Choice Modeling in Pharma Research
Choice Modeling in Pharma Research
Choice modeling is a research methodology that uses behavioral or stated preference data to determine relative importance of a set of attributes. Choice modeling uses experimental design to set up choices with varying attributes in order to classify the importance of each attribute. Each decision made is analyzed to infer relative priorities in which attributes are more important, and which values or levels for an attribute are desirable. In plain English, choice modeling presents hypothetical scenarios to people, measures their decisions, and uses fancy-math to infer insights about what drives decision-making.
Choice modeling can be used in pharma research for:
- Message testing
- Physician/patient gap
- Quality of life feedback
Creating the messaging and claims for new pharmaceutical products is a multi-faceted challenge in pharma research. The messaging needs to resonate with both physicians and the end consumers (i.e., patients). But these two unique audiences are looking for very different things.
Choice modeling can discover interesting insights about your messaging. In a choice modeling study, participants are asked to make tradeoff decisions between options to deduce the relative rank position of each option. By conducting a choice modeling study with both physicians and consumers using the same messages, the impact and relative preference of those messages can be determined for each audience. Looking at these relative preferences in parallel can help to identify messaging that is impactful with both physicians and consumers.
Physician / patient satisfaction gap
In medical care, there is often a gap between the patient’s needs and the physician’s priorities. By conducting separate choice modeling exercises with patients and physicians, the relative importance of a variety of needs and priorities can be measured for each audience. Comparing these results can identify gaps in patient care that could lead to better patient satisfaction ratings. Look for areas where there is a disconnect between the patient needs and physician priorities. Is the physician prioritizing what is most important to the patient?
Quality of life feedback
Patients in long term care or with chronic conditions frequently complete quality of life assessments. These assessments survey the patients on their physical, mental, and emotional well being. The questions ask about frequency and severity of different symptoms or feelings in an attempt to measure the quality of life for a patient. It does not, however, provide any detail about their relative impact or relative importance. Is the low severity, high frequency item affecting their quality of life more, or is it the high severity, low frequency item? Supplementing existing Quality of Life surveys with a choice modeling exercise can lead to some additional insights.
The following example Datagame uses statements adapted from the RAND 36-item short form (SF-36). By asking patients to rank the items as well, it gives additional information about which items are having the biggest impact on the patient’s overall quality of life.
Datagame for pharma research
Datagame has two choice modeling games in its library. MaxDiff Rankifier uses the maximum difference, or best-worst scaling, methodology. Participants select their most and least favorite option from a subset of the total options. By repeating these discrete choices, relative preference can be inferred based on their choices.
Prefer! is a preference ranking game that displays options two at a time and asks respondents to select their favorite. This simple choice is repeated multiple times until a full rank order for all of the options has been determined. An optional feedback question may also be asked to capture additional information.
These discrete choice modeling methods can help to hone your message and bridge the patient-physician gap.