Publications

Background Effective pharmacological treatments for Post-Stroke Cognitive Impairment (PSCI) remain elusive. Preclinical studies have shown that phosphodiesterase 4 (PDE4) inhibition improved cognition, particularly memory, in post-stroke animal models and in healthy young and elderly individuals. This study tested whether the PDE4 inhibitor roflumilast could improve memory in PSCI patients. Methods A double-blind randomized placebo-controlled trial

Background Accurately characterizing placebo response is essential for improving sensitivity in osteoarthritis (OA) trials. This analysis investigates how baseline patient characteristics and psychological factors – leveraged through historical data models – contribute to placebo response. It is based on the ATLAS study, an online, double-blind, randomized, placebo-controlled study that assesses the efficacy and safety of

Background & Aims RCTs in neuropathic pain (NeP) are challenged by the high inter-individual variability in treatment response, reflecting the diverse underlying mechanisms of pain. While the Neuropathic Pain Symptom Inventory (NPSI) has previously delineated three pain clusters (Bouhassira et al 2021): pinpointed pain (paresthesia/dysesthesia), evoked pain (stimulus-induced pain), and deep pain (spontaneous pressing pain),

Purpose Disparities among sites are frequently observed in the results of Randomized Controlled Trials (RCTs).  As a result, adjusting for site effects is a common strategy in RCT analyses to account for these disparities. However, such adjustments introduce additional parameters into the analysis, which can be especially detrimental in Osteoarthritis (OA ), with an average number

The FDA’s 2023 guidance on baseline covariate adjustment highlights the importance of incorporating prognostic covariates into randomized clinical trials (RCTs) efficacy analyses. Adjusting for such covariates can reduce variability in treatment effect estimates, leading to narrower confidence intervals and more powerful hypothesis testing. In line with this guidance, Placebell baseline prognostic covariates were developed for

Background: Patient non-adherence and drop-out increase the time and cost of clinical trials. A tool that predicts, at baseline, which patients are at risk of dropping out could enhance trial management through personalized engagement strategies. Understanding patient profiles and behaviors is essential for this. Our goal was to develop a machine learning-based model to predict