<img alt="" src="https://secure.lazy8krti.com/218831.png" style="display:none;">

Case study: How max-diff analysis was used to better understand the key brand drivers in HCV

The challenge

In HCV, compliance, adverse events caused by treatment, and the decreased chance of SVR if retreatment is necessary, means that patients play a large role in the decision to go on therapy, so their buy-in is critical. Our client is looking to launch a product in HCV and needed to develop a deeper understanding of patient beliefs, barriers and triggers to starting treatment.

max-diff analysis was used to better understand the key brand drivers in HCV- case study
max-diff analysis was used to better understand the key brand drivers in HCV- case study

The challenge

In HCV, compliance, adverse events caused by treatment, and the decreased chance of SVR if retreatment is necessary, means that patients play a large role in the decision to go on therapy, so their buy-in is critical. Our client is looking to launch a product in HCV and needed to develop a deeper understanding of patient beliefs, barriers and triggers to starting treatment.

The solution
We carried out a qualitative phase comprising over 80 60-min face-to-face or telephone in-depth interviews amongst a mix of current, experienced and treatment-naïve patients with HCV in the 5 EU and Australia. During the quantitative phase, we undertook 200 30-min online surveys with a mixture of experienced and naïve patients. We used max-diff analysis to identify the attributes of most importance to patients in the treatment of HCV.


The outputs
We were able to provide our client with preference utility scores for each attribute level, highlighting relative importance of the attributes and attribute levels in choosing to be treated with any given product profile.

An Excel-based simulator allowed our client to model patient preference for any number of potential future market scenarios: including their products / regimens in development together with the many competitors, both current and future.

In addition, a latent class analysis allowed us to segment the patients based on how they make their product choices. This identified key patient segments for our client, and which attributes need highlighting to drive product acceptance.

Sign up to receive Rapport

Rapport is our e-newsletter and online resource for sharing our expertise and experience in global healthcare market research.

Sign up here