1
$\begingroup$

Sorry, I am new to everything, so any and all answers would be very helpful, just to help guide me in the right direction for reading.

I have a table of 20 independent variables (17 binary, 3 continuous) with a binary dependent outcome. I have ~600 observation subjects, each with these 20 independent variables and 1 dependent variable. In total, for my dependent variable I have 37 events with "poor signal" presumably caused by the 20 variables prior.

I already calculated each categorical data with chisquare and odds ratios with a 2 x 2 table. Many of my variables showed significance by itself in these calculations. I wanted to assess the effects of these variables on the outcomes when combined.

Therefore, I applied the logit regression via glm command in R. When I do my calculations, say for just gender, I get: Gender1 Estimate 0.9433 Std Error 0.3692 Z-value 2.555 Pr(>|z|) 0.0106 *
which actually equals to the same P-value I calculated for odds-ratio for Gender. I do this, because I have a lot of NA values for certain variables, and wanted to make sure the calculation was okay.

However, when I start adding multiple variables together, what was once significant started to become insignificant. Also, what once was correlated with a poor signal (dependent variable), was now correlated with a good signal. In otherwords, a positive Estimation becomes a negative Estimate (which I assume are the regression coefficients).

So, my question is how do I optimize this model? How do I choose which variables to include because including all 20 is not helpful. I read somewhere about "LASSO". Is this something that applies here?

Thanks

Edit: Also, is this something I should be using Breslow-Day test for homogenity? Or stepwise regression?

$\endgroup$
1

0

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Browse other questions tagged or ask your own question.