By: Jean Paty, PhD | May 24, 2016
Why the industry needs to translate complex clinical results into value-based terms that everyone understands.
Patient reported outcomes (PRO) can be incredibly useful for providing context about the patient’s experience with their disease and treatment. Not only does this data help guide clinical trial research, it helps regulators and payers make more informed decisions about drug approvals, pricing and label claims. In oncology, for example, there is an increased focus on the use of PRO instruments to examine several aspects of a cancer patient’s experience, including symptoms, physical limitations, treatment related effects, and broader aspects of health-related quality of life. These endpoints are being used to support approval and label claims related to efficacy and safety.
However, many PRO endpoints are often a combination of a number of aspects of the patient’s experience, and can be difficult to understand and interpret for stakeholders. Such confusion can add time to the approval process, and may add additional risk that decision makers won’t recognize the value of these products because the PRO endpoints can be complex and vary across instruments and health conditions, making them difficult to interpret.
While traditional approaches to scoring and constructing PRO endpoints are used in most clinical trials, it would also be useful to generate complementary, intuitive measures for PRO instruments that are easier to interpret by non-PRO experts.
Get out of the lab
For the ISPOR meeting in May, the team conducted research exploring novel methods for translating quantitative PRO findings into succinct, simple language describing patient experience of the disease and the impact of treatment. We reviewed data from four enzalutamide clinical trials in different prostate cancer populations using results from four PRO tools:
We devised a 0–100% metric to standardize the results and generate total scores for each trial characteristic. This metric indicates the responses’ position relative to maximum scale length (e.g., a score of 119 on a 0–156 scale would equal 76%).
Creating this linear metric allowed us to compare data across trials, instruments, and patient populations and provided an easy way to identify high and low scores. The 0–100% metrics were then aligned with simple language that maps onto the patient’s experience as a way to translate quantitative data into meaningful results.
What the data really said
The data from these PRO tools showed that according to their own perception of themselves, patients across the prostate cancer trials experienced were functioning at relatively high levels prior to treatment, which is important when interpreting drug effects.
Once we simplified the data into the 0-100% metric, we found that patients taking enzalutamide largely maintained levels of functioning relative to control treatments over time. This is an important result because it indicates that the drug staves off a decline in the patient’s ability to function effectively and enjoy a higher quality of life.
By simplifying the potentially complex PRO data and endpoints into a common metric and associating that metric with descriptive terms, we believe the results will more easily resonate with various stakeholders. Explaining that patients taking a particular drug were able to continue working and enjoy more leisure activities, and that they had fewer issues related to urination and sexual activity clearly communicates the real world value of the treatment.
Across the healthcare industry, stakeholders are clamoring for this type of clarity around clinical data so they can be more judicious in the treatment decisions they make. If the industry wants those stakeholders to approve, pay for, and prescribe these products, they have to find ways to communicate that value proposition in terms that everyone can understand.
This piece was co-authored by Catherine Fickley and Emily Hawryluk.