The overall risk of clinical trial failure is still too high – meaning more repeat trials, lost timelines and premature abandonment of programs. In part, this is because of the placebo response: the measured improvement of a patient in a trial after receiving a sham treatment. While a high placebo response doesn’t necessarily mean the
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.
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
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).