Publications

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:

Abstract: Motor cortex repetitive transcranial magnetic stimulation (M1-rTMS) induces analgesic effects in neuropathic pain, but not all patients are good responders, and no clinical predictors of the response have been identified. The present study aimed to develop and validate a simple and easy-to-use predictive algorithm for the individual response to M1-rTMS in peripheral neuropathic pain

Background and Aims Baseline Pain Variability (PV) is often cited as a key predictor of placebo response (1-3). This variability can be caused both by actual fluctuating pain levels and by an inability to accurately assess one’s pain. Some suggest that this latter source may indicate patients more prone to contextual influence and thus more

Objective: The expectations of improvement among subjects participating in a Randomized Controlled Trials (RCT) are one of the features most frequently associated with the placebo response. Consequently, it becomes a pivotal factor for understanding treatment effect, particularly in chronic pain RCTs such as Osteoarthritis (OA). There are multiple techniques to account for these expectations in studies,