Solved – How to interpret coefficient standard errors for logistic regression

generalized linear model

The coefficients estimated for logistic regression are in log odds, and I understand it is common — at least when interpreting the output — to convert the log odds to odds so they're more easily understandable.

When reporting results for logistic regression, should log odds and their standard errors be reported, say, in a table, and then odds used to interpret the coefficients?

Or, can odds (and, uh, standard error odds?) be reported in a table?

Best Answer

It's fine to report odds ratios rather than log-odds differences if that will be easier for your audience to understand. If you report odds ratios it probably makes sense to report the confidence intervals. That is, if $\beta_i$ are the log-odds differences, $\sigma_i$ is the standard error of $\beta_i$ (also on the log-odds scale), and $q$ is 1.96 for $\alpha=0.05$ (or whatever Normal quantile you want use), then you should report the back-transformed confidence intervals:

$$ \exp(\beta_i \pm q \sigma_i) $$

rather than back-transformed values $\pm$ standard errors, as the confidence intervals are often asymmetric on the odds scale (they're not necessarily symmetric on the log-odds scale either, but generally much closer).

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