I would like to test whether there is a significant difference in outcome in a pre/post scenario.
I have the following set-up:
Measured level of concentration of A (
continuous) in blood pre-death and measured concentration of A post-death for each individual (roughly 100 individuals in total).
Working hypothesis: After death, the concentration of A falls and hence should be on average lower then pre-death concentration.
So, my first impulse was to go for a
paired-t-test given all assumptions are fulfilled.
However, my problem arises from the fact that the post-death concentration of A is not always measured at the same time after the death has occurred. So maybe 1h after death, sometimes 12h later and so on. I wonder if this causes any problems and whether I can somehow factor in the difference in post-death time-to-test.
(There are also other parameters which change on an individual basis: age, concentration, time, sex, cause of death; they are all categorical)
Is there any alternative approach to this or is the "significant difference" approach, given so many loose ends, just not reasonable at all? In the
paired t-test I would just ignore all other parameters, though they might affect the concentration of A as well.
Another idea would be to use a
one-way MANOVA with the dependent variables "pre-death_A" and "post-death_A" and the independent variables age, concentration, time, sex, cause of death.
I have never done this before so I am a bit hesitant. Any other suggestions?