#Clear existing data and graphics
rm(list=ls())
graphics.off()
#Load Hmisc library
library(Hmisc)
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library(kableExtra)
library(MASS)
library(lmtest)
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library(haven)
library(gtools)
library(broom)
library(tidyverse)
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library(survival)
library(survminer)
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## logit
#Read Data
data=read.csv('Q2Neurocheck_DATA_2024-04-15_1611.csv')
#Setting Labels
label(data$record_id)="Record ID"
label(data$data_extractor)="Name of Data Extractor"
label(data$dob)="Birth Date"
label(data$age)="Age"
label(data$sex)="Sex"
label(data$race)="Race"
label(data$ethnicity_77c852)="Ethnicity"
label(data$comments_fafe4a)="Comments"
label(data$demographics_complete)="Complete?"
label(data$ed_arrival)="ED Arrival Time and Date"
label(data$discharge)="Discharge Date"
label(data$trauma_number)="Trauma Number"
label(data$account_number)="Account Number"
label(data$injury___1)="Injury (choice=SDH)"
label(data$injury___2)="Injury (choice=SAH)"
label(data$injury___3)="Injury (choice=EDH)"
label(data$injury___4)="Injury (choice=IPH)"
label(data$injury___5)="Injury (choice=DAI)"
label(data$injury___6)="Injury (choice=Skull fracture)"
label(data$injury___7)="Injury (choice=Spinal Cord Injury)"
label(data$injury___8)="Injury (choice=Central Cord Syndrome)"
label(data$injury___9)="Injury (choice=IVH)"
label(data$injury___10)="Injury (choice=Vertebral fracture)"
label(data$injury___11)="Injury (choice=Other)"
label(data$spinal_injury___1)="If spinal injury, specify level of injury (choice=Cord contusion)"
label(data$spinal_injury___2)="If spinal injury, specify level of injury (choice=C4)"
label(data$spinal_injury___3)="If spinal injury, specify level of injury (choice=C5)"
label(data$spinal_injury___4)="If spinal injury, specify level of injury (choice=C6)"
label(data$spinal_injury___5)="If spinal injury, specify level of injury (choice=Other)"
label(data$other_spine)="If other level, specifiy"
label(data$other_injury)="If other injury, specify"
label(data$neurocheck)="Neurocheck change interval"
label(data$delirium_dx)="Delirium Diagnosis"
label(data$complications___1)="Complications (choice=None)"
label(data$complications___2)="Complications (choice=Unplanned Return to OR)"
label(data$complications___3)="Complications (choice=Return to ICU)"
label(data$complications___4)="Complications (choice=Re-bleed)"
label(data$complications___5)="Complications (choice=Other)"
label(data$comments)="Comments/Findings"
label(data$injury_and_neurocheck_complete)="Complete?"
label(data$hospital_los)="Hospital LOS"
label(data$icu_los)="ICU LOS"
label(data$ventilation)="Ventilation"
label(data$days_ventilation)="Days Ventilated"
label(data$discharge_disposition)="Discharge Disposition"
label(data$outcomes_complete)="Complete?"
