blog/binary-logistic-regression-in-r/

[giscus[bot]]

blog/binary-logistic-regression-in-r/

Learn when and how to use a (univariate and multivariate) binary logistic regression in R. Learn also how to interpret, visualize and report results

https://statsandr.com/blog/binary-logistic-regression-in-r/

[aarontimo]

Pure gold - thank you Antoine!

[AntoineSoetewey]

You're welcome Aaron!

[venkatpgi]

Superb Antoine...much needed post for many researchers.
I have come across few situations and trying to find a way out.

1. Variables with unusually weird estimates. When you exponentiate them, the adjusted OR and their C.I are extremely high or low. I read in one post that such variables may be causing a near complete separation or classification of the binary dependent variable and there were some advises as to drop them or perform a Firth's bias reduced logistic regression or some form of penalized regression like LASSO. How do you handle them?
2. Variables to be entered in the model - variable selection. How do you select in your practice and which method do you advise? - filter based (using a 'p' value) or by some random method? I do check for VIF for numerical variables to reduce variables. But could not find a similar approach for categorical variables.

Thanks always.
Venkat

[AntoineSoetewey]

Thanks @venkatpgi!

1. It happens quite often in practice that the width of a CI is huge. I have heard about several methods such as those you mentioned, but very often I simply drop these problematic variables.
2. The method I recommend and use in most cases is the mixed stepwise selection (using AIC). However, I have seen many times and I have already used (in particular in health/medical fields) the following method: first, perform a univariate binary logistic regression separately for each independent variable and second, include significant variables (or close to significance, for instance p-value < 0.2) from the first step in one final model to see whether or not these variables remained significant when using a multivariate binary logistic regression.

Hope this helps.

[tomhiatt]

This is the kind of helpful post that I always wish I had while attempting some past project. Thank you!

Quick mistake: "This means that, the odds of developing a heart disease are (3.574 - 1) × 100 = 257.4% higher for males than for females."

(odds should be replaced with probability)

On that note, using risk instead of odds is easier for non-gamblers to interpret and also easier to communicate findings. Do you have any suggestions on straightforward ways to convert ORs from models to RRs? For example I would love to use tbl_regression() displaying RRs instead of ORs. Is there a way to do that?

[AntoineSoetewey]

Dear @tomhiatt,

Quick mistake: "This means that, the odds of developing a heart disease are (3.574 - 1) × 100 = 257.4% higher for males than for females."
(odds should be replaced with probability)

Good catch, it's corrected, thanks!

On that note, using risk instead of odds is easier for non-gamblers to interpret and also easier to communicate findings. Do you have any suggestions on straightforward ways to convert ORs from models to RRs?

The relationship between OR and RR is rather complex. To convert an OR to a RR, you can use the formula:

$RR = \frac{OR}{(1 – p + (p * OR))},$

where $p$ is the risk in the control group, that is, the prevalence of the outcome in the reference group. This risk in the control group is in our case the chance that the outcome is 1 if the predictor is 0, which can be computed as follows:

$p = \frac{\exp(\hat{\beta}_0)}{1 + \exp(\hat{\beta}_0)}.$

See this post and this thread for more information.

For example I would love to use tbl_regression() displaying RRs instead of ORs. Is there a way to do that?

Based on the package's documentation, I don't think there is an easy way to report RR instead of OR, at least at the moment.

I found this post which stipulates that you can obtain RR with exp(coef(model)) from a glm fit with family = quasipoisson.

As a side note, these two papers may be of interest to you as well:

1. Calculating risk and prevalence ratios and differences in R: developing intuition with a hands-on tutorial and code
2. Common pitfalls in statistical analysis: Odds versus risk

Hope this helps.

[imenedj]

I'm a PhD student currently analyzing my data, and your post is incredibly valuable. I appreciate the effort you put into explaining concepts, which has been particularly helpful for someone like me who is new to data analysis. Could you please provide an explanation for multinomial logistic regression? I need to apply it to my data. Thanks in advance!

[AntoineSoetewey]

Hello @imenedj,

Glad you find it useful!

Writing a tutorial about multinomial logistic regression in R is on my to do list but do not expect it soon because I am quite busy with my PhD thesis at the moment.

In the meantime, here is a great tutorial if you want to learn more: https://bookdown.org/drki_musa/dataanalysis/multinomial-logistic-regression.html

Hope this helps.

Regards,
Antoine

[Eghelakpo]

God bless you for this fantastic piece, Antoine.

[AntoineSoetewey]

Thank you @Eghelakpo !