Clinical Trials

We specialize in adaptive trial designs that use accumulating data to efficiently adjust sample sizes, dosing strategies, and patient enrollment criteria mid-trial, reducing total duration and cost. Subgroup analysis using [causal forest methods](/research#heterogeneous-treatment-effects) reveals patient populations most likely to benefit from treatment, enabling precision medicine approaches. [Heterogeneous treatment effect estimation](/research#debiased-ml-cate) identifies not just average efficacy but which patient characteristics predict response, critical for post-marketing real-world evidence generation. We handle missing data through multiple imputation strategies grounded in causal theory, not just statistical convenience.

Pharmaceutical companies leveraging our platform can substantially reduce trial timelines while improving patient outcomes through adaptive dosing and enrollment strategies. Regulatory submissions benefit from transparent, scientifically rigorous analysis of treatment effects across subgroups. Post-market surveillance and real-world evidence programs scale by automatically detecting which patient populations experience the greatest benefit from treatment.

Our solutions integrate with clinical data systems and maintain full FDA-compliant audit trails and documentation.