Information about project titled 'How to handle missing data'
How to handle missing data
|Details about the project - category||Details about the project - value|
|Project manager:||Lena Kristin Bache-Mathiesen|
|Supervisor(s):||Morten Wang Fagerland, Thor Einar Andersen|
Background: In recent years, researchers have attempted to determine the effect of training load on injury risk.Sufficient sample size is necessary to estimate the effect with acceptable certainty. Missing data in training load measures causes reduced sample sizes and, in the worst-case scenario, introduces selection bias. So far, no study has provided a solution to missing training load measures.
Aim: Determine how missing data should be handled in training load and injury risk research.
Methods: First, we will map the current practice of handling missing data in the training load and injury field with a narrative review. Second, we will simulate a relationship between training load and injury risk in a real-world training load dataset. Methods for imputing or deleting missing observations in training load will be compared by their ability to uncover the simulated relationship.