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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 type`1      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
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  • $\begingroup$ or it could be for one of your classes, there's only 1 observation. for example HTN_new $\endgroup$ – StupidWolf Jun 8 '20 at 20:16
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    $\begingroup$ 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? $\endgroup$ – Demetri Pananos Jun 8 '20 at 20:25
  • $\begingroup$ @DemetriPananos I have not, how would I go about that? Sorry I'm a bit new to all of this :/ $\endgroup$ – bdg67 Jun 8 '20 at 20:31
  • $\begingroup$ @bdg67 That's ok. Try fitting your data with this package. You might have to run 'install.package('logistif')'. $\endgroup$ – Demetri Pananos Jun 8 '20 at 20:33
  • 1
    $\begingroup$ The highly-voted threads in stats.stackexchange.com/questions/tagged/separation?tab=Votes should help. $\endgroup$ – Sycorax Jun 8 '20 at 21:17

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