Pediatricians' preferences for infant meningococcal vaccination.

BACKGROUND: Meningococcal disease is rare but can cause death or disabilities. Although the Advisory Committee on Immunization Practices has recommended meningococcal vaccination for at-risk children aged 9 through 23 months, it has not endorsed universal vaccination. Health insurance payments for the vaccination of children who are not at risk are likely to be limited. Use of infant meningococcal vaccines by these families will thus depend on the preferences of physicians who might recommend vaccination to parents, as well as parents' preferences. OBJECTIVE: To quantify pediatricians' preferences for specific features of hypothetical infant meningococcal vaccines. METHODS: A sample of pediatricians (n = 216) completed a Web-enabled, discrete choice experiment survey in which respondents chose between pairs of hypothetical vaccines in a series of trade-off questions. The questions described vaccines with six attributes. A random-parameters logit regression model was used to estimate the relative importance weights physicians place on vaccine features. These weights were used to calculate the predicted probability that a physician chooses hypothetical vaccines with given characteristics. RESULTS: Pediatricians' choices indicated that increases in vaccine effectiveness were among the most important factors in their vaccine recommendations, followed by increases in the number of injections. The age at which protection begins and the number of additional office visits were less important. Whether a booster was required after 5 years was the least important factor in vaccine recommendations. The results suggest that virtually all (99.9%) physicians in the sample would recommend a vaccine even with the least-preferred features rather than no infant meningococcal vaccine. CONCLUSIONS: Physicians' responses indicate a strong preference for infant meningococcal vaccination.

Quantifying benefit-risk preferences for medical interventions: an overview of a growing empirical literature.

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.

Constructing experimental designs for discrete-choice experiments: report of the ISPOR Conjoint Analysis Experimental Design Good Research Practices Task Force.

Stated-preference methods are a class of evaluation techniques for studying the preferences of patients and other stakeholders. While these methods span a variety of techniques, conjoint-analysis methods-and particularly discrete-choice experiments (DCEs)-have become the most frequently applied approach in health care in recent years. Experimental design is an important stage in the development of such methods, but establishing a consensus on standards is hampered by lack of understanding of available techniques and software. This report builds on the previous ISPOR Conjoint Analysis Task Force Report: Conjoint Analysis Applications in Health-A Checklist: A Report of the ISPOR Good Research Practices for Conjoint Analysis Task Force. This report aims to assist researchers specifically in evaluating alternative approaches to experimental design, a difficult and important element of successful DCEs. While this report does not endorse any specific approach, it does provide a guide for choosing an approach that is appropriate for a particular study. In particular, it provides an overview of the role of experimental designs for the successful implementation of the DCE approach in health care studies, and it provides researchers with an introduction to constructing experimental designs on the basis of study objectives and the statistical model researchers have selected for the study. The report outlines the theoretical requirements for designs that identify choice-model preference parameters and summarizes and compares a number of available approaches for constructing experimental designs. The task-force leadership group met via bimonthly teleconferences and in person at ISPOR meetings in the United States and Europe. An international group of experimental-design experts was consulted during this process to discuss existing approaches for experimental design and to review the task force's draft reports. In addition, ISPOR members contributed to developing a consensus report by submitting written comments during the review process and oral comments during two forum presentations at the ISPOR 16th and 17th Annual International Meetings held in Baltimore (2011) and Washington, DC (2012).

An experiment on simplifying conjoint analysis designs for measuring preferences.

In conjoint analysis (CA) studies, choosing between scenarios with multiple health attributes may be demanding for respondents. This study examined whether simplifying the choice task in CA designs, by using a design with more overlap of attribute levels, provides advantages over standard minimal-overlap methods. Two experimental conditions, minimal and increased-overlap discrete choice CA designs, were administered to 353 respondents as part of a larger HIV testing preference survey. In the minimal-overlap survey, all six attribute levels were allowed to vary. In the increased-overlap survey, an average of two attribute levels were the same between each set of scenarios. We hypothesized that the increased-overlap design would reduce cognitive burden, while minimally impacting statistical efficiency. We did not find any significant improvement in consistency, willingness to trade, perceived difficulty, fatigue, or efficiency, although several results were in the expected direction. However, evidence suggested that there were differences in stated preferences. The results increase our understanding of how respondents answer CA questions and how to improve future surveys.