Event

Design of clinical trials has traditionally relied on the frequentist hypothesis testing framework where the trial size is specified as the minimum sample size that guarantees a required level of power. Sample size determination may be performed analytically when the test statistic has a known asymptotic sampling distribution and the power function is available in analytic form. Bayesian methods have gained popularity in all stages of discovery, namely, design, analysis and decision making. Bayesian decision procedures rely on posterior summaries whose sampling distributions are commonly estimated via Monte Carlo simulations. In the design of clinical trials, the Bayesian approach incorporates uncertainty about the design value(s) instead of conditioning on a single value of the model parameter(s). Accounting for uncertainties in the design value(s) is particularly critical when the model includes nuisance parameters. In this talk, I present methodology that utilizes the large-sample properties of the posterior distribution together with Bayesian additive regression trees (BART) to efficiently obtain the optimal sample size and decision criteria in fixed and adaptive designs. The proposed approach significantly reduces the computational burden associated with Bayesian design and enables wide adoption of Bayesian operating characteristics as assessment criteria in design of clinical trials.

Shirin Golchi is an Associate Professor of biostatistics at McGill University. Her research interests are broadly Bayesian inference and computation with a focus on Bayesian design and analysis of clinical trials. Specific topics covered by her research program include efficient assessment of design operating characteristics, precision methods for clinical trials and statistical methods for flexible and innovative trial designs. 

https://sgolchi.research.mcgill.ca/

Date and time
25 June, 9:30am-10:30am AEST

Zoom details
This seminar will be held by Zoom only and will be recorded.

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meeting ID: 858 3529 9111 and

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