#Setting Units
#Setting Factors(will create new variable for factors)
data$sex.factor = factor(data$sex,levels=c("1","2","3"))
data$race.factor = factor(data$race,levels=c("1","2","3","4","5","6","7"))
data$ethnicity_77c852.factor = factor(data$ethnicity_77c852,levels=c("1","2","3"))
data$demographics_complete.factor = factor(data$demographics_complete,levels=c("0","1","2"))
data$injury___1.factor = factor(data$injury___1,levels=c("0","1"))
data$injury___2.factor = factor(data$injury___2,levels=c("0","1"))
data$injury___3.factor = factor(data$injury___3,levels=c("0","1"))
data$injury___4.factor = factor(data$injury___4,levels=c("0","1"))
data$injury___5.factor = factor(data$injury___5,levels=c("0","1"))
data$injury___6.factor = factor(data$injury___6,levels=c("0","1"))
data$injury___7.factor = factor(data$injury___7,levels=c("0","1"))
data$injury___8.factor = factor(data$injury___8,levels=c("0","1"))
data$injury___9.factor = factor(data$injury___9,levels=c("0","1"))
data$injury___10.factor = factor(data$injury___10,levels=c("0","1"))
data$injury___11.factor = factor(data$injury___11,levels=c("0","1"))
data$spinal_injury___1.factor = factor(data$spinal_injury___1,levels=c("0","1"))
data$spinal_injury___2.factor = factor(data$spinal_injury___2,levels=c("0","1"))
data$spinal_injury___3.factor = factor(data$spinal_injury___3,levels=c("0","1"))
data$spinal_injury___4.factor = factor(data$spinal_injury___4,levels=c("0","1"))
data$spinal_injury___5.factor = factor(data$spinal_injury___5,levels=c("0","1"))
data$neurocheck.factor = factor(data$neurocheck,levels=c("1","2","3","4"))
data$delirium_dx.factor = factor(data$delirium_dx,levels=c("1","0"))
data$complications___1.factor = factor(data$complications___1,levels=c("0","1"))
data$complications___2.factor = factor(data$complications___2,levels=c("0","1"))
data$complications___3.factor = factor(data$complications___3,levels=c("0","1"))
data$complications___4.factor = factor(data$complications___4,levels=c("0","1"))
data$complications___5.factor = factor(data$complications___5,levels=c("0","1"))
data$injury_and_neurocheck_complete.factor = factor(data$injury_and_neurocheck_complete,levels=c("0","1","2"))
data$ventilation.factor = factor(data$ventilation,levels=c("1","0"))
data$discharge_disposition.factor = factor(data$discharge_disposition,levels=c("0","1","2","3","4","5","6","7","8","9","10","11","12","13","14","15"))
data$outcomes_complete.factor = factor(data$outcomes_complete,levels=c("0","1","2"))
levels(data$sex.factor)=c("Male","Female","Other")
levels(data$race.factor)=c("White/Caucasian","Black/African-American","Asian","Native American","Native Hawaiian or Other Pacific Islander","Other","Unknown")
levels(data$ethnicity_77c852.factor)=c("Non-Hispanic","Hispanic","Declined or Unknown")
levels(data$demographics_complete.factor)=c("Incomplete","Unverified","Complete")
levels(data$injury___1.factor)=c("Unchecked","Checked")
levels(data$injury___2.factor)=c("Unchecked","Checked")
levels(data$injury___3.factor)=c("Unchecked","Checked")
levels(data$injury___4.factor)=c("Unchecked","Checked")
levels(data$injury___5.factor)=c("Unchecked","Checked")
levels(data$injury___6.factor)=c("Unchecked","Checked")
levels(data$injury___7.factor)=c("Unchecked","Checked")
levels(data$injury___8.factor)=c("Unchecked","Checked")
levels(data$injury___9.factor)=c("Unchecked","Checked")
