Alternatives to Power for Sample Size Determination

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Alternative Methods to Power for Sample Size Determination

Power is the most common metric used for sample size determination. However, the calculated sample size will only achieve the target level of Power if the assumed effect size and other assumptions are proven to be correct.

In this tutorial, Brian Fox, research statistician at nQuery has explored alternatives to Power for the purposes of sample size determination. He has reviewed some available alternatives to Power and highlighted methods available in the areas of Interval Estimation and Bayesian designs.

Learning objectives of this webinar:

This webinar offers practical insights into alternative methodologies for sample size determination, enabling researchers to design studies that are both efficient and statistically sound.

Key Areas Covered:

1. Understanding Limitations of Traditional Power Analysis

• Recognize that calculated sample sizes based solely on power analysis may not achieve the desired power if assumed effect sizes and other parameters are inaccurate.

• Identify scenarios where deviations from initial assumptions can lead to underpowered or overpowered studies.

2. Explore Interval Estimation Methods for Sample Size Determination

• Learn how to utilize interval estimation techniques, such as confidence intervals, to determine sample sizes that ensure a specified level of precision.

• Understand the application of various statistical intervals, including prediction intervals and mixed Bayesian likelihood intervals, in the context of sample size planning.

3. Examine Bayesian Approaches to Sample Size Planning

• Investigate pure Bayesian methods, such as credible intervals and Bayes factors, for determining sample sizes that incorporate prior knowledge and quantify uncertainty.

• Explore hybrid Bayesian-frequentist approaches that combine elements of both paradigms to enhance the robustness of sample size calculations.

Alternative Methods to Power for Sample Size Determination: A Quick Guide for Biostatisticians 

Sample Size Determination Using Methods Other Than Power

Traditionally, the number of subjects required for a study is calculated in order for a certain target level of power to be achieved. However, as the calculation of the Power is dependant on the assumed effect size and other parameters and assumptions, deviations in these parameters and assumptions can result in underpowered or overpowered studies.

Some of the most common alternatives to Power include Interval Estimation, pure Bayesian approaches, and hybrid Bayesian approaches. Interval estimation is a widely understood method however is less commonly used for sample size determination.

Some popular Bayesian methods for sample size determination would be the credible Intervals, bayes factors, CRM, Mixed Bayesian Likelihood (MBL) intervals, posterior errors, and Bayesian Assurance. 

Each of these alternative methods has various characteristics which make them appealing for sample size determination.

Understanding the Limitations of Power Analysis

Power analysis relies on predefined effect sizes and variance estimates, which are often uncertain in real-world studies. If these assumptions are incorrect, studies may end up underpowered, leading to inconclusive results, or overpowered, wasting resources. Sensitivity analyses can help assess the impact of assumption variability, but alternative methods offer additional strategies to refine sample size calculations.

Using Interval Estimation for Sample Size Determination

Interval estimation methods, such as confidence and prediction intervals, allow researchers to determine sample sizes based on the precision of estimated effects rather than just power. These methods ensure that the study provides sufficiently narrow confidence intervals around key estimates, making results more interpretable and actionable. By focusing on precision rather than hypothesis testing, biostatisticians can design studies that yield more reliable and meaningful conclusions.

Exploring Bayesian Approaches to Sample Size Planning

Bayesian methods incorporate prior knowledge and quantify uncertainty in a way that traditional power analysis does not. Credible intervals and Bayes factors provide alternatives to frequentist hypothesis testing, allowing for more flexible decision-making. Additionally, hybrid Bayesian-frequentist approaches can enhance the robustness of sample size calculations, particularly in cases where prior data is available.

Best Practices for Biostatisticians

To optimize sample size determination using alternative methods, biostatisticians should:

• Evaluate the reliability of power analysis assumptions and consider sensitivity analyses.
• Use interval estimation methods to ensure precise and meaningful results.
• Consider Bayesian approaches when prior data is available or when flexibility is needed.
• Align sample size methodology with study objectives, regulatory requirements, and available resources.

About nQuery
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