Structuring simulation to determine optimal sample size re-estimation strategy at trial design

Featuring Emily Hammarstrom-Wickens, Statistician II at PHASTAR

Abstract: 'What would happen if...? Structuring simulation to determine an optimal sample size re-estimation strategy at the trial design stage'.

When proving something mathematically is difficult or limited to specific situations, simulation studies may be an effective solution. In a clinical trial setting, simulations can represent the results of simultaneously performing many real trials, creating an empirical distribution for measures of interest, such as the Type I error. Anticipated parameters, such as the expected treatment effect, and the sensitivity in changes to such parameters can be assessed via simulating plausible scenarios for a clinical trial. Transparent and well-planned simulation studies can be modified to suit particular requirements, making them both a useful and informative tool when designing a clinical trial.

Assessing the benefit and functionality of a new adaptive design would be an appropriate occasion for utilising simulation studies. For example, assessing the effect of using Bayesian sample size re­estimation methods (SSR) on measures such as the Type I error rate. This example is motivated by the RO LARR trial Uayne et al., 2017), which failed to show a statistically significant treatment effect, potentially as a result from the misspecification of the sample size assumptions. SSR methods use accumulated study data to adjust the initial assumptions and re-estimate the sample size. Assessment and comparison of properties associated with these methods provides an example of where simulation studies can be used at the design stage of a clinical trial.

This talk provides guidance on the use of simulation studies to assess the benefit of using a Bayesian SSR design and the selection of an optimal strategy.

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