John F. P. Bridges

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

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.

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 forces 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).

ISPOR task force reportConstructing 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 forces 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).

Conjoint Analysis Applications in Health - How are Studies being Designed and Reported?: An Update on Current Practice in the Published Literature between 2005 and 2008.

Despite the increased popularity of conjoint analysis in health outcomes research, little is known about what specific methods are being used for the design and reporting of these studies. This variation in method type and reporting quality sometimes makes it difficult to assess substantive findings. This review identifies and describes recent applications of conjoint analysis based on a systematic review of conjoint analysis in the health literature. We focus on significant unanswered questions for which there is neither compelling empirical evidence nor agreement among researchers.We searched multiple electronic databases to identify English-language articles of conjoint analysis applications in human health studies published since 2005 through to July 2008. Two independent reviewers completed the detailed data extraction, including descriptive information, methodological details on survey type, experimental design, survey format, attributes and levels, sample size, number of conjoint scenarios per respondent, and analysis methods. Review articles and methods studies were excluded. The detailed extraction form was piloted to identify key elements to be included in the database using a standardized taxonomy.We identified 79 conjoint analysis articles that met the inclusion criteria. The number of applied studies increased substantially over time in a broad range of clinical applications, cancer being the most frequent. Most used a discrete-choice survey format (71%), with the number of attributes ranging from 3 to 16. Most surveys included 6 attributes, and 73% presented 7–15 scenarios to each respondent. Sample size varied substantially (minimum = 13, maximum = 1258), with most studies (38%) including between 100 and 300 respondents. Cost was included as an attribute to estimate willingness to pay in approximately 40% of the articles across all years.Conjoint analysis in health has expanded to include a broad range of applications and methodological approaches. Although we found substantial variation in methods, terminology, and presentation of findings, our observations on sample size, the number of attributes, and number of scenarios presented to respondents should be helpful in guiding researchers when planning a new conjoint analysis study in health.

Globalization and healthcare: understanding health and medical tourism

Faced with long waiting lists, the high cost of elective treatment and fewer barriers to travel, the idea of availing healthcare in another country is gaining greater appeal to many. The objective of this review is to perform a literature review of health and medical tourism, to define health and medical tourism based on the medical literature and to estimate the size of trade in healthcare. The Medline database was used for our literature review. In our initial search for ‘health tourism’ and ‘medical tourism’ we found a paucity of formal literature as well as conceptual ambiguity in the literature. Subsequently, we reviewed the literature on ‘tourism’ in general and in the context of healthcare. On the basis of 149 papers, we then sought to conceptualize health tourism and medical tourism. Based on our definitions, we likewise sought to estimate market capacity internationally. We defined health tourism as “the organized travel outside one’s local environment for the maintenance, enhancement or restoration of an individual’s wellbeing in mind and body”. A subset of this is medical tourism, which is “the organized travel outside one’s natural healthcare jurisdiction for the enhancement or restoration of the individual’s health through medical intervention”. At the international level, health tourism is an industry sustained by 617 million individuals with an annual growth of 3.9% annually and worth US

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

513 billion. In conclusion, this paper underscored the issue of a severely limited formal literature that is compounded by conceptual ambiguity facing health and medical tourism scholarship. In clarifying the concepts and standardizing definitions, and providing evidence with regard to the scale of trade in healthcare, we hope to assist in furthering fundamental research tasks, including the further development of reliable and comparable data, the push and pull factors for engaging in health and medical tourism, and the impact of health tourism but, more so, medical tourism on local healthcare systems.

Things are Looking up Since We Started Listening to Patients

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.

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

Clinical and healthcare decision makers have repeatedly endorsed patient-centered care as a goal of the health system. However, traditional methods of evaluation reinforce societal views, and research focusing on views of patients is often referred to as ‘soft science.’ Conjoint analysis presents a scientifically rigorous research tool that can be used to understand patient preferences and inform decision making. This paper documents applications of conjoint analysis in medicine and systematically reviews this literature in order to identify publication trends and the range of topics to which conjoint analysis has been applied. In addition, we document important methodological aspects such as sample size, experimental design, and method of analysis.Publications were identified through a MEDLINE search using multiple search terms for identification. We classified each article into one of three categories: clinical applications (n = 122); methodological contributions (n = 56); and health system applications (n = 47). Articles that did not use or adequately discuss conjoint analysis methods (n = 164) were discarded. We identified a near exponential increase in the application of conjoint analyses over the last 10 years of the study period (1997–2007). Over this period, the proportion of applications on clinical topics increased from 40% of articles published in MEDLINE from 1998 to 2002, to 64% of articles published from 2003 to 2007 (p = 0.002).The average sample size among articles focusing on health system applications (n = 556) was significantly higher than clinical applications (n = 277) [p = 0.001], although this 2-fold difference was primarily due to a number of outliers reporting sample sizes in the thousands. The vast majority of papers claimed to use orthogonal factorial designs, although over a quarter of papers did not report their design properties. In terms of types of analysis, logistic regression was favored among clinical applications (28%), while probit was most commonly used among health systems applications (38%). However, 25% of clinical applications and 33% of health systems articles failed to report what regression methods were used. We used the International Classification of Diseases — version 9 (ICD-9) coding system to categorize clinical applications, with approximately 26% of publications focusing on neoplasm. Program planning and evaluation applications accounted for 22% of the health system articles.While interest in conjoint analysis in health is likely to continue, better guidelines for conducting and reporting conjoint analyses are needed.

Healthcare Rationing by Proxy

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.

Determinants of demand for total hip and knee arthroplasty: A systematic literature review

The application of cost-effectiveness analysis in healthcare has become commonplace in the US, but the validity of this approach is in jeopardy unless the proverbial