Publications

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

Background: In randomized controlled trials (RCTs), assay sensitivity issues can affect both statistical power and the confidence in treatment efficacy estimates. This sensitivity problem is often linked to the role of contextual effects in patient response. While statistical adjustments for prognostic variables can address this, it is impractical to account for all possible covariates. Machine

Objective: Patient non-adherence and drop-out significantly extend the duration and cost of clinical trials. A predictive tool identifying patients likely to drop out could enhance clinical trial management through targeted and personalized engagement strategies. We aimed to develop such a tool using predictive modeling on data from two studies on schizophrenia and dry eye.  Design: