Insights
The Importance of Patient Psychology in Health and Clinical Research
When we think about patient characteristics that influence health, disease, and clinical research, we tend to think about things like vital statistics, medical history and genetic makeup – while patient personality or psychology is often overlooked. In reality, the importance of personality has been under scrutiny for centuries and dates back to Greek and Roman times
The placebo response in drug development. Part 4: Irritable Bowel Disease
As in many other diseases (e.g. pain, osteoarthritis, rheumatoid arthritis, Parkinson’s Disease, etc ), patients participating in clinical trials for IBD treatments may experience a pronounced placebo effect or placebo response. As IBD is a chronic disease, efficacy is comprised of improvement rates as well as remission rates; unfortunately, both are influenced by placebo response, albeit at different rates.
Prediction of placebo response in Osteoarthritis improves estimation of the treatment effect: Impact on drug development
Placebell©™ can be used in OA and similar diseases in which efficacy is characterized using patient-reported outcomes to reduce the interference of the placebo effect and improve assessment of the drug effect. The performance and applicability of PlacebellI©™ has recently been demonstrated in a Phase 2 RCT conducted by a biotech sponsor in subjects with moderate to severe painful knee OA.
The placebo response – a phenomenon related to the placebo group or to the individual patient?
Historically, interpretation of clinical trials relies on “assay sensitivity”, or the sensitivity to detect clinically meaningful differences between endpoints measured in the group of patients given active drug compared to the group of patients given placebo. Assay sensitivity can be influenced by many factors, including the study design, specific endpoints selected, number of clinical sites and, of course, the magnitude of the placebo response.
From covariates to confounding factors: the danger of having too many covariates
Clinical trials typically evaluate efficacy of experimental therapies in heterogeneous patient populations, as patient characteristics vary significantly. These patient characteristics might be prognostic factors that ultimately induce variability in clinical trial data. An imbalance in these factors between treatment groups at baseline will increase variability of the estimated treatment effect, ultimately compromising study power and decreasing
Regulatory Guidelines for the Use of Baseline Covariates to Increase Clinical Trial Study Power
Clinical trials data analyses can employ baseline covariates to control for factors that may impact measurement of outcomes – particularly to describe individual patient characteristics that may or may not relate to treatment response. For example, patient age may be used as a baseline covariate to reduce data variability resulting from this factor. The use of baseline