Three confounder, all related to the same variable or different variables? How do you know they are confounders without running a multiple regression yet?
for example, we have 3 confounding random variables, x1, x2 and x3 with 3 different variances. If we had to treat each variable alone, we would have used odd ratio or relative risk ( depending on what kind of study we are using whether retrospective or prospective) to determine the risk of the disease given the variable. What if we have 3 variables? how can we combine all of them to the get the odd ratio of diagnosing the disease? Do we have to use logistic regression analysis with the logit as a linear combination function of multiple variables? Also what does it have to do with our specific patient who comes with a combination of x1, x2 and x3, do we have to plug those values into the logistic analysis in order to get the probability of having the disease?
To complicate the issue ( although it is not neccessarily at this stage), what if we conduct a metaanalysis with different studies are conducted at different places, how would we combine those data into a the result?
Three confounder, all related to the same variable or different variables? How do you know they are confounders without running a multiple regression yet?
Stop cowardice, ban guns!
I consider them independent variables. I may omit the term confounding for now.
Now suppose my study is retrospective one, case-control, which is suppose to get the odd of the exposure given the disease. Then how to get the probabilty of the disease given the variables in hand? Because this is what I care about, namely the probability of the disease given the exposure not the opposite. How can I run the logistic regression in the retrospective study and get a meaningful inference about the probability of the disease given the exposure?
Yes, you would want to run logistic regression. It would produce the log odds which can easily convert to probability.
Stop cowardice, ban guns!
PeterFlom (05-24-2015)
Tweet |