At the recent 2025 Advanced Technologies & Treatments for Diabetes congress in Amsterdam, Cognivia presented two new scientific posters demonstrating the applicability and scientific robustness of its data-driven tools, Placebell and Compl-AI, in Type 1 Diabetes (T1D) clinical trials. These results mark a significant step forward in addressing two persistent challenges: data variability and patient drop-out, both of which can compromise statistical power
Experts shared their perspectives from real-world trials on both classical and new approaches to manage the placebo response – and some questions arose throughout the discussion.
A growing body of work has demonstrated that pain perception can be modulated by social, cultural, contextual and interpersonal factors.1,2,3,4 Beyond this, patient trust in their doctor – an important component of the doctor-patient relationship – has been shown to positively influence patient health outcomes. A growing body of research is also showing that the placebo effects have
While significant placebo responses rates are often noted in clinical trials for indications like pain and depression, this issue can plague drug development in any therapeutic area – particularly in diseases that rely on subjective or patient-reported outcomes as primary efficacy endpoints. Quality of life (QoL) endpoints, for example, are often used to measure therapeutic efficacy in oncology clinical trials – but also in diseases like schizophrenia, pain, heart failure, inflammatory bowel disease (IBD), allergy and pruritus.
Using Predictive Modeling to understand the impact on assay sensitivity The placebo response is a heavily studied and historically challenging phenomenon for drug developers. Strong placebo effect diminishes the ability to distinguish efficacy of an experimental drug, leading to phase II and III trial failures1– even for otherwise effective drugs. Researchers have long devised strategies
Placebo response is one of the biggest threats to clinical data analysis– especially when left unchecked. In this blog, we review the top three risks trial scientists must address (and how to address them).