We're doing an experiment collecting saliva pre & post stressor, measuring cortisol & alpha-amylase. I've seen the results analyzed a few different ways and am wondering if there is one "best" method. What I've seen includes:
1) Simple Post-minus Pre calculation
2) LogPost-minus-LogPre
3) %change i.e. (Post-minus-Pre)/Pre
4) Use of Pre as a covariate for Post-minus-Pre
5) AUC
Are any of these methods (or others) the most widely accepted or used? Or is it completely up to how we want to look at the data?
I've done some within-group comparisons and we compared mean values at pre and post for the group and we used option 2. I would say that it is not appropriate to use option 1 as for the assay we used it was essential to log transform the values to gain the calculation.
AUC is the most accurate but it requires several samples if cost is an issue. However this is probably more appropriate if you are looking at the long term effects of a stressor.
I'd be interested in any papers you have that have used option 4 as i had not previously considered this?
Cheers
James
I agree with James. If you have only two sample points (pre and post stress), I am not sure that AUC would be a good way to analyse your data. In my opinion, the option 2 is better. Other interesting analysis is the option 4, because you can also do a regression analysis with other variables (are you doing psychometric evaluations?) and try to have a model that would explain the influence of each factor in the changing of hormones due to stress.
I hope I've helped!
Best regards,
Nastassja
if possible use a repeated-measures ANOVA., maybe you have a look at my thesis
for better underst
anding
- Badges
- Science topic
- Similar topics
- Biological Science
- Physiology
- Physiological Processes
- Stress