Case study: Covariate adjustment
Authors
Testimonial
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Deep learning to reduce sample size requirement for adjuvant HCC trials
Context
No treatment is yet approved in the hepatocellular carcinoma (HCC) adjuvant setting.
Methods
Owkin developed the HCCNet model for prognosis of resected hepatocellular carcinoma patients. Using public liver patient data, we evaluate the added value of HCCnet as an adjustment covariate.
Results
Results show the use of Owkin’s HCCNet model outputs as an additional adjustment in an adjuvant trial setting. HCCnet is added to tumor stage and ECOG to evaluate its added value.
Covariate adjustment on HCCNet achieves +6% statistical power with the same number of patients, and the same statistical power with 12% fewer patients.
Impact
De-risk phase 3 trial by maximizing the probability of achieving statistical significance.
Shorten timelines by reaching significance at an earlier interim analysis.
Reduce enrolment needs, hence trial timelines, costs and time to launch.