levels(data$injury___10.factor)=c("Unchecked","Checked")
levels(data$injury___11.factor)=c("Unchecked","Checked")
levels(data$spinal_injury___1.factor)=c("Unchecked","Checked")
levels(data$spinal_injury___2.factor)=c("Unchecked","Checked")
levels(data$spinal_injury___3.factor)=c("Unchecked","Checked")
levels(data$spinal_injury___4.factor)=c("Unchecked","Checked")
levels(data$spinal_injury___5.factor)=c("Unchecked","Checked")
levels(data$neurocheck.factor)=c("8-24 hours","24-48 hours",">48 hours","< 8 hours")
levels(data$delirium_dx.factor)=c("Yes","No")
levels(data$complications___1.factor)=c("Unchecked","Checked")
levels(data$complications___2.factor)=c("Unchecked","Checked")
levels(data$complications___3.factor)=c("Unchecked","Checked")
levels(data$complications___4.factor)=c("Unchecked","Checked")
levels(data$complications___5.factor)=c("Unchecked","Checked")
levels(data$injury_and_neurocheck_complete.factor)=c("Incomplete","Unverified","Complete")
levels(data$ventilation.factor)=c("Yes","No")
levels(data$discharge_disposition.factor)=c("AHR-ROUTINE DISCHARGE","ATE-TRAN/DSCH TO EXTND SKILLED NURS","ATR-TRANSFER TO REHAB CENTER","AMA-LEFT AGAINST MEDICAL ADVICE","ATH-TRANS TO SHORT-TERM GEN HOSP","ATP-TRANS TO PSYCHIATRIC FACILITY","ATW-T/DC HHC RELATED TO ADMIT","ARS-R/DC HOME CARE NOT RELATED TO ADMIT","ATY-TRANSFER TO LONG TERM CARE HOSPITAL","ATV-T/DC TO HHC RELATED TO INPT ADMIT","DBZ-DIED IN MEDICAL FACILITY, AUTOPSY UN","DBN-DIED IN MEDICAL FACILITY, NO AUTOPSY","D7N-OTHER DEATH - NO AUTOPSY","ATA-TRAN/DSCH TO ACUTE CARE INST.","ATU -T/DC HOSP OWNED REHAB UNIT","ATF-TRAN/DSCH TO STATE FACILITY")
levels(data$outcomes_complete.factor)=c("Incomplete","Unverified","Complete")
# Creating new variables
## Creating a new numerical data set "dataN" and a categorical data set "data"
dataN <- data
## Injury classification
data$injury[data$injury___1 == 1] <- "SDH"
data$injury[data$injury___2 == 1] <- "SAH"
data$injury[data$injury___3 == 1] <- "EDH"
data$injury[data$injury___4 == 1] <- "IPH"
data$injury[data$injury___5 == 1] <- "DAI"
data$injury[data$injury___6 == 1] <- "Skull Fracture"
data$injury[data$injury___7 == 1] <- "Spinal Cord Injury"
data$injury[data$injury___8 == 1] <- "Central Cord Syndrome"
data$injury[data$injury___9 == 1] <- "IVH"
data$injury[data$injury___10 == 1] <- "Vertebral Fracture"
data$injury[data$injury___11 == 1] <- "Other Injuries"
## Sex Classification
data <- mutate(data, sex = factor(sex, levels = c(1, 2, 3),
labels = c("Male", "Female", "Other")))
## Race Classification
data <- mutate(data, race = factor(race, levels = c(1, 2, 3, 4, 5, 6, 7),
labels = c("White", "Black", "Asian", "Native American", "Native Hawaiian or Other Pacific Islander", "Other", "Unknown")))
##Ethnicity Classification
data <- mutate(data, ethnicity = factor(ethnicity_77c852, levels = c(1, 2, 3),
labels = c("Non-Hispanic", "Hispanic", "Declined or Unknown")))
## Neuro check Intervals
data <- mutate(data, neurocheckinterval = factor(neurocheck, levels = c(4, 1, 2, 3),
labels = c("<8", "8-24", "24-48", ">48")))
## Creating new variables combining 24 hours or less (one day), and two days or more ( two day)
data <- mutate(data, ncdays = recode(neurocheck, "c('1', '4')='oneday';c('2', '3') = 'twodays'"))
## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `ncdays = recode(neurocheck, "c('1', '4')='oneday';c('2', '3') =
