Why and how to use qualitative research methods in conjunction with discrete choice experiments in healthcare
This week our colleagues Eleanor Butler and Marina Kousta attended an ISPOR webinar that explored why and how to use qualitative research methods in conjunction with discrete choice experiments in healthcare. They have written a blog about their attendance, reflecting on how Lightning API’s Payer and Physician Insight Platform can support both quantitative and qualitative research needs virtually.
Discrete Choice Experiments are a technique used in quantitative research to determine respondent’s preferences through their valuing of given characteristics (attributes). DCEs can be used in a variety of settings such as health policy and resource allocation or to understand views on a new treatment that is not yet commercially available. Qualitative research methods can complement the design of such experiments, especially when defining attributes and validating participants lived experiences.
During the seminars we explored key components of a DCE with a focus on sampling strategy and data collection methods. Conversely to a majority of qualitative research, purposive sampling strategies can add a lot of value over the traditional saturation approach in this scenario. Participant selection can be tailored at the start of the study to meet the specific goals of the research, providing a range of characteristics with a breadth of insights. The platform allows for planning in advance with a wide range of stakeholders and flexible recruitment. Data collection was discussed in the capacity of traditional qualitative techniques such as face-to-face interviews or focus groups in which the researchers must take in to account environmental factors which may impact the findings (e.g. interviewer attributes, setting, who else is present (e.g. patient if carer is being interviewed), interview group size). The Lightning API platform mitigates all of these factors. As a digital tool it facilitates such research as it allows for natural breaks in a response window which suits the needs of the respondent and their schedule. Additionally, it allows for planning in advance with a wide range of stakeholders and flexible recruitment.
Also notable is the requirement for as much transparency and evidence as possible in qualitative reporting. Such transparency and evidence may be difficult in traditional face to face or telephone scenarios where some key ideas may not be recorded. The Lighting API platform captures anything inputted by the respondent and ensures continuous quality control processes at every stage of the research, allowing for follow-up where necessary to clarify points raised in the responses. Finally, it is important to be iterative in design, allowing for flexibility. Lightning API platform can be used to identify and select attributes in a pre-test capacity, refinement of attribute content and wording through testing in the qualitative study and finally to refine attributes and compose wording for DCE.
In conclusion, qualitative research is important in determining and refining attributes to be tested in DCEs. It adds validity through participants lived experiences and can give a voice to patients or other key stakeholders. The focus is on purposive samples to ensure a range of rich insights. The Lighting API platform adds significant value here with the diverse and constantly growing selection of stakeholders. The data collection process on the platform enables complete transparency; everything is captured electronically with quality control processes at each stage- this resonates with one of the key messages in the workshop.
Perhaps the most interesting part was a participant question at the end around how to continue with qualitative research in current times, “are you aware of any virtual platforms?”
There is potentially a big unmet need for this at present, and perhaps there is a question around how the “gold standard” of face to face interviews will evolve now that we are forced to adapt to ensure continuity of research. The Lighting API platform is built with such challenges in mind, placing users in control of research needs, with an evolution of continuous development to meet the ever-evolving research landscape in the life sciences industry.
To learn more about how the Lighting API platform can support your research needs virtually, get in touch at email@example.com.
Article published 10 June 2020.