Data-driven covariate adjustment

Optimizing randomized control arm clinical trials

Using AI-discovered prognostic covariates to strengthen treatment signal
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Question

How do I ensure my trial meets its primary endpoint by increasing power without adding more patients?

By adjusting for data-driven covariates in the statistical analysis of your trial, you can reduce bias and increase the power of your study without having to increase sample size. 

Owkin’s solution 

Data-driven covariate adjustment

Owkin’s data-driven covariate adjustment increases the probability of phase III trial success through increased statistical power and broader inclusion criteria.

Methodology

Click
Next
Step 1
1
Understand
the target indication and inclusion criteria being studied
Down
Selection of criteria to build external training data 
Step 1
Understand
the target indication
Click
Next
Step 2
2
Multimodal
data hunt
Down
Build the external training data set to accurately match the trial inclusion criteria
Step 2
Data hunt
to build external training data set
Click
Next
Step 3
3
State-of-the-art ML
Down
Combine image and clinical features to train a model to identify which pre-treatment covariates best predict patient outcomes
Step 3
State-of-the-art ML
to select prognostic covariates
Click
Next
Step 4
4
Covariate adjustment
Down
Either:
1.  Apply the selected prognostic covariates for adjustment
2. Directly use the model as a composite covariate during adjustment
Step 4
Covariate adjustment
to improve trial readouts
Case study
Video
How do I select data-driven covariates without sacrificing power?

Data-driven covariate adjustment

Why Owkin?

Engaged with regulators
Engaged with regulators

Continued conversations with EMA and FDA, including an EMA letter of support for our data-driven covariate adjustment approach.

Multimodal external data
Robust and up-to-date, multimodal external data

Access to continuously enriched, up-to-date multimodal data especially digital pathology images representing the latest standard of care and capturing disease heterogeneity and the tumor microenvironment.

Leading AI and biostatistics team
Leading AI and biostatistics team

Renowned AI expertise with # published papers in leading journals, two strategic alliances with leading biopharma companies, and AI digital pathology diagnostics.

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“A main potential strength of the artificial intelligence (AI) models is the gain in prognostic performance compared to an adjustment with covariates used in current practice in clinical trial settings. This gain in performance could translate to gains in statistical power.”
European Medicines Agency
Letter in support of Owkin's approach