## 'twodays'")`.
## Caused by warning in `recode()`:
## ! NAs introduced by coercion
## Delirium
data$delirium[data$delirium_dx == 1] <- "Yes"
data$delirium[data$delirium_dx == 0] <- "No"
data <- data %>%
mutate(delirium_dx = ifelse(delirium == "No",0,1))
## Complications & death
### Categorical complications
data$complications[data$complications___1 == 1] <- "None"
data$complications[data$complications___2 == 1] <- "Unplanned Return to OR"
data$complications[data$complications___3 == 1] <- "Return to ICU"
data$complications[data$complications___4 == 1] <- "Re-bleed"
data$complications[data$complications___5 == 1] <- "Other"
### Numeric complications
data <- data %>%
mutate(complications_dx = ifelse(complications == "None",0,1))
### Categorical death outcome
data <- mutate(data, died = factor(discharge_disposition, levels = c(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15),
labels = c("No", "No", "No", "No", "No", "No", "No", "No", "No", "No", "Yes", "Yes", "Yes", "No", "No", "No")))
### Numeric death outcome
data <- data %>%
mutate(died_dx = ifelse(died == "No",0,1))
data <- mutate(data, outcomes = factor(discharge_disposition, levels =
c(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15), labels =
c("Routine Discharge", "Transfer/discharge to extended skilled nursing
center", "Transfer to rehab center", "Left against medical advice",
"Transfer to short-term general hospital", "Transfer to psychiatric
facility", "Transfer/discharge to HHC related to admit", "R/DC home care
not related to admit", "Transfer to long term care hospital",
"Transfer/discharge to HHC related to inpatient admit", "Died in medical
facility, Autopsy UN", "Died in medical facility, no autopsy", "Other
death - No autopsy", "Transfer/discharge to acute care institution",
"Transfer/discharge to hospital-owned rehab unit", "Transfer/discharge
to state facility")))
#Explore data
## Demographics
### Race
count(data, race) %>%
mutate(perc = n / sum(n) * 100) %>%
kable(digits = 2, col.names = c("Race", "Frequency", "Percentage"))
| White |
42 |
35.29 |
| Black |
50 |
42.02 |
| Asian |
2 |
1.68 |
| Other |
16 |
13.45 |
| Unknown |
9 |
7.56 |
### Comparing race in neuro check interval groups
select(data, neurocheckinterval, race) %>%
group_by(neurocheckinterval, race) %>%
count() %>%
mutate(perc = n / sum(n) * 100) %>%
kable(digits = 2, col.names = c("Neurocheck Intervals", "Race", "Total", "Percentage"))
| <8 |
White |
7 |
100 |
| <8 |
Black |
4 |
100 |
| <8 |
Asian |
1 |
100 |
| <8 |
Other |
1 |
100 |
| 8-24 |
White |
16 |
100 |
| 8-24 |
Black |
15 |
100 |
| 8-24 |
Asian |
1 |
100 |
| 8-24 |
Other |
7 |
100 |
| 8-24 |
Unknown |
6 |
100 |
| 24-48 |
White |
3 |
100 |
| 24-48 |
Black |
14 |
100 |
| 24-48 |
Other |
4 |
100 |
| >48 |
White |
16 |
100 |
| >48 |
Black |
17 |
100 |
| >48 |
Other |
4 |
100 |
| >48 |
Unknown |
3 |
100 |
### Ethnicity
count(data, ethnicity) %>%
mutate(perc = n / sum(n) * 100) %>%
kable(digits = 2, col.names = c("Ethnicity", "Frequency", "Percentage"))
| Non-Hispanic |
83 |
69.75 |
| Hispanic |
8 |
6.72 |
| Declined or Unknown |
28 |
23.53 |
