Download and explore the data yourself. Data files include:
- Bayesian Assurance for Two Normal Means - Example .nqt
- Bayesian Assurance for Mean Difference with Custom Prior - Example .nqt
- Group Sequential Design for Two Means (Lan-DeMets Spending Function only) - Example .nqt
- Inequality Tests for Difference of Two Proportions - Example .nqt
- Z-test for Two Means - Example .nqt
Choosing the Effect Size for Sample Size Calculations
Understanding MCID, Sensitivity Analysis and Assurance
Selecting an appropriate effect size is crucial in sample size determination. Choosing an effect size that is too small can lead to overestimating the necessary sample size.
Conversely, choosing an effect size that is too large can result in an underpowered trial due to an underestimated sample size. Additionally, the uncertainty associated with effect size estimation adds complexity, emphasizing the need for careful consideration and accurate estimation.
In this webinar, we explore the challenges of selecting an appropriate effect size. We will discuss basing estimates on the minimum clinically important difference (MCID) versus the conventional method for sample size determination, as well as techniques for exploring uncertainty in effect size estimation.
Choosing the Effect Size for Sample Size Calculations
Choosing an appropriate effect size is one of the five essential steps in sample size determination, requiring careful consideration.
In the past, effect size was typically chosen in line with the conventional method for sample size determination, where a realistic difference is determined from existing evidence and a sample size is determined from this estimate.
Recently, there has been a notable shift towards alternative methods for estimating effect size. The minimum clinically important difference has become a popular alternative to the conventional method. There are criticisms of this shift, questioning how important the MCID is in sample size determination.
Another step in the 5 essential steps in sample size determination, is, exploring uncertainty. Given that effect size may only be an estimate, it is important to consider the uncertainty surrounding it.
Sensitivity analysis is employed to explore various scenarios, while assurance involves characterizing our uncertainty in the effect size, using a statistical prior rather than a set of distinct values.
In this webinar, we have explored the challenges of selecting an appropriate effect size. We examine basing estimates on the minimum clinically important difference (MCID), versus the conventional method, along with techniques for addressing uncertainty in effect size estimation.
