Could we consider the cell experiment at multiply time points as a repeated measurement design?

Question

We conduct a study for exploring the mechanism of osteoporosis. There is an experiment about observing the biological reactions of osteocyte when we treat it as three drugs (A, B, C type).

First, we have isolated the osteocyte from tissue.

Second, we will perform cell culture and divide them into five groups (I, II, III, IV, V).

Third, each cell group (I, II, III, IV, V) will also divide three groups for treating with the three drugs (A, B, C type).

We will treat the osteocyte with the three drugs at five different time points ( I group (A, B, C type) at 12 hours, II group (A, B, C type) at 24 hours, III group (A, B, C type) at 36 hours, IV group (A, B, C type) at 48 hours, V group (A, B, C type) at 72 hours).

(In fact, we divide the cell for 5X3=15 groups.)

In our experiment, the primary outcome measure for the biological reactions was continuous variable. And we detect the primary outcome measure at the five time points, respectively.

Thus, the question is: should we consider it as a repeated measurement design?

Although the five groups cell was originated from the same primary cell for the same mice, we think the cell experiment is different from the human or the animal.

The five groups cell at five different time points might satisfy the independence requirement. So, we could analysis the data by the ANOVA at five time points, respectively, rather than Repeated measures ANOVA or Linear mixed model or GEE.

Is right?

Answer 1

The answer depends on what you expect when you "perform cell culture and divide them into five groups (I, II, III, IV, V)."

If you expect those five groups just to be technical replicates, separate but otherwise equivalent samples from the originating cell culture, then there's no need to treat this as a repeated-measure design.

If you expect there to be systematic differences among those five groups in baseline levels of your outcome measure or in their responses to drugs or in their changes over time, then you need to take that into account with an analysis that takes those systematic differences into account. Otherwise the correlations of effects within each group are likely to mis-estimate the true variability in the effects of drugs over time.