### Age
summarize(data, mean(age, na.rm = TRUE), sd(age, na.rm = TRUE), min(age, na.rm = TRUE), max(age, na.rm = TRUE)) %>%
kable(., digits = 2, col.names = c("Mean Age", "SD of Age", "Min Age", "Max Age"))
### age statistic by neuro check intervals
group_by(data, neurocheckinterval) %>%
summarize(mean(age, na.rm = TRUE), sd(age, na.rm = TRUE), min(age, na.rm = TRUE), max(age, na.rm = TRUE)) %>%
kable(., digits = 2, col.names = c("Neurocheck Interval", "Mean Age", "SD of Age", "Min Age", "Max Age"))
| <8 |
58.92 |
20.00 |
27 |
88 |
| 8-24 |
54.84 |
21.13 |
15 |
94 |
| 24-48 |
48.67 |
20.64 |
17 |
82 |
| >48 |
53.52 |
24.36 |
15 |
99 |
### Sex
count(data, sex) %>%
mutate(perc = n / sum(n) * 100) %>%
kable(digits = 2, col.names = c("Sex", "Frequency", "Percentage"))
| Male |
82 |
68.91 |
| Female |
37 |
31.09 |
### Sex is analyzed according to neuro check interval group
group_by(data, neurocheckinterval) %>%
count(sex) %>% # the count of sex is taken to formulate the total and percentages
mutate(perc = n / sum(n) * 100) %>%
kable(digits = 2, col.names = c("Neuro check interval group", "Sex", "Total", "Percentage"))
| <8 |
Male |
9 |
69.23 |
| <8 |
Female |
4 |
30.77 |
| 8-24 |
Male |
32 |
71.11 |
| 8-24 |
Female |
13 |
28.89 |
| 24-48 |
Male |
15 |
71.43 |
| 24-48 |
Female |
6 |
28.57 |
| >48 |
Male |
26 |
65.00 |
| >48 |
Female |
14 |
35.00 |
## Medical History
### Neuro check Intervals
count(data, ncdays) %>%
mutate(prop = n / sum(n)) %>%
kable(digits = 2, col.names = c("Neurocheck Intervals", "Frequency", "Proportion"))
| oneday |
58 |
0.49 |
| twodays |
61 |
0.51 |
### Injury
count(data, injury) %>%
mutate(prop = n / sum(n)) %>%
kable(digits = 2, col.names = c("Injury Type", "Frequency", "Proportion"))
| Central Cord Syndrome |
9 |
0.08 |
| DAI |
6 |
0.05 |
| EDH |
3 |
0.03 |
| IPH |
12 |
0.10 |
| IVH |
2 |
0.02 |
| Other Injuries |
16 |
0.13 |
| SAH |
24 |
0.20 |
| SDH |
18 |
0.15 |
| Skull Fracture |
15 |
0.13 |
| Vertebral Fracture |
14 |
0.12 |
### Comparing delirium counts in neuro check interval groups
select(data, ncdays, delirium) %>%
group_by(ncdays) %>%
count(delirium) %>%
mutate(perc = n / sum(n) * 100) %>%
kable(digits = 2, col.names = c("Neurocheck Intervals", "Delirium", "Total", "Percentage"))
| oneday |
No |
54 |
93.10 |
| oneday |
Yes |
4 |
6.90 |
| twodays |
No |
48 |
78.69 |
| twodays |
Yes |
13 |
21.31 |
### Hospital stays in each neuro check interval group
group_by(data, neurocheckinterval) %>%
summarize(mean(hospital_los, na.rm = TRUE), sd(hospital_los, na.rm = TRUE), min(hospital_los, na.rm = TRUE), max(hospital_los, na.rm = TRUE))%>%
kable(., digits = 2, col.names = c("Neurocheck Interval", "Mean Hospital LOS", "SD of Hospital LOS", "Min Hospital LOS", "Max Hospital LOS"))
| <8 |
8.64 |
24.08 |
0.18 |
88.59 |
| 8-24 |
8.35 |
9.03 |
0.52 |
45.60 |
| 24-48 |
10.04 |
8.40 |
1.79 |
37.10 |
| >48 |
29.75 |
44.02 |
1.80 |
191.78 |
### Comparing complications counts in neuro check interval groups
select(data, neurocheckinterval, complications) %>%
group_by(neurocheckinterval) %>%
count(complications) %>%
mutate(perc = n / sum(n) * 100) %>%
