Background
Patients have a unique role in deciding what treatments should be available for them and regulatory agencies should take their preferences into account when making treatment approval ...decisions. This is the first study designed to obtain quantitative patient-preference evidence to inform regulatory approval decisions by the Food and Drug Administration Center for Devices and Radiological Health.
Methods
Five-hundred and forty United States adults with body mass index (BMI) ≥30 kg/m
2
evaluated tradeoffs among effectiveness, safety, and other attributes of weight-loss devices in a scientific survey. Discrete-choice experiments were used to quantify the importance of safety, effectiveness, and other attributes of weight-loss devices to obese respondents. A tool based on these measures is being used to inform benefit-risk assessments for premarket approval of medical devices.
Results
Respondent choices yielded preference scores indicating their relative value for attributes of weight-loss devices in this study. We developed a tool to estimate the minimum weight loss acceptable by a patient to receive a device with a given risk profile and the maximum mortality risk tolerable in exchange for a given weight loss. For example, to accept a device with 0.01 % mortality risk, a risk tolerant patient will require about 10 % total body weight loss lasting 5 years.
Conclusions
Patient preference evidence was used make regulatory decision making more patient-centered. In addition, we captured the heterogeneity of patient preferences allowing market approval of effective devices for risk tolerant patients. CDRH is using the study tool to define minimum clinical effectiveness to evaluate new weight-loss devices. The methods presented can be applied to a wide variety of medical products. This study supports the ongoing development of a guidance document on incorporating patient preferences into medical-device premarket approval decisions.
Abstract Conjoint analysis is a stated-preference survey method that can be used to elicit responses that reveal preferences, priorities, and the relative importance of individual features associated ...with health care interventions or services. Conjoint analysis methods, particularly discrete choice experiments (DCEs), have been increasingly used to quantify preferences of patients, caregivers, physicians, and other stakeholders. Recent consensus-based guidance on good research practices, including two recent task force reports from the International Society for Pharmacoeconomics and Outcomes Research, has aided in improving the quality of conjoint analyses and DCEs in outcomes research. Nevertheless, uncertainty regarding good research practices for the statistical analysis of data from DCEs persists. There are multiple methods for analyzing DCE data. Understanding the characteristics and appropriate use of different analysis methods is critical to conducting a well-designed DCE study. This report will assist researchers in evaluating and selecting among alternative approaches to conducting statistical analysis of DCE data. We first present a simplistic DCE example and a simple method for using the resulting data. We then present a pedagogical example of a DCE and one of the most common approaches to analyzing data from such a question format—conditional logit. We then describe some common alternative methods for analyzing these data and the strengths and weaknesses of each alternative. We present the ESTIMATE checklist, which includes a list of questions to consider when justifying the choice of analysis method, describing the analysis, and interpreting the results.
Abstract Background The application of conjoint analysis (including discrete-choice experiments and other multiattribute stated-preference methods) in health has increased rapidly over the past ...decade. A wider acceptance of these methods is limited by an absence of consensus-based methodological standards. Objective The International Society for Pharmacoeconomics and Outcomes Research (ISPOR) Good Research Practices for Conjoint Analysis Task Force was established to identify good research practices for conjoint-analysis applications in health. Methods The task force met regularly to identify the important steps in a conjoint analysis, to discuss good research practices for conjoint analysis, and to develop and refine the key criteria for identifying good research practices. ISPOR members contributed to this process through an extensive consultation process. A final consensus meeting was held to revise the article using these comments, and those of a number of international reviewers. Results Task force findings are presented as a 10-item checklist covering: 1) research question; 2) attributes and levels; 3) construction of tasks; 4) experimental design; 5) preference elicitation; 6) instrument design; 7) data-collection plan; 8) statistical analyses; 9) results and conclusions; and 10) study presentation. A primary question relating to each of the 10 items is posed, and three sub-questions examine finer issues within items. Conclusions Although the checklist should not be interpreted as endorsing any specific methodological approach to conjoint analysis, it can facilitate future training activities and discussions of good research practices for the application of conjoint-analysis methods in health care studies.
Abstract Background Treatment decisions for advanced non-small cell lung cancer (NSCLC) are complex and require trade-offs between the benefits and risks experienced by patients. We evaluated the ...benefits that patients judged sufficient to compensate for the risks associated with therapy for NSCLC. Methods Participants with a self-reported diagnosis of NSCLC ( n = 100) were sampled from an online panel in the United Kingdom. Eligible and consenting participants then completed a self-administered online survey about their disease and their treatment preferences were assessed. This involved respondents choosing among systematically paired profiles that spanned eight attributes: progression-free survival PFS, symptom severity, rash, diarrhoea, fatigue, nausea and vomiting, fever and infection, and mode of treatment administration (infusion and oral). A choice model was estimated using mixed-logit regression. Estimates of importance for each attribute level and attribute were then calculated and acceptable tradeoffs among attributes were explored. Results A total of 89 respondents (73% male) completed all choice tasks appropriately. Increases in PFS together with improvements in symptom severity were judged most important and increased with PFS benefit – 4 months: 5.7; 95% CI: 3.5–7.9; 5 months: 7.1; 95% CI: 4.4–9.9; and 7 months: 10.0; 95% CI: 6.1–13.9. However, improvements in PFS were viewed as most beneficial when disease symptoms were mild and as detrimental when patients had severe symptoms. Fatigue (5.0; 95% CI: 2.7–7.3) was judged to be the most important risk, followed by diarrhoea (2.8; 95% CI: 0.7–4.9), nausea and vomiting (2.1; 95% CI: 0.1–4.1), fever and infection (2.1; 95% CI: 0.2–4.1), and rash (2.0; 95% CI: 0.2–3.9). Oral administration was preferred to infusion (1.8; 95% CI: 0.0–3.6). Patients with mild and moderate symptoms traded PFS for less risks or more convenience if the severe symptoms were not experienced. Conclusion This study demonstrates the value of conjoint analysis in the study of patient preferences for cancer treatments. In this small sample of patients with NSCLC from the UK, we demonstrate that the value of improvements in PFS is conditional upon the severity of disease symptoms; and that risks are valued differently.
