controversial, particularly in the face of glenoid and/or humeral bone loss. The purpose of this study was to use a contingent-behavior questionnaire (CBQ) to determine which factors drive surgeons to perform bony procedures over soft tissue procedures to address recurrent anterior shoulder instability. METHODS: A CBQ survey presented each respondent with 32 clinical vignettes of recurrent shoulder instability that contained 8 patient factors. The factors included (1) age, (2) sex, (3) hand dominance, (4) number of previous dislocations, (5) activity level, (6) generalized laxity, (7) glenoid bone loss, and (8) glenoid track. The survey was distributed to fellowship-trained surgeons in shoulder/elbow or sports medicine. Respondents were asked to recommend either a soft tissue or bone-based procedure, then specifically recommend a type of procedure. Responses were analyzed using a multinomial-logit regression model that quantified the relative importance of the patient characteristics in choosing bony procedures. RESULTS: Seventy orthopedic surgeons completed the survey, 33 were shoulder/elbow fellowship trained and 37 were sports medicine fellowship trained; 52% were in clinical practice ≥10 years and 48% <10 years; and 95% reported that the shoulder surgery made up at least 25% of their practice. There were 53% from private practice, 33% from academic medicine, and 14% in government settings. Amount of glenoid bone loss was the single most important factor driving surgeons to perform bony procedures over soft tissue procedures, followed by the patient age (19-25 years) and the patient activity level. The number of prior dislocations and glenoid track status did not have a strong influence on respondents' decision making. Twenty-one percent glenoid bone loss was the threshold of bone loss that influenced decision toward a bony procedure. If surgeons performed 10 or more open procedures per year, they were more likely to perform a bony procedure. CONCLUSION: The factors that drove surgeons to choose bony procedures were the amount of glenoid bone loss with the threshold at 21%, patient age, and their activity demands. Surprisingly, glenoid track status and the number of previous dislocations did not strongly influence surgical treatment decisions. Ten open shoulder procedures a year seems to provide a level of comfort to recommend bony treatment for shoulder instability.