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There is an intrinsic interest of leveraging all available information for an efficient design and analysis of clinical trials. Including external data in the analysis of clinical trials may increase study power or allow for the reduction of (usually) the control group sample size. The use of external data in trials are nowadays used in earlier phases of drug development (Trippa, Rosnerand Muller, 2012; French, Thomas and Wang, 2012; Hueber et al., 2012; Smith et al., 2019), occasionally in phase III trials (French et al., 2012; Viele et al., 2018), and also in special areas such as medical devices (FDA, 2010a), orphan indications (Dupont and Van Wilder, 2011) and extrapolation in pediatric studies (Berry, 1989; Best et al., 2019). This allows adequately powered trials at smaller sample sizes leading to faster trial conduct and exposure of fewer patients to a potentially in-effective control treatment.


In this short course, we will provide a statistical framework to incorporate external information into a trial. During the first part of the course, we will introduce Bayesian Dynamic Borrowing designs based on the meta-analytic predictive (MAP) model (Neuenschwander et al., 2010). The MAP model is a Bayesian hierarchical model, which combines the evidence from different potentially heterogeneous sources. Dynamic borrowing permits to limit the use of historical data when it is incompatible with the data observed within the trial.

In the second part of the course, we will propose a practice session with applications using the R package RBesT, the R Bayesian evidence synthesis tools, which are freely available from CRAN. These exercises will enable participants to apply the presented approach themselves. During third and last part of the course, more advanced topics will be detailed such as effective and maximum sample sizes, advanced operating characteristics, and probability of success.


Who is this event intended for? All statisticians working on clinical trials, with a basic knowledge of R. Some notions of Bayesian statistics could be helpful.

What is the benefit of attending? Attendees will have the chance to cover; Bayesian Dynamic Borrowing designs based on the meta-analytic predictive (MAP) model; Design planning, operating characteristics, statistical analysis; and Applications using the R package RBesT

Agenda

Monday, January 24, 2022


9

00AM

-

12

00PM

Lecture Session 1

• Meta-analytic predictive (MAP) model • Robustification for dynamic borrowing • Design planning, operating characteristics • Final analysis

12

00PM

-

5

00PM

Practice Sessions

Homework will be completed individually but will be supervised via a separate forum set-up, where delegates can ask questions, liaise with course trainers etc. Details on this will follow at a later date.

Tuesday, January 25, 2022


9

00AM

-

5

00PM

Practice Sessions

Homework will be completed individually but will be supervised via a separate forum set-up, where delegates can ask questions, liaise with course trainers etc. Details on this will follow at a later date.

Wednesday, January 26, 2022


9

00AM

-

5

00PM

Practice Sessions

Homework will be completed individually but will be supervised via a separate forum set-up, where delegates can ask questions, liaise with course trainers etc. Details on this will follow at a later date.

Thursday, January 27, 2022


9

00AM

-

12

00PM

Lecture Session 2

• Effective sample size, maximum sample size • Advanced operating characteristics • Equivalence between MAP and MAC (Meta-analysis combined) • Probability of success, decision rule

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    £465.-

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