Objective:
Understanding patients' preferences for attributes of injectable antihyperglycemic regimens may improve patient satisfaction and medication adherence. Our objective was to quantify the ...preferences of patients with type 2 diabetes mellitus (T2DM) for reducing the frequency of glucagon-like peptide-1 receptor agonist injections from once daily to once weekly.
Methods:
A total of 643 respondents with a self-reported physician diagnosis of type 2 diabetes completed a web-based discrete-choice experiment survey. The sample included four prespecified subgroups: currently using exenatide once weekly (n = 150), liraglutide once daily (n = 153), insulin (but not exenatide once weekly or liraglutide) (n = 156), and no injectable treatment (n = 184). Device attributes included type of injection device, needle size and pain, injection frequency, refrigeration, and injection-site reactions. Random-parameters logit was used to estimate the relative impact of device attributes on treatment choice for each subgroup.
Results:
In all subgroups, changing injection frequency from daily to weekly (independent of the effect of injection frequency on preferences for other attributes) was the most important predictor of treatment choice. Switching from a longer and thicker needle to a shorter and thinner needle and eliminating injection-site reactions were also statistically significant predictors of device choice (P < 0.05). Exenatide once weekly users and those not currently using injections were more likely to choose a treatment with characteristics similar to exenatide once weekly.
Conclusions:
The treatment attribute most important to patients choosing among hypothetical injectable treatments for T2DM was injection frequency: patients preferred weekly over daily injections.
Limitations:
The primary limitations of this study are that it included only a limited number of attributes that may not reflect the full complexity of patient choices, diagnosis was self-reported, and patients were recruited from an Internet panel and may not be representative of the T2DM patient population.
Abstract Background: In response to 2012 guidance in which the US Food and Drug Administration’s (FDA) Center for Devices and Radiological Health (CDRH) stated the importance of patient-centric ...measures in regulatory benefit-risk assessments, the Medical Device Innovation Consortium (MDIC) initiated a project. The project was used to develop a framework to help the Food and Drug Administration (FDA) and industry sponsors understand how patient preferences regarding benefit and risk might be integrated into the review of innovative medical devices. Methods: A public-private partnership of experts from medical device industry, government, academia and non-profits collaborated on development of the MDIC patient centered benefit-risk framework. Results: The MDIC Framework examines what patient preference information is and the potential use and value of patient preference information in the regulatory process and across the product development life cycle. The MDIC Framework also includes a catalog of patient preference assessment methods and an agenda for future research to advance the field. Conclusions: This article discusses key concepts in patient preference assessment of particular importance for regulators and researchers that are addressed in the MDIC Framework for patient centered benefit-risk assessment as well as the unique public-private collaboration that led its development.
Decisions regarding the development, regulation, sale, and utilization of pharmaceutical and medical interventions require an evaluation of the balance between benefits and risks. Such evaluations ...are subject to two fundamental challenges—measuring the clinical effectiveness and harms associated with the treatment, and determining the relative importance of these different types of outcomes. In some ways, determining the willingness to accept treatment-related risks in exchange for treatment benefits is the greater challenge because it involves the individual subjective judgments of many decision makers, and these decision makers may draw different conclusions about the optimal balance between benefits and risks. In response to increasing demand for benefit–risk evaluations, researchers have applied a variety of existing welfare-theoretic preference methods for quantifying the tradeoffs decision makers are willing to accept among expected clinical benefits and risks. The methods used to elicit benefit–risk preferences have evolved from different theoretical backgrounds. To provide some structure to the literature that accommodates the range of approaches, we begin by describing a welfare-theoretic conceptual framework underlying the measurement of benefit–risk preferences in pharmaceutical and medical treatment decisions. We then review the major benefit–risk preference-elicitation methods in the empirical literature and provide a brief overview of the studies using each of these methods. The benefit–risk preference methods described in this overview fall into two broad categories: direct-elicitation methods and conjoint analysis. Rating scales (6 studies), threshold techniques (9 studies), and standard gamble (2 studies) are examples of direct elicitation methods. Conjoint analysis studies are categorized by the question format used in the study, including ranking (1 study), graded pairs (1 study), and discrete choice (21 studies). The number of studies reviewed here demonstrates that this body of research already is substantial, and it appears that the number of benefit–risk preference studies in the literature will continue to increase. In addition, benefit–risk preference-elicitation methods have been applied to a variety of healthcare decisions and medical interventions, including pharmaceuticals, medical devices, surgical and medical procedures, and diagnostics, as well as resource-allocation decisions such as facility placement. While preference-elicitation approaches may differ across studies, all of the studies described in this review can be used to provide quantitative measures of the tradeoffs patients and other decision makers are willing to make between benefits and risks of medical interventions. Eliciting and quantifying the preferences of decision makers allows for a formal, evidence-based consideration of decision-makers’ values that currently is lacking in regulatory decision making. Future research in this area should focus on two primary issues—developing best-practice standards for preference-elicitation studies and developing methods for combining stated preferences and clinical data in a manner that is both understandable and useful to regulatory agencies.
