when developing LASSO logistic regression models using the OHDSI/PLP framework, we generally receive as an output a model with coefficient values assigned to each candidate predictor. Some coefficients may be zero due to LASSO shrinkage.
We were wondering, if reporting the 95% confidence interval of these coefficient values is actually a thing:
Is there functionality to compute the 95% confidence interval of coefficient values? I believe we are not doing this in the PLP package @jennareps? Is this information computed in Cyclops @msuchard?
Would there be any reason why we would want the 95% confidence interval for a regression coefficient? It appears common practice to report this CI for hazard ratios or odds ratios to then conclude which covariates are “good” risk factors, but translated to a prediction problem, I believe LASSO is already making this decision for us.
I would be interested if anyone has experience with CIs for regression coefficients