AI-powered RCTs for the 21st century

TwinRCTs™

AI-powered TwinRCTs reach full enrollment faster because they use smaller control groups than other trial designs

21%

Reduction in control arms during Phase 3 trials in Huntington’s Disease and ALS

"[T]he proposed prognostic covariate procedure is an acceptable statistical approach for primary analysis of clinical trials."

EMA Qualification Opinion for PROCOVA™

TwinRCTs leverage patients’ digital twins to enable highly powered studies with smaller control groups

About AI-powered digital twins

01 Design an AI-powered trial

Our digital twin-generating AI models are trained on vast amounts of historical, individual-level data. By accounting for the information contained in these models, TwinRCTs are designed to provide more power with smaller control groups than other randomized trial designs.

RCT

TwinRCT

02 Create AI-generated digital twins for every patient

The data collected from each patient at their first visit is input into the pre-trained AI model to generate their digital twins. These digital twins provide comprehensive, longitudinal predictions of every patient's potential outcomes if they were to receive placebo and/or standard of care.

03 Use our regulatory-qualified framework to estimate the treatment effect

Each patient in a TwinRCT is randomized to a treatment or control group. At the end of the trial, information from the patients' digital twins is used to compute a precise, unbiased estimate of the treatment effect using our regulatory-qualified PROCOVA™ framework.

Impact

Highly powered clinical trials that reach full enrollment faster

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Accelerate time-to-market

AI-powered TwinRCTs hit enrollment targets faster because they require fewer patients to achieve the same power compared to traditional trial designs.

Attract patients

Many patients are wary of participating in clinical trials due to the prospect of receiving a placebo. TwinRCTs make control groups smaller, increasing a patient’s chance of receiving the experimental treatment.

Increase precision

Incorporating the comprehensive information from patients’ digital twins into trial analyses allows for more precise estimates of treatment effects at the population, subgroup, and even individual patient level.