Two Methods, One Story? Comparing Results of a Choice Experiment and Multidimensional Thresholding From a Clinician Preference Study in Aneurysmal Subarachnoid Hemorrhage

Patient Preferences for First-Line Treatment of Locally Advanced or Metastatic Urothelial Carcinoma: An Application of Multidimensional Thresholding

OBJECTIVES

Patient preferences have the potential to influence the development of new treatments for locally advanced/metastatic urothelial carcinoma (la/mUC), and therefore we explored how patients with la/mUC value different attributes of first-line treatments.

METHODS

An online preference survey and multidimensional thresholding (MDT) exercise were developed following a targeted literature review and qualitative interviews with physicians, patients with la/mUC, and their caregivers. Treatment attributes included two benefits (overall response rate [ORR], pain related to bladder cancer [scored 0-100; 100 being the worst pain possible]) and four treatment-related risks (peripheral neuropathy, severe side effects, mild to moderate nausea, mild to moderate skin reactions). A Dirichlet regression was used to estimate average preference weights. Marginal utility and the reduction in ORR that patients would accept in exchange for a 10-point decrease or a 10% decrease in other attributes were calculated.

RESULTS

A total of 100 patients were recruited and self-completed the survey and MDT. Mean patient age was 64.9 years (standard deviation, 7.6), 54% were female, and 38% identified as white. All included treatment attributes had a statistically significant impact on preferences. Changes in ORR had the largest impact, followed by cancer-related pain and treatment-related risks. Patients were willing to accept an 8.4% decrease in ORR to reduce their pain level by 10 points or a 7.8% decrease in ORR to reduce the risk of peripheral neuropathy by 10%. For a 10% decrease in severe side effects, mild to moderate nausea, or skin reaction, patients would accept decreases in ORR of 5.5%, 3.7%, or 3.4%, respectively.

CONCLUSIONS

Of the attributes tested, changes in ORR were most important to patients. Patients made tradeoffs between treatment attributes indicating that a lower ORR may be acceptable for an improvement in other attributes such as reduced cancer-related pain or the risk of treatment-related adverse events.

Making Use of Technology to Improve Stated Preference Studies

The interest in quantifying stated preferences for health and healthcare continues to grow, as does the technology available to support and improve health preference studies. Technological advancements in the last two decades have implications and opportunities for preference researchers designing, administering, analysing, interpreting and applying the results of stated preference surveys. In this paper, we summarise selected technologies and how these can benefit a preference study. We discuss empirical evaluations of the technology in preference research, with examples from health where possible. The technologies reviewed include serious games, virtual reality, eye tracking, innovative formats and decision aids with values clarification components. We conclude with a critical reflection on the benefits and limitations of implementing (often costly) technology alongside stated preference studies.

Multidimensional Thresholding for Individual-Level Preference Elicitation

OBJECTIVES

Multiple methods are available for collecting health preference information. However, information on the design and analysis of novel methods is limited. This article aims to provide the first introduction into the design and analysis of multidimensional thresholding (MDT).

METHODS

We introduce MDT as a 2-step approach: First, participants rank the largest possible improvements in all considered attributes by their importance. Second, participants complete a series of systematically combined trade-off questions. Hit-and-Run sampling is used for obtaining preference weights. We also use a computational experiment to compare different MDT designs.

RESULTS

The outlined MDT can generate preference information suitable for specifying a multiattribute utility function at the individual level. The computational experiment demonstrates the method's ability to recover preference weights at a high level of precision. While all designs in the computation experiment perform comparably well on average, the design outlined in the paper stands out with a high level of precision even if differences in relative attribute importance are large.

CONCLUSION

MDT is suitable for preference elicitation, in particular if sample sizes are small. Future research should help improve the methods (e.g., remove the need for an initial ranking) to increase the potential reach of MDT.

Comparing Discrete Choice Experiment with Swing Weighting to Estimate Attribute Relative Importance: A Case Study in Lung Cancer Patient Preferences

INTRODUCTION

Discrete choice experiments (DCE) are commonly used to elicit patient preferences and to determine the relative importance of attributes but can be complex and costly to administer. Simpler methods that measure relative importance exist, such as swing weighting with direct rating (SW-DR), but there is little empirical evidence comparing the two. This study aimed to directly compare attribute relative importance rankings and weights elicited using a DCE and SW-DR.

METHODS

A total of 307 patients with non-small-cell lung cancer in Italy and Belgium completed an online survey assessing preferences for cancer treatment using DCE and SW-DR. The relative importance of the attributes was determined using a random parameter logit model for the DCE and rank order centroid method (ROC) for SW-DR. Differences in relative importance ranking and weights between the methods were assessed using Cohen's weighted kappa and Dirichlet regression. Feedback on ease of understanding and answering the 2 tasks was also collected.

RESULTS

Most respondents (>65%) found both tasks (very) easy to understand and answer. The same attribute, survival, was ranked most important irrespective of the methods applied. The overall ranking of the attributes on an aggregate level differed significantly between DCE and SW-ROC (P < 0.01). Greater differences in attribute weights between attributes were reported in DCE compared with SW-DR (P < 0.01). Agreement between the individual-level attribute ranking across methods was moderate (weighted Kappa 0.53-0.55).

CONCLUSION

Significant differences in attribute importance between DCE and SW-DR were found. Respondents reported both methods being relatively easy to understand and answer. Further studies confirming these findings are warranted. Such studies will help to provide accurate guidance for methods selection when studying relative attribute importance across a wide array of preference-relevant decisions.

HIGHLIGHTS

Both DCEs and SW tasks can be used to determine attribute relative importance rankings and weights; however, little evidence exists empirically comparing these methods in terms of outcomes or respondent usability.Most respondents found the DCE and SW tasks very easy or easy to understand and answer.A direct comparison of DCE and SW found significant differences in attribute importance rankings and weights as well as a greater spread in the DCE-derived attribute relative importance weights.