Statistical Challenges in the Conduct and Management of Ongoing Clinical Trials During the COVID-19 Pandemic

Antiepileptogenesis after stroke-trials and tribulations: Methodological challenges and recruitment results of a Phase II study with eslicarbazepine acetate

There is currently no evidence to support the use of antiseizure medications to prevent unprovoked seizures following stroke. Experimental animal models suggested a potential antiepileptogenic effect for eslicarbazepine acetate (ESL), and a Phase II, multicenter, randomized, double-blind, placebo-controlled study was designed to test this hypothesis and assess whether ESL treatment for 1 month can prevent unprovoked seizures following stroke. We outline the design and status of this antiepileptogenesis study, and discuss the challenges encountered in its execution to date. Patients at high risk of developing unprovoked seizures after acute intracerebral hemorrhage or acute ischemic stroke were randomized to receive ESL 800 mg/d or placebo, initiated within 120 hours after primary stroke occurrence. Treatment continued until Day 30, then tapered off. Patients could receive all necessary therapies for stroke treatment according to clinical practice guidelines and standard of care, and are being followed up for 18 months. The primary efficacy endpoint is the occurrence of a first unprovoked seizure within 6 months after randomization ("failure rate"). Secondary efficacy assessments include the occurrence of a first unprovoked seizure during 12 months after randomization and during the entire study; functional outcomes (Barthel Index original 10-item version; National Institutes of Health Stroke Scale); post-stroke depression (Patient Health Questionnaire-9; PHQ-9); and overall survival. Safety assessments include the evaluation of treatment-emergent adverse events; laboratory parameters; vital signs; electrocardiogram; suicidal ideation and behavior (PHQ-9 question 9). The protocol aimed to randomize approximately 200 patients (1:1), recruited from 21 sites in seven European countries and Israel. Despite the challenges encountered, particularly during the COVID-19 pandemic, the study progressed and included a remarkable number of patients, with 129 screened and 125 randomized. Recruitment was stopped after 30 months, the first patient entered in May 2019, and the study is ongoing and following up on patients according to the Clinical Trial Protocol.

Estimators for handling COVID-19-related intercurrent events with a hypothetical strategy

The COVID-19 pandemic has affected clinical trials across disease areas, raising the questions how interpretable results can be obtained from impacted studies. Applying the estimands framework, analyses may seek to estimate the treatment effect in the hypothetical absence of such impact. However, no established estimators exist. This simulation study, based on an ongoing clinical trial in patients with Tourette syndrome, compares the performance of candidate estimators for estimands including either a continuous or binary variable and applying a hypothetical strategy for COVID-19-related intercurrent events (IE). The performance is investigated in a wide range of scenarios, under the null and the alternative hypotheses, including different modeling assumptions for the effect of the IE and proportions of affected patients ranging from 10% to 80%. Bias and type I error inflation were minimal or absent for most estimators under most scenarios, with only multiple imputation- and weighting-based methods displaying a type I error inflation in some scenarios. Of more concern, all methods that discarded post-IE data displayed a sharp decrease of power proportional to the proportion of affected patients, corresponding to both a reduced precision of estimation and larger confidence intervals. The simulation study shows that de-mediation via g-estimation is a promising approach. Besides showing the best performance in our simulation study, these approaches allow to estimate the effect of the IE on the outcome and cross-compare between different studies affected by similar IEs. Importantly, the results can be extrapolated to IEs not related to COVID-19 that follow a similar causal structure.