Marion Danner1, Vera Vennedey2, Mickaël Hiligsmann3, Sascha Fauser4, Stephanie Stock2. 1. Institute for Health Economics and Clinical Epidemiology, Cologne University Hospital, Gleueler Straße 176-178, 50935, Cologne, Germany. marion.danner@uk-koeln.de. 2. Institute for Health Economics and Clinical Epidemiology, Cologne University Hospital, Gleueler Straße 176-178, 50935, Cologne, Germany. 3. Department of Health Services Research, CAPHRI School for Primary Care and Public Health, Maastricht University, Maastricht, The Netherlands. 4. Center for Ophthalmology, Cologne University Hospital, Cologne, Germany.
Abstract
OBJECTIVES: Patients suffering from age-related macular degeneration (AMD) are rarely actively involved in decision-making, despite facing preference-sensitive treatment decisions. This paper presents a qualitative study to prepare quantitative preference elicitation in AMD patients. The aims of this study were (1) to gain familiarity with and learn about the special requirements of the AMD patient population for quantitative data collection; and (2) to select/refine patient-relevant treatment attributes and levels, and gain insights into preference structures. METHODS: Semi-structured focus group interviews were performed. An interview guide including preselected categories in the form of seven potentially patient-relevant treatment attributes was followed. To identify the most patient-relevant treatment attributes, a ranking exercise was performed. Deductive content analyses were done by two independent reviewers for each attribute to derive subcategories (potential levels of attributes) and depict preference trends. RESULTS: The focus group interviews included 21 patients. The interviews revealed that quantitative preference surveys in this population will have to be interviewer assisted to make the survey feasible for patients. The five most patient-relevant attributes were the effect on visual function [ranking score (RS): 139], injection frequency (RS: 101), approval status (RS: 83), side effects (RS: 79), and monitoring frequency (RS: 76). Attribute and level refinement was based on patients' statements. Preference trends and dependencies between attributes informed the quantitative instrument design. CONCLUSION: This study suggests that qualitative research is a very helpful step to prepare the design and administration of quantitative preference elicitation instruments. It especially facilitated familiarization with the target population and its preferences, and it supported attribute/level refinement.
OBJECTIVES:Patients suffering from age-related macular degeneration (AMD) are rarely actively involved in decision-making, despite facing preference-sensitive treatment decisions. This paper presents a qualitative study to prepare quantitative preference elicitation in AMDpatients. The aims of this study were (1) to gain familiarity with and learn about the special requirements of the AMDpatient population for quantitative data collection; and (2) to select/refine patient-relevant treatment attributes and levels, and gain insights into preference structures. METHODS: Semi-structured focus group interviews were performed. An interview guide including preselected categories in the form of seven potentially patient-relevant treatment attributes was followed. To identify the most patient-relevant treatment attributes, a ranking exercise was performed. Deductive content analyses were done by two independent reviewers for each attribute to derive subcategories (potential levels of attributes) and depict preference trends. RESULTS: The focus group interviews included 21 patients. The interviews revealed that quantitative preference surveys in this population will have to be interviewer assisted to make the survey feasible for patients. The five most patient-relevant attributes were the effect on visual function [ranking score (RS): 139], injection frequency (RS: 101), approval status (RS: 83), side effects (RS: 79), and monitoring frequency (RS: 76). Attribute and level refinement was based on patients' statements. Preference trends and dependencies between attributes informed the quantitative instrument design. CONCLUSION: This study suggests that qualitative research is a very helpful step to prepare the design and administration of quantitative preference elicitation instruments. It especially facilitated familiarization with the target population and its preferences, and it supported attribute/level refinement.
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