A. Brett Hauber

Conjoint analysis applications in health - A checklist: A report of the ISPOR Good Research Practices for Conjoint Analysis Task Force

BACKGROUNDnThe 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.nnnOBJECTIVEnThe 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.nnnMETHODSnThe 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.nnnRESULTSnTask 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.nnnCONCLUSIONSnAlthough 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.

Statistical Methods for the Analysis of Discrete Choice Experiments: A Report of the ISPOR Conjoint Analysis Good Research Practices Task Force

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.

Spatial Boundaries and Choice Set Definition in a Random Utility Model of Recreation Demand

We are concerned with the definition of choice set used in Random Utility Models of recreation demand. In particular, we are concerned with the spatial boundaries used to define choice sets. In this paper, using a model of day-trip fishing in Maine, we examine the sensitivity of parameter and welfare estimates to changes in the spatial boundary. We find that there exists some threshold distance beyond which adding more sites to the choice set has negligible effects on the estimation results.

Factors that affect adherence to bipolar disorder treatments: A stated-preference approach

Background: Medication nonadherence is high among patients with bipolar disorder, and may lead to poor clinical outcomes, decreased quality of life, and increased resource utilization. Objective: To investigate the factors associated with nonadherence and to assess the effect of patient-stated preferences on stated adherence to hypothetical medications. Research Design: A choice-format stated-preference Web survey was administered. In each choice question, patients were asked to choose among 2 or 3 different hypothetical medications. Each choice question was followed by a question asking patients about their likely adherence to the selected medication alternative. Subjects: Patients (N = 469) with self-reported bipolar disorder completed the survey which was programmed and administered to members of a chronic-illness Web panel. Measures: Factors associated with stated adherence to current treatment were identified. The effects of socioeconomic characteristics and medication attributes on stated adherence to hypothetical medications were assessed. Results: Patient socioeconomic characteristics affect patients adherence. Being white and having more education has a significant positive effect on adherence. Self-reported current adherence is a strong factor in predicting adherence for better medications. Medication outcome attributes, especially severity of depressive episodes, strongly influence patients stated adherence to treatment. Weight gain and cognitive effects of a medication most significantly affected patients likely adherence to medications for bipolar disorder. Conclusions: Patients are the final health care decision makers; their satisfaction with a medication is likely to affect whether or not they adhere to the medication prescribed by their physician. In the case of bipolar disorder, this study suggests patients are likely to be more adherent to medications that reduce the severity of depressive episodes and do not cause weight gain or cognitive side effects. By understanding the factors that improve adherence, health care providers can optimize prescribing patterns, which may ultimately lead to more effective management and improvement in the patients condition.

Patients’ preferences for treatment outcomes for advanced non-small cell lung cancer: A conjoint analysis

BACKGROUNDnTreatment 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.nnnMETHODSnParticipants 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.nnnRESULTSnA 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.nnnCONCLUSIONnThis 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.

Potential Savings in the Cost of Caring for Alzheimer’s Disease

AbstractObjective: To estimate savings in the cost of caring for patientswith Alzheimer’s disease (AD) during 6 months, 1 year and 2 years of treatment with rivastigmine. An intermediate objective was to estimate the relationship between disease progression and institutionalisation.Design and setting: We assessed the relationship between Mini-Mental State Examination (MMSE) score and institutionalisation using a piecewise Cox proportional hazard model. To estimate cost savings from treatments lasting 6 months, 1 year and 2 years, estimates of the probability of institutionalisation were integrated with data from two 6-month phase III clinical trials of rivastigmine and a hazard model of disease progression.Main outcome measures and results: Our data suggest that savings in the overall cost of caring for patients with mild and moderate AD can be as high as

Hypothetical bias, cheap talk, and stated willingness to pay for health care

US4839 per patient after 2 years of treatment. Furthermore, the probability of institutionalisation increases steadily as MMSE score falls. Among our study individuals, age, race, level of education and marital status were significant predictors of institutionalisation, whereas gender had little effect.Conclusions: Using rivastigmine to treat AD results in a delay in disease progression for patients who begin treatment during the mild or moderate stages of the disease. By delaying the probability that a patient will be institutionalised, the cost of caring for AD patients can be significantly reduced.n

Incorporating patient-preference evidence into regulatory decision making.

Subjects with rheumatoid arthritis (RA) enrolled in an online panel were asked to evaluate pairs of treatment alternatives with different attributes. Half of the sample saw a cheap-talk text. Preference parameters were estimated using random-parameters logit models to account for unobserved taste heterogeneity. The models also were estimated in willingness-to-pay (WTP) space instead of conventional utility space. Cheap talk not only affected the coefficient on the cost attribute, but also preferences for other attributes. WTP estimates were generally lower in cheap talk sample, except for the most important attribute and a 2-level attribute. Subjects who were presented with cheap talk discriminated between the adjoning attribute levels better than the subjects in the control sample.

Savings in the cost of caring for patients with Alzheimer's disease in Canada: An analysis of treatment with rivastigmine

BackgroundPatients 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.MethodsFive-hundred and forty United States adults with body mass index (BMI)xa0≥30xa0kg/m2 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.ResultsRespondent 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 0.01xa0% mortality risk, a risk tolerant patient will require about 10xa0% total body weight loss lasting 5xa0years.ConclusionsPatient 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.

Quantifying Benefit–Risk Preferences for Medical Interventions: An Overview of a Growing Empirical Literature

OBJECTIVEnTo estimate per-patient potential cost savings using rivastigmine in the treatment of Alzheimers disease (AD) in Canada.nnnBACKGROUNDnIn recent years, new members of a class of pharmaceuticals known as cholinesterase inhibitors have been introduced for the treatment of patients with AD. Two recent studies conducted in the United Kingdom and the United States estimated potential cost savings from the new cholinesterase inhibitor rivastigmine. The present study combined the disease-progression model used in those 2 studies with Canadian costs to estimate per-patient potential savings resulting from the treatment of AD in Canada.nnnMETHODSnEfficacy data from 2 pivotal, phase III clinical trials of rivastigmine were used in a hazard model of disease progression to estimate long-term differences in cognitive functioning between patients receiving rivastigmine and patients receiving no treatment. We used the Mini-Mental State Examination (MMSE) score as our measure of disease progression. We also used Canadian costs of AD care, estimated as a function of MMSE score, to estimate cost savings experienced by treated patients compared with patients receiving no treatment. All costs and cost savings are presented in 1997 Canadian dollars. We used a societal perspective in this analysis.nnnRESULTSnRivastigmine was estimated to delay the transition to more severe stages of AD by up to 188 days for patients with mild AD after 2 years of treatment. For patients with mild-to-moderate and moderate disease, this delay was estimated to be 106 and 44 days, respectively. For patients with the mild stage of AD, estimated average daily cost savings (excluding the cost of rivastigmine) ranged from Can