How does DESeq2 account for the dependency between samples in repeated 16S data, for example, relative abundance for samples from the same child?

Following the vignette section on time series, I've written the code below, which runs fine but I'm nervous that the variance is being underestimated, since I don't see a way to include the info about which samples are from the same child.

dds <- DESeqDataSetFromMatrix(countData = count.table, colData = coldata, design = ~ case + time + time.case)

dds <- DESeq(dds, test="LRT", reduced=~time.case) results(dds)

If you want to test the effect on a condition (or in this case time) and account for patient level baseline, you would just use ~patient + condition. For an LRT you would use a reduced design of ~patient.

If you have a single sample per patient per condition (or time point) you cannot estimate something like an interaction between patient and condition.