Repeated measurements over time
Time courses, growth curves and repeated scores on the same animals or cultures.
- Choose a methodRepeated measures ANOVA vs mixed model: which to use for measurements over timeRepeated measures ANOVA and a mixed model agree on complete data; when a value is missing for a reason unrelated to the value itself, use the mixed model. R code for both, and a Python per-animal summary route.
- Choose a methodGlucose tolerance test statistics: total AUC, incremental AUC or the time course?Decide the question first. For overall glucose handling, compare one area under the curve per mouse with Welch's t-test; if fasting glucose may differ, plan to add the incremental AUC. Skip per-time-point t-tests. R and Python code.
- Choose a methodTumour growth curves: how to compare treatment groups when mice reach the humane endpointCompare tumour growth rates on the log scale using every measurement up to culling, with a mixed model or per-mouse slopes. Comparing only the mice still measured on the last day underestimates the effect. Python and R code.
- Choose a methodClinical scores over time (EAE, arthritis): how to compare groups without a test on every daySummarise each animal's daily clinical scores into measures chosen in advance, such as cumulative score, maximum score, incidence and day of onset, and compare those. A test on every day inflates false positives. Python and R code.
- Choose a methodCell proliferation and doubling time: fitting the growth phase and comparing cell linesEstimate doubling time from the slope of log cell number over the exponential phase, before cells approach confluence. Compare cell lines on growth rates from independent experiments, not on wells at one time point. Python and R code.
- Choose a methodFear conditioning freezing statistics: per-tone t-tests, extinction curves and what counts as nAnalyse percent freezing with one value per mouse: a mean or an extinction slope per mouse, or a repeated-measures model with a sphericity correction. A t-test at every tone, or tones as n, gives false positives. Python and R code.