The amount of data collected from patients involved in clinical trials is continuously growing. All baseline patient characteristics are potential covariates that could be used to improve clinical trial analysis and power. However, the limited number of patients in phases I and II studies restricts the possible number of covariates included in the analyses. In
Placebo Response
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
The baseline pain variability (BPV) has often been presented as positively correlating with the placebo response (PR) and associated with a lack of consistency in the subjects’ pain evaluation. Excluding high BPV subjects should then improve the precision of the treatment response. Another common method to increase the essay sensitivity is to adjust the analysis for covariates
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).
Each September, pain advocates and specialists raise awareness about issues facing the millions of people living with chronic pain. One of the most pressing? The lack of treatments available to alleviate suffering. In this blog, we look at three top challenges of getting new pain drugs to market—and offer a potential solution.
Tools4Patient presented data at the MDS Congress 2021 that predicted the placebo response in Parkinson’s disease (PD) in a multi-center, multi-national clinical study.