Hi all, would be grateful of some advice with regards to suitable statistical analysis for a study with repeated measures taken from the same subject.
The study revolves around repeated heart rate variability measurements taken from the same animal at multiple event occurrences that are not at fixed time point - e.g. each animal takes a different amount of time to reach each separate event.
Each animal acts as its own control, with a baseline reading being taken prior to the start of the process, and then a reading taken as the animal enters several situations thereafter. So my data is 1 row per animal, with 7 columns of heart rate variability measurements per animal. I am looking to compare results within-subject so to speak - so I am looking to confirm whether changes of heart rate variability at each event is significantly different to the baseline reading for each animal. I am not looking to compare animals to each other, which seems to be where I come unstuck as most software programs seem to insist on having multiple rows - therefore the analysis of the data ends up involving more than one animal.
Does anyone have any advice on how to manage this situation, and whether there is a test that will function to compare a test subject's result to another result from the same test subject.
Thanks in advance!
For a repeated measures within-subject study, common statistical analyses include:, Repeated Measures ANOVA (RM-ANOVA): Compares means across multiple time points or conditions.
Mixed Effects Models (MEM): Accounts for individual subject variability and correlations between measurements.
Generalized Estimating Equations (GEE): Analyzes correlated data, such as repeated measures, with non-normal distributions.
Linear Mixed Models (LMM): Combines fixed effects (e.g., treatment) with random effects (e.g., subject variability).
Multilevel Models (MLM): Examines hierarchical data structures, like measurements nested within subjects.
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