I have a dataset comprising clinical characteristics and glycomics profiles of disease samples (n=100+) and matched healthy controls. So far, I have been able to do classification modeling to discriminate between disease vs healthy using the biomolecular profiles.
On top of that, I have glycomics profiles collected across 2 more time points (total 3 time points - > timepoint 1 = time of discharge, timepoint 2 = 1 month follow-up, timepoint 3 = 6 months follow-up) from the disease patients. However, the healthy controls only have their glycan profiles measured at 1 time point (this is because no follow-up assessments and blood taking were done for the healthy controls).
My question is this: What kind of statistical analysis can I perform to draw meaningful insights on how the glycan profiles change across the 3 timepoints? I was originally thinking of survival analysis, but only a handful of patients out of the 100+ samples had adverse outcomes. So I question the applicability of that. Other than that, are univariate or multivariate tests to determine significant differences in the biomolecular profiles between each time point the only thing I can do?
I apologise for the lengthy question and appreciate any advice given!
As the samples are acquired from the same patient across time, the ideal thing would be to perform a paired analysis (repeated measures ANOVA or a non-parametric equivalent). However, the levels of the factor time (the timepoints) are required for every sample group, which are absent in controls). Therefore, I'd move to a combination of pairwise comparisons (t test or Mann-Whitney, depending on data normality), at the expense of sacrifizing the pvalue of interactions. However, post-hoc corrections as FDR may penalize less the false positives as variables in your glicomics data are considered in individual pairwise comparisons, while the correction initially should correct for all variables tested in the study.
Hope this helps!!!
Since you have three timepoints of the same measures on the same patients, you could simply ignore the controls and do mean of differences of each chemical analysis quantity t2 vs ti . t3 vs t2 , and t3 vs t1, and possibly a regression of differences on time.
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