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Description: Check what's going on in the Comments tab on the right. Want to collaborate? Send me an email fredfeys@vub.ac.be Adverse effects (AEs) of pharmaceuticals being tested in placebo controlled randomized clinical trials (PC-RCTs) can unblind participants and bias treatment outcomes. During informed consent, participants entering a PC-RCTs are informed of relevant AEs. We assume that the most common AE will be emphasized in communicating possible harm. During treatment phase, experiencing AEs may break the double blind of an PC-RCTs. Having no AEs, volunteers may perceive allocation to a placebo group. Out of discouragement ('taking a sugar pill'), they may report worse outcomes. Converserarily, participants can find out allocation to the intervention simply by experiencing AEs. PC-RCTs were participants in placebo and intervention groups have a marked different frequency of AE reporting may easily become unblinded. Why it is important to do this research: Unblinded randomized double blinded clinical trials become internally invalid. Unblinded outcome estimates for the treatment effect are biased (Hróbjartsson 2013). Still, regulatory entities such as the Federal Drugs Agency (FDA) and others require PC-RCTs for drug approval. Also, systematic reviews pile up this biased evidence creating clinical guidelines that may further distort the real clinical picture of pharmaceuticals. Objectives: We want to test for indirect evidence for unblinding by examining AEs profiles in placebo and intervention groups. An unpublished, preliminary analysis we did, found a large effect of congruent AEs profiles leading to smaller treatment effect sizes. However, this analysis is restricted to one specific field os sexual medicine: PDE-5 inhibitors. With this pilot, we want to broaden the scope and sample across a wide range of pharmaceutical interventions and research what other factors may explain AE profile congruency. We test the hypothesis that within an PC-RCT, more congruent AE profiles across intervention and placebo groups lead to smaller treatment effect sizes.