The servEB project (WISS 2025, federal state of Salzburg, 20102/F2300645-FPR) combines
clinical expertise, advanced statistical analyses, and AI-driven imagine classification technology to improve the assessment of trial outcomes in rare diseases, especially Epidermolysis Bullosa research as an example. When defining meaningful endpoints, multiple aspects of the disease have to be considered,...
Binary endpoints are commonly used to measure clinical outcomes in randomized controlled trials. In this context, conditional odds ratios (ORs) based on logistic regression have been routinely used as population-level summary to quantify treatment effects. However, ORs have been criticized for a lack of interpretability, non-collapsibility, and sensitivity to model specification. In response,...
Background:
Multiple endpoints are a major topic of discussion in rare disease research, particularly regarding to patient-centered outcome measures, as they allow for a more comprehensive assessment of treatment effects. However, a critical challenge in these trials is allocation bias, as they are often unblinded or single-blinded. Allocation bias arises when future treatment allocations can...
Even in rare diseases, where the sample size is limited and blinding is less frequently implemented, randomized controlled trials are considered the gold standard to proof efficacy. Randomization is used to mitigate bias and regulatory guidance recommend the investigation of the impact of bias on the test decision. We quantified how allocation bias affects the test decision in small sample...
In rare diseases, the need for innovative clinical trial designs is increasing. Platform trials are becoming particularly popular, as they allow for flexible adding and dropping of arms and reduce sample size requirements by using a shared control. In a platform trial setting with two experimental arms and one control, we use clinical trial simulations to quantify the impact on operating...
