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Linear Mixed Effects Modeling
We, more often than not, work on experiments involving repeated measures. Examples include responses measured in cellular assays where the repeated measures may occur over multiple experimental batches, multiple plates, multiple wells within a plate etc. Data derived from behavior of mice assayed over consecutive trials would be another example of experiments involving repeated measures. The distinctions between biological and technical replicates become less clear in these scenarios. Standard statistical tests like t-tests, ANOVA are not applicable. Instead it requires the use of more sophisticated approaches. Linear Mixed Effect Modeling refer to one such approach.
In this course, we will go over the motivation for the use of these models, the underlying description and assumptions behind them, example scenarios where they can be used and code going over the implementation and interpretation of these models in R.
We will go over these slides during the workshop today
Our core led the development of an R package, RMeDPower that can be used to design and appropriately power studies involving repeated measures. This is specifically relevant to the designs of cellular assays. The relevant functions we developed are based on linear mixed effects models. This is a biorxiv manuscript describing the tool. Please feel free to use this package in your own work and give us feedback, ask questions etc.