In this free webinar, you will learn about:
- Regression Models and Sample Size Calculation for Common Regression Models
- Sample Size Determination for Cluster Randomized Designs using Mixed-Effects Models
- How to Compute the Minimum Sample Size for Clinical Prediction Models
More about the webinar
Regression models are types of statistical models often used in clinical trials to describe the relationship between an outcome variable and one or more predictor variable(s). Simple linear regression, multiple linear regression, logistic regression and Cox regression are all examples of regression models that can be used in various scenarios. We will investigate practical examples showing how to calculate the sample size when these models are used.
Mixed effects or hierarchical models are another example of regression models typically used in clinical trials, particularly in the analysis of Cluster randomized trials (CRTs). CRTs are a type of experimental design which can be more efficient and cost-effective than individually randomized trials, leading to smaller sample sizes and reduced variance in outcome, however they come with their own challenges and limitations.
We will explore the use of mixed-effects models in these scenarios and provide practical examples of how to determine the sample size for these models.
Minimum sample size requirements for clinical prediction models in the past have often been set using guidelines such as the 10 subjects per predictor variable rule-of-thumb. In recent years many methods have been developed in order to provide a more rigorous approach to determining the minimum sample size for clinical prediction models.
In this webinar, we will provide an analysis of the various regression models that can be used for clinical trials and discuss how the sample size can be computed for each.
We will also discuss the benefits and limitations of each model and investigate methods for computing the minimum sample size for clinical prediction models.