Objective
Although clinical guidelines recommend administration of pegfilgrastim 1–4 days after a myelosuppressive chemotherapy cycle to decrease the incidence of febrile neutropenia (FN), some ...physicians administer pegfilgrastim on the same day as chemotherapy administration. A novel on-body injector (OBI) that automatically delivers pegfilgrastim the day after chemotherapy is also available. Our objective was to estimate patient and physician preferences among the pegfilgrastim administration options.
Methods
We conducted a cross-sectional survey of patients receiving pegfilgrastim and physicians prescribing pegfilgrastim. Respondents’ preferences for pegfilgrastim administration options were elicited using direct elicitation; the relative importance of features associated with the options was estimated in a point-allocation exercise. Physicians considered two hypothetical patient profiles when completing the exercises.
Results
The samples included 200 patients and 200 physicians. Patients generally preferred the administration option with which they had experience. Among patients, 48.5% with previous in-clinic injections 24 hours after chemotherapy preferred this option; 56.8% with previous OBI administration preferred this option. For a clinically compromised patient, 37.5% of physicians preferred an in-clinic injection option; 49.5% preferred the OBI. For a less compromised patient, 55.5% preferred an in-clinic injection option; 28.0% preferred the OBI. Avoiding the need to return to the clinic was chosen most often as the most important treatment feature for patients and physicians.
Conclusions
Patients and physicians identified that returning clinic visits for pegfilgrastim administration may be burdensome. A potential solution to mitigate this burden is the OBI, which allows adherence to the labeled use of pegfilgrastim without return visits to the clinic.
To identify meaningful treatment attributes and quantify patient preferences for attributes of systemic atopic dermatitis (AD) treatments.
Qualitative interviews were conducted with adults with ...moderate-to-severe AD (N = 21) to identify AD treatment attributes that patients consider most important and inform attribute selection for an online discrete-choice experiment (DCE) survey administered to patients in the United States with moderate-to-severe AD. Participants identified probability of clear/almost clear skin at 16 weeks, time to itch relief, mode of administration, and safety risks as very important. DCE data were analyzed using a random-parameters logit model to estimate the relative importance of treatment attributes and maximum acceptable risk.
A total of 320 respondents completed the DCE survey (74% female; mean age, 35 years). Annual risk of malignancy was the most important attribute, followed by mode of administration, probability of clear skin at 16 weeks, and time to onset of itch relief. Respondents preferred daily oral treatment over injectable treatment. Respondents were willing to accept increases in adverse event risks for improvements in efficacy and mode of administration.
The findings of this study can help inform joint patient-physician decision making in managing moderate-to-severe AD.
Stated-preference methods provide a systematic approach to quantitatively assess the relative preferences for features of cancer screening tests. We reviewed stated-preference studies for breast, ...cervical, and colorectal cancer screening to identify the types of attributes included, the use of questions to assess uptake, and whether gaps exist in these areas. The goal of our review is to inform research on the design and promotion of public health programs to increase cancer screening.
Using the PubMed and EconLit databases, we identified studies published in English from January 1990 through July 2013 that measured preferences for breast, cervical, and colorectal cancer screening test attributes using conjoint analysis or a discrete-choice experiment. We extracted data on study characteristics and results. We categorized studies by whether attributes evaluated included screening test, health care delivery characteristics, or both.
Twenty-two studies met the search criteria. Colorectal cancer was the most commonly studied cancer of the 3. Fifteen studies examined only screening test attributes (efficacy, process, test characteristics, and cost). Two studies included only health care delivery attributes (information provided, staff characteristics, waiting time, and distance to facility). Five studies examined both screening test and health care delivery attributes. Overall, cancer screening test attributes had a significant effect on a patient's selection of a cancer screening test, and health care delivery attributes had mixed effects on choice.
A growing number of studies examine preferences for cancer screening tests. These studies consistently find that screening test attributes, such as efficacy, process, and cost, are significant determinants of choice. Fewer studies have examined the effect of health care delivery attributes on choice, and the results from these studies are mixed. There is a need for additional studies on the barriers to cancer screening uptake, including health care delivery attributes, and the effect of education materials on preferences.
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