I am working on identifying biomarkers of the outcome of treatment in a disease. I have 20 potential biomarkers and more than 30 potential confounders. Actually, I know that some of those potential confounders affects treatment outcome. However, before determining the association between each biomarker and the therapeutic outcome, I want to know which of these confounders affects the expression level of each biomarker. I want to understand the impact of covariates on the expression of biomarkers to include them as confounders in multivariate analysis.
from reading your question I believe you mean covariates not confounders.. Check and be sure which term you mean and ask your question again, please. Best, David Booth
I am looking for biomarkers that predict my therapeutic response to a drug. I have a list of 20 biomarker candidates. Additionally I have a list of more than 30 covariates. These covariates were selected because in previous studies it has been seen that they are related to the therapeutic outcome or could be related, so my idea is to adjust for them in the multivariate models. However, I also want to know if these covariates could affect the expression levels of each one of my biomarkers before taking this to multivariate models. Do you have an idea how I can explore this?
I had planned to build regression models taking each of the biomarkers separately as an outcome variable. In this way, I could know which of the covariates are associated with a higher or lower expression of the biomarker. I had also thought about doing a principal component analysis (although I feel that this would not allow me to identify exactly which covariates can affect the expression of the biomarker). Do you know or recommend me any method that allows me to identify this?
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