# What is the cause of this very high standard error in my logistic regression model?

I am running a logistic regression model where the outcome variable is Neurologic Complications, and there are various factors who's impact I am examining. One of the factors (HTN_new1), a categorical variable, has a strangely high standard error value, and is throwing off subsequent confidence interval and OR calculations. What could be the cause of this inflated value and how can I go about fixing it? Note, I have already checked the original excel model & R dataframe for entry errors and could not find anything that stood out.

Input:

 NeuroLogit2 <- glm(Neurologic Complication? ~ stroke_comorbid + HTN_new +
anesthesia type+Over 75yo? + Gender_new + Embol_Collateralart +
carotid.subclavian + Spinal drain?, data=Tevar.new, family=binomial)
> summary(NeuroLogit2)



Output:

Call:
glm(formula = Neurologic Complication? ~ stroke_comorbid +
HTN_new + anesthesia type + Over 75yo? + Gender_new +
Embol_Collateralart + carotid.subclavian + Spinal drain?,
family = binomial, data = Tevar.new)

Deviance Residuals:
Min        1Q    Median        3Q       Max
-1.09673  -0.37157  -0.27390  -0.00009   2.87970

Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept)           -20.90519 1153.29897  -0.018   0.9855
stroke_comorbid1        1.40348    0.57747   2.430   0.0151 *
HTN_new1               16.59862 1153.29876   0.014   0.9885
anesthesia type1      1.49715    0.77617   1.929   0.0537 .
Over 75yo?1           0.17094    0.51136   0.334   0.7382
Gender_new1             0.00523    0.54231   0.010   0.9923
Embol_Collateralart1   -0.58778    1.14262  -0.514   0.6070
carotid.subclavian1     0.28837    0.64745   0.445   0.6560
Spinal drain?1        1.03701    0.53742   1.930   0.0537 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

Null deviance: 144.76  on 324  degrees of freedom
Residual deviance: 118.84  on 316  degrees of freedom
AIC: 136.84

Number of Fisher Scoring iterations: 18

• or it could be for one of your classes, there's only 1 observation. for example HTN_new – StupidWolf Jun 8 '20 at 20:16
• This usually happens in complete or quasi-complete separation, and I'm willing to bet that is what is happening. Essentially, there is a hyper plane which perfectly separates the 1s from the 0s. Have you tried Firth's Logistic Regression? – Demetri Pananos Jun 8 '20 at 20:25
• @DemetriPananos I have not, how would I go about that? Sorry I'm a bit new to all of this :/ – bdg67 Jun 8 '20 at 20:31
• @bdg67 That's ok. Try fitting your data with this package. You might have to run 'install.package('logistif')'. – Demetri Pananos Jun 8 '20 at 20:33
• The highly-voted threads in stats.stackexchange.com/questions/tagged/separation?tab=Votes should help. – Sycorax Jun 8 '20 at 21:17