kable(digits = 2, col.names = c("Neurocheck Intervals", "Complications", "Total", "Percentage"))
| <8 |
None |
11 |
84.62 |
| <8 |
Other |
1 |
7.69 |
| <8 |
Return to ICU |
1 |
7.69 |
| 8-24 |
None |
35 |
77.78 |
| 8-24 |
Other |
5 |
11.11 |
| 8-24 |
Re-bleed |
2 |
4.44 |
| 8-24 |
Return to ICU |
2 |
4.44 |
| 8-24 |
Unplanned Return to OR |
1 |
2.22 |
| 24-48 |
None |
17 |
80.95 |
| 24-48 |
Other |
2 |
9.52 |
| 24-48 |
Return to ICU |
1 |
4.76 |
| 24-48 |
NA |
1 |
4.76 |
| >48 |
None |
23 |
57.50 |
| >48 |
Other |
10 |
25.00 |
| >48 |
Re-bleed |
3 |
7.50 |
| >48 |
Return to ICU |
3 |
7.50 |
| >48 |
Unplanned Return to OR |
1 |
2.50 |
ggplot(data, aes(x = hospital_los)) +
geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Comparing linearity
## Regression Models
### Logistic regression model is created as our variable of interest, delirium is binary
#### the final model will be selected by backwards elimination
model1 <- glm(delirium_dx ~ ncdays + hospital_los + icu_los + ventilation +
complications + age + sex + race + ethnicity, data = data, family = 'binomial')
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
summary(model1)
##
## Call:
## glm(formula = delirium_dx ~ ncdays + hospital_los + icu_los +
## ventilation + complications + age + sex + race + ethnicity,
## family = "binomial", data = data)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.41448 1.70469 -3.763 0.000168 ***
## ncdaystwodays 1.69766 0.83002 2.045 0.040823 *
## hospital_los 0.05114 0.02647 1.932 0.053371 .
## icu_los -0.13069 0.10033 -1.303 0.192708
## ventilation 0.22357 0.97921 0.228 0.819396
## complicationsOther 1.61440 0.99576 1.621 0.104959
## complicationsRe-bleed 2.11032 1.35949 1.552 0.120594
## complicationsReturn to ICU -0.44339 2.19609 -0.202 0.839996
## complicationsUnplanned Return to OR -17.91063 5785.85227 -0.003 0.997530
## age 0.04424 0.01931 2.291 0.021975 *
## sexFemale 0.77921 0.82937 0.940 0.347460
## raceBlack -0.38765 0.75124 -0.516 0.605843
## raceAsian -16.99351 7461.73837 -0.002 0.998183
## raceOther -18.79681 2045.28688 -0.009 0.992667
## raceUnknown -2.25961 1.77423 -1.274 0.202815
## ethnicityHispanic -16.10332 3008.49924 -0.005 0.995729
## ethnicityDeclined or Unknown 1.42157 0.91086 1.561 0.118597
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 93.664 on 117 degrees of freedom
## Residual deviance: 60.071 on 101 degrees of freedom
## (1 observation deleted due to missingness)
## AIC: 94.071
##
## Number of Fisher Scoring iterations: 18
#### AIC = 94.1
#### Exponentiating coefficients to get OR
model1 %>% coef() %>% exp()
## (Intercept) ncdaystwodays
## 1.637669e-03 5.461130e+00
## hospital_los icu_los
## 1.052474e+00 8.774924e-01
## ventilation complicationsOther
## 1.250539e+00 5.024865e+00
## complicationsRe-bleed complicationsReturn to ICU
## 8.250894e+00 6.418593e-01
## complicationsUnplanned Return to OR age
## 1.665376e-08 1.045232e+00
## sexFemale raceBlack
## 2.179760e+00 6.786469e-01
## raceAsian raceOther
## 4.166892e-08 6.865150e-09
## raceUnknown ethnicityHispanic
## 1.043911e-01 1.014885e-07
## ethnicityDeclined or Unknown
## 4.143634e+00
#### CIs
model1 %>% confint() %>% exp()
