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.

Health care decisions are complex and involve confronting trade-offs between multiple, often conflicting, objectives. Using structured, explicit approaches to decisions involving multiple criteria can improve the quality of decision making and a set of techniques, known under the collective heading multiple criteria decision analysis (MCDA), are useful for this purpose. MCDA methods are widely used in other sectors, and recently there has been an increase in health care applications. In 2014, ISPOR established an MCDA Emerging Good Practices Task Force. It was charged with establishing a common definition for MCDA in health care decision making and developing good practice guidelines for conducting MCDA to aid health care decision making. This initial ISPOR MCDA task force report provides an introduction to MCDA - it defines MCDA; provides examples of its use in different kinds of decision making in health care (including benefit risk analysis, health technology assessment, resource allocation, portfolio decision analysis, shared patient clinician decision making and prioritizing patients' access to services); provides an overview of the principal methods of MCDA; and describes the key steps involved. Upon reviewing this report, readers should have a solid overview of MCDA methods and their potential for supporting health care decision making.

Health care decisions are complex and involve confronting trade-offs between multiple, often conflicting objectives. Using structured, explicit approaches to decisions involving multiple criteria can improve the quality of decision making. A set of techniques, known under the collective heading, multiple criteria decision analysis (MCDA), are useful for this purpose. In 2014, ISPOR established an Emerging Good Practices Task Force. The task force's first report defined MCDA, provided examples of its use in health care, described the key steps, and provided an overview of the principal methods of MCDA. This second task force report provides emerging good-practice guidance on the implementation of MCDA to support health care decisions. The report includes: a checklist to support the design, implementation and review of an MCDA; guidance to support the implementation of the checklist; the order in which the steps should be implemented; illustrates how to incorporate budget constraints into an MCDA; provides an overview of the skills and resources, including available software, required to implement MCDA; and future research directions.

Abstract-A limited number of clinical studies have examined the effect of poststroke rehabilitation with robotic devices on hemiparetic arm function. We systematically reviewed the literature to assess the effect of robot-aided therapy on stroke patients' upper-limb motor control and functional abilities. Eight clinical trials were identified and reviewed. For four of these studies, we also pooled short-term mean changes in FuglMeyer scores before and after robot-aided therapy. We found that robot-aided therapy of the proximal upper limb improves short-and long-term motor control of the paretic shoulder and elbow in subacute and chronic patients; however, we found no consistent influence on functional abilities. In addition, robotaided therapy appears to improve motor control more than conventional therapy.

Objective: To investigate the impact of upper extremity deficit in subjects with tetraplegia. Setting: The United Kingdom and The Netherlands. Study design: Survey among the members of the Dutch and UK Spinal Cord Injury (SCI) Associations. Main outcome parameter: Indication of expected improvement in quality of life (QOL) on a 5-point scale in relation to improvement in hand function and seven other SCI-related impairments.Results: In all, 565 subjects with tetraplegia returned the questionnaire (overall response of 42%). Results in the Dutch and the UK group were comparable. A total of 77% of the tetraplegics expected an important or very important improvement in QOL if their hand function improved. This is comparable to their expectations with regard to improvement in bladder and bowel function. All other items were scored lower. Conclusion: This is the first study in which the impact of upper extremity impairment has been assessed in a large sample of tetraplegic subjects and compared to other SCI-related impairments that have a major impact on the life of subjects with SCI. The present study indicates a high impact as well as a high priority for improvement in hand function in tetraplegics.

Worldwide, billions of dollars are invested in medical product development and there is an increasing pressure to maximize the revenues of these investments. That is, governments need to be informed about the benefits of spending public resources, companies need more information to manage their product development portfolios and even universities may need to direct their research programmes in order to maximize societal benefits. Assuming that all medical products need to be adopted by the heavily regulated healthcare market at one point in time, it is worthwhile to look at the logic behind healthcare decision making, specifically, decisions on the coverage of medical products and decisions on the use of these products under competing and uncertain conditions. With the growing tension between leveraging economic growth through R&D spending on the one hand and stricter control of healthcare budgets on the other, several attempts have been made to apply the health technology assessment (HTA) methodology to earlier stages of technology development and implementation. For instance, horizon scanning was introduced to systematically assess emerging technologies in order to inform health policy. Others have introduced iterative economic evaluation, e.g. economic evaluations in earlier stages of clinical research. However, most of these methods are primarily intended to support governments in making decisions regarding potentially expensive new medical products. They do not really inform biomedical product developers on the probability of return on investment, nor do they inform about the market needs and specific requirements of technologies in development. It is precisely this aspect that increasingly receives attention, i.e. is it possible to use HTA tools and methods to inform biomedical product development and to anticipate further development and market access. Several methods have been used in previous decades, but have never been compiled in a comprehensive review. The main objective of this article was to provide an overview of previous work and methods in the field of early HTA, and to put these approaches in perspective through a conceptual framework introduced in this paper. A particular goal of the review was to familiarize decision makers with available techniques that can be employed in early-stage decision making, and to identify opportunities for further methodological growth in this emerging field of HTA.

Health care delivery systems are inherently complex, consisting of multiple tiers of interdependent subsystems and processes that are adaptive to changes in the environment and behave in a nonlinear fashion. Traditional health technology assessment and modeling methods often neglect the wider health system impacts that can be critical for achieving desired health system goals and are often of limited usefulness when applied to complex health systems. Researchers and health care decision makers can either underestimate or fail to consider the interactions among the people, processes, technology, and facility designs. Health care delivery system interventions need to incorporate the dynamics and complexities of the health care system context in which the intervention is delivered. This report provides an overview of common dynamic simulation modeling methods and examples of health care system interventions in which such methods could be useful. Three dynamic simulation modeling methods are presented to evaluate system interventions for health care delivery: system dynamics, discrete event simulation, and agent-based modeling. In contrast to conventional evaluations, a dynamic systems approach incorporates the complexity of the system and anticipates the upstream and downstream consequences of changes in complex health care delivery systems. This report assists researchers and decision makers in deciding whether these simulation methods are appropriate to address specific health system problems through an eight-point checklist referred to as the SIMULATE (System, Interactions, Multilevel, Understanding, Loops, Agents, Time, Emergence) tool. It is a primer for researchers and decision makers working in health care delivery and implementation sciences who face complex challenges in delivering effective and efficient care that can be addressed with system interventions. On reviewing this report, the readers should be able to identify whether these simulation modeling methods are appropriate to answer the problem they are addressing and to recognize the differences of these methods from other modeling approaches used typically in health technology assessment applications.

Triggered electrical stimulation may be more effective than non-triggered electrical stimulation in facilitating upper extremity motor recovery following stroke. It appears that the specific stimulus parameters may not be crucial in determining the effect of electrical stimulation.