Simulating Clinical Trials With and Without Intracranial EEG Data

It is currently unknown if knowledge of clinically silent (electrographic) seizures improves the statistical efficiency of clinical trials.

Using data obtained from 10 patients with chronically implanted subdural electrodes over an average of 1 year, a Monte Carlo bootstrapping simulation study was performed to estimate the statistical power of running a clinical trial based on A) patient reported seizures with intracranial EEG (icEEG) confirmation, B) all patient reported events, or C) all icEEG confirmed seizures. A "drug" was modeled as having 10%, 20%, 30%, 40% and 50% efficacy in 1000 simulated trials each. Outcomes were represented as percentage of trials that achieved p<0.05 using Fisher Exact test for 50%-responder rates (RR50), and Wilcoxon Rank Sum test for median percentage change (MPC).

At each simulated drug strength, the MPC method showed higher power than RR50. As drug strength increased, statistical power increased. For all cases except RR50 with drug of 10% efficacy, using patient reported events (with or without icEEG confirmation) was not as statistically powerful as using all available intracranially confirmed seizures (p<0.001).

This study demonstrated using simulation that additional accuracy in seizure detection using chronically implanted icEEG improves statistical power of clinical trials. Newer invasive and noninvasive seizure detection devices may have the potential to provide greater statistical efficiency, accelerate drug discovery and lower trial costs.