Our Findings Help Inform You on Next Step Decision-Making and Planning for Your Drug or Medical Device
Our Approach
Information from the validation of surrogate endpoints is used for decision-making at the company level or by HTA agencies for reimbursement. Our approach for validation of surrogate endpoints may be used when:
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- suspecting a strong relationship between a clinically important endpoint (e.g. Overall Survival, OS) and another endpoint occurring earlier in the disease process (e.g. Progression-Free Survival, PFS)
- there is lack of good quality data for a treatment and endpoint of interest
- typical case for new drugs of long-term studies with good efficacy or low number of deaths
We use data on the drug of interest (in terms of the surrogate outcome) and similar drugs with long-term market presence, which provides information on efficacy of both the surrogate and the true, important outcome. Predictive modelling is performed for the drug of interest when a general relationship between the effect on the surrogate and the true endpoint is found as significant. The services that we offer and steps taken as part of Validation of Surrogate Endpoints are showcased on our page here.
This approach may also be applied to devices if adequate data on both the surrogate and the clinically important endpoint, related to the healthcare problem of interest), are possible to identify.
Our Experience
Our services for surrogate validation offer support to our clients through their decision-making process. We have experience performing data analysis in our models of a suspected relationship.
Below are sample projects:
- We performed a systematic literature review to identify, describe and categorize validation methods for surrogacy. With different statistical models for surrogacy validation at the trial- and individual- levels, and a combination of the two types of data, we applied the most suitable model for a given data-set.
- We performed a systematic review of RCTs for a given disease with various treatment regimens and information on PFS and OS. From this, we assessed the validity of PFS for OS surrogacy through statistical modeling. As a follow up, we performed a broad sensitivity analysis by applying different criteria for study selection, leading to different conclusions on different sub-populations.
