Webinars and Videos

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Biological structures and spirals.
Case Studies in using FACTS to design Clinical Trials: A 2-arm MACE safety study
Using FACTS to design a large 2-arm safety study, borrowing information from a previous trial, exploring alternative endpoints and using an enriched population.
Biological structures and spirals.
Case Studies in using FACTS to design Clinical Trials: An Alzheimer’s Phase 2 dose finding study
A Phase 2 dose finding study using a Bayesian dose response model and response adaptive randomization across multiple doses and treatment regimens. Includes a presentation of the interim analyses from the actual trial that lead to the successful phase 3 trial of Lecanemab.
Biological structures and spirals.
Case Studies in using FACTS to design Clinical Trials: A trial in hemorrhagic stroke with adaptive enrichment
A randomized two arm study, using a utility weighted modified Rankin score and rules for adapting the enrolment criteria based on the location of the haemorrhage.
Biological structures and spirals.
FACTS Webinar: Response Adaptive Randomization RAR in multi arm trials aiming to select the best arm
This FACTS Webinar will address RAR: 1) A surprising amount of the literature considers just the two arm setting, when arguably the most interesting setting for this technique is the multi-arm one. 2) Present some results in favor of RAR. 3) Lastly there are still misconceptions around that RAR inflates type-1 error, where it is easy to show that the opposite can be true.
Biological structures and spirals.
Case Studies in using FACTS to design Clinical Trials: An open enrollment CRM dose escalation study
Designing a phase 1 dose escalation using Bayesian Logistic Regression Model (BLRM), with open enrolment (rather than cohort) using FACTS N-CRM.
Biological structures and spirals.
Case Studies in using FACTS to design Clinical Trials: A two-arm study with adherence model driven effect size and irregular interim analyses
Using FACTS to design a two-arm study where differences in adherence between the arms drive the effect size. Uncertainty in the effect size prompted spacing the interims to correspond to different sample sizes resulting in irregular interim analyses. Using a uses a finite mixture model from previous data on the analysis endpoint to justify the irregular interims.
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