Adaptive Clinical Trial Designs

Jul 9, 2018 | Webinar

Recorded June 13, 2017

Webinar On Demand

Trends in Protocol Design Practice and Optimization

Presented by: Ken Getz, Director of Sponsored Programs at the Center for the Study of Drug Development, Tufts University School of Medicine

The scientific and operating demands of protocol designs have increased rapidly during the past decade due to numerous factors. This talk characterizes trends in, and the forces driving, protocol design practices. The impact of these practices on clinical trial performance and economics will be addressed. Several protocol design optimization strategies will also be discussed including the adoption and use of adaptive designs and patient centric approaches.

Designing and Operating a High Performance DMC/DSMB – Controlling Possible Bias

Presented by: Joe Massaro, Biostatistics Professor at Boston University School of Public Health

In an adaptive clinical trial, a Data Monitoring Committee (DMC), also known as a Data Safety Monitoring Board (DSMB), may review interim efficacy data at planned intervals to determine if the trial should be adapted, such as stopping the trial early or increasing the sample size for the final analysis.  Here we will discuss examples of the types of interim study results commonly provided to DMC/DSMBs; how and to whom communication of study results should be disseminated by the DMC/DSMB after their review of interim data; and the potential for biasing the remainder of the study, whether adapted or not, if such communications are not handled appropriately.

Adaptive Design Practical Considerations and Execution

Presented by: Dale Usner, PhD, CSO & Sr. VP, Strategic Scientific Consulting at SDC

Adaptive designs, statistical backbone, team effort.  Multiple illustrative examples will be used to present the practical considerations and execution of designing and running various adaptive design strategies, including logistics and impact on clinical operations, data management, and randomization services with focus on implications for statisticians.

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