## Waiting for profiling to be done...
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## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## 2.5 % 97.5 %
## (Intercept) 3.386725e-05 3.030046e-02
## ncdaystwodays 1.164463e+00 3.224098e+01
## hospital_los 1.006240e+00 1.115756e+00
## icu_los 7.017301e-01 9.940804e-01
## ventilation 1.620958e-01 8.183850e+00
## complicationsOther 7.267995e-01 4.018714e+01
## complicationsRe-bleed 5.347028e-01 1.313337e+02
## complicationsReturn to ICU 6.029746e-03 3.587667e+01
## complicationsUnplanned Return to OR NA Inf
## age 1.009283e+00 1.090333e+00
## sexFemale 4.242927e-01 1.168694e+01
## raceBlack 1.486607e-01 2.999140e+00
## raceAsian NA Inf
## raceOther NA 9.059206e+52
## raceUnknown 1.648945e-03 2.379513e+00
## ethnicityHispanic NA 1.844250e+80
## ethnicityDeclined or Unknown 6.547926e-01 2.612858e+01
#### Tabulated ORs and 95% CIs
table1 <- tidy(model1, conf.int = T) %>% mutate(.,
or = exp(estimate),
lb = exp(conf.low),
ub = exp(conf.high)) %>%
filter(., term != "(Intercept)") %>%
dplyr::select(., term, or, lb, ub, p.value)
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## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
table1
## # A tibble: 16 × 5
## term or lb ub p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 ncdaystwodays 5.46 1.16 3.22e+ 1 0.0408
## 2 hospital_los 1.05 1.01 1.12e+ 0 0.0534
## 3 icu_los 0.877 0.702 9.94e- 1 0.193
## 4 ventilation 1.25 0.162 8.18e+ 0 0.819
## 5 complicationsOther 5.02 0.727 4.02e+ 1 0.105
## 6 complicationsRe-bleed 8.25 0.535 1.31e+ 2 0.121
## 7 complicationsReturn to ICU 0.642 0.00603 3.59e+ 1 0.840
## 8 complicationsUnplanned Return to OR 0.0000000167 NA Inf 0.998
## 9 age 1.05 1.01 1.09e+ 0 0.0220
## 10 sexFemale 2.18 0.424 1.17e+ 1 0.347
## 11 raceBlack 0.679 0.149 3.00e+ 0 0.606
## 12 raceAsian 0.0000000417 NA Inf 0.998
## 13 raceOther 0.00000000687 NA 9.06e+52 0.993
## 14 raceUnknown 0.104 0.00165 2.38e+ 0 0.203
## 15 ethnicityHispanic 0.000000101 NA 1.84e+80 0.996
## 16 ethnicityDeclined or Unknown 4.14 0.655 2.61e+ 1 0.119
kable(table1, digits = 2, col.names = c("Variables", "OR", "Lower Bound", "Upper Bound", "P value"),
caption = ("Table 1 showing ORs associated with delirium when confounders are analyzed"))
Table 1 showing ORs associated with delirium when confounders
are analyzed
| ncdaystwodays |
5.46 |
1.16 |
3.224000e+01 |
0.04 |
| hospital_los |
1.05 |
1.01 |
1.120000e+00 |
0.05 |
| icu_los |
0.88 |
0.70 |
9.900000e-01 |
0.19 |
| ventilation |
1.25 |
0.16 |
8.180000e+00 |
0.82 |
| complicationsOther |
5.02 |
0.73 |
4.019000e+01 |
0.10 |
| complicationsRe-bleed |
8.25 |
0.53 |
1.313300e+02 |
0.12 |
| complicationsReturn to ICU |
0.64 |
0.01 |
3.588000e+01 |
0.84 |
| complicationsUnplanned Return to OR |
0.00 |
NA |
Inf |
1.00 |
| age |
1.05 |
1.01 |
1.090000e+00 |
0.02 |
| sexFemale |
2.18 |
0.42 |
1.169000e+01 |
0.35 |
| raceBlack |
0.68 |
0.15 |
3.000000e+00 |
0.61 |
| raceAsian |
0.00 |
NA |
Inf |
1.00 |
| raceOther |
0.00 |
NA |
9.059206e+52 |
0.99 |
| raceUnknown |
0.10 |
0.00 |
2.380000e+00 |
0.20 |
| ethnicityHispanic |
0.00 |
NA |
1.844250e+80 |
1.00 |
| ethnicityDeclined or Unknown |
4.14 |
0.65 |
2.613000e+01 |
0.12 |
### Analyzing ventilation requirement
model2 <- glm(ventilation ~ ncdays +
age + sex +race + ethnicity, data = data, family = "binomial")
#### Exponentiating coefficients to get ORs
model2 %>% coef() %>% exp()
## (Intercept) ncdaystwodays
## 6.093688e-01 3.804914e+00
## age sexFemale
## 9.818552e-01 5.034488e-01
## raceBlack raceAsian
## 1.308685e+00 1.874944e-06
## raceOther raceUnknown
## 1.641744e+00 9.231886e-01
## ethnicityHispanic ethnicityDeclined or Unknown
## 1.305437e+00 9.868584e-01
#### CIs
model2 %>% confint() %>% exp()
