I am studying the immune system. This is the defense the body has to protect itself from pathogens like bacteria and viruses. The main cells involved are commonly known as white blood cells.
What we measure: We generally measure the strength of people’s immune response. We do this by:
- taking a vial of blood from an individual
- introducing a pathogen (virus, bacteria, fungus) to the blood and,
- measuring the amount of certain proteins produced by the white blood cells. More protein is considered a stronger response.
Our first hypothesis:
We suspect a specific environmental situation might temporarily decrease the response strength of the immune system in response to certain stimuli. We want to test this by first taking blood from people at baseline, then putting them in the specific environmental situation for a few minutes, and after taking them out of the situation we take some blood again. This way we can compare before and after. We will stimulate the blood with different pathogens and compare the protein production before and after. To statistically compare these I can use a paired t-test or Wilcoxon signed-rank test.
Our second hypothesis (the one my post on this forum concerns):
We also suspect specific pathogens to be effected more by the specific environmental situation than others. Say for instance we expect pathogen A to be more effected than pathogen B. My question is: what statistical tests can I use to determine if the effect is larger in A than in B? There is one more thing to take into account, namely that the baseline levels of immune response to pathogen A and B will not be the same. E.g. pathogen A might induce 3 times the amount of protein compared to B at baseline. This means we will have to work with ratios or percentages.
I was thinking I might calculate the percentage decreases or ratios/fold-changes per person for both pathogen A and B and compare these with a (non)parametric t-test. However, I am not sure if this is correct, and if I should use percentages or ratios/fold-changes. Also, we know age and gender effect responses, so should we correct for these? This situation might compare to some drug trails, where they compare the effects of drug A vs drug B (e.g. cholesterol lowering), but here the baselines will be more comparable.
I hope I was clear, if not, please feel free to ask,
Thanks in advance, Have a wonderful day,
PS. Some examples of drug/treatment studies: In this study http://jamanetwork.com/journals/jama/fullarticle/198311#JOC32467T3 they study the effect of lipid lowering drugs. As a test they use an analysis of covariance model applied to rank transformed data (see Table 3). This other somewhat similar study http://care.diabetesjournals.org/content/28/7/1547.full treatments were compared using least-square means.