## Waiting for profiling to be done...
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## 2.5 % 97.5 %
## (Intercept) 0.14304622 2.491625e+00
## ncdaystwodays 1.62226932 9.454274e+00
## age 0.96157224 1.001546e+00
## sexFemale 0.17109327 1.381192e+00
## raceBlack 0.49157474 3.526995e+00
## raceAsian NA 2.632105e+63
## raceOther 0.39835369 6.807670e+00
## raceUnknown 0.09907613 6.437338e+00
## ethnicityHispanic 0.20289613 7.328431e+00
## ethnicityDeclined or Unknown 0.30081864 3.110345e+00
#### Tabulated ORs and 95% CIs
table2 <- tidy(model2, conf.int = T) %>% mutate(.,
OR = exp(estimate),
lb = exp(conf.low),
ub = exp(conf.high)) %>%
filter(., term != "(Intercept)") %>%
dplyr::select(., term, OR, lb, ub, p.value)
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
table2
## # A tibble: 9 × 5
## term OR lb ub p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 ncdaystwodays 3.80 1.62 9.45e 0 0.00278
## 2 age 0.982 0.962 1.00e 0 0.0758
## 3 sexFemale 0.503 0.171 1.38e 0 0.193
## 4 raceBlack 1.31 0.492 3.53e 0 0.590
## 5 raceAsian 0.00000187 NA 2.63e63 0.990
## 6 raceOther 1.64 0.398 6.81e 0 0.489
## 7 raceUnknown 0.923 0.0991 6.44e 0 0.938
## 8 ethnicityHispanic 1.31 0.203 7.33e 0 0.765
## 9 ethnicityDeclined or Unknown 0.987 0.301 3.11e 0 0.982
kable(table2, digits = 4, col.names = c("Variables", "OR", "Lower Bound", "Upper Bound", "P value"),
caption = ("Table 2 showing odds of ventilation requirement"))
Table 2 showing odds of ventilation requirement
| ncdaystwodays |
3.8049 |
1.6223 |
9.454300e+00 |
0.0028 |
| age |
0.9819 |
0.9616 |
1.001500e+00 |
0.0758 |
| sexFemale |
0.5034 |
0.1711 |
1.381200e+00 |
0.1932 |
| raceBlack |
1.3087 |
0.4916 |
3.527000e+00 |
0.5897 |
| raceAsian |
0.0000 |
NA |
2.632105e+63 |
0.9897 |
| raceOther |
1.6417 |
0.3984 |
6.807700e+00 |
0.4887 |
| raceUnknown |
0.9232 |
0.0991 |
6.437300e+00 |
0.9381 |
| ethnicityHispanic |
1.3054 |
0.2029 |
7.328400e+00 |
0.7652 |
| ethnicityDeclined or Unknown |
0.9869 |
0.3008 |
3.110300e+00 |
0.9821 |
### Analyzing deaths
model3 <- glm(died_dx ~ age + sex + race + ethnicity, data = data, family = "binomial")
#### Exponentiating coefficients to get the ORs
model3 %>% coef() %>% exp()
## (Intercept) age
## 1.359976e-02 1.036016e+00
## sexFemale raceBlack
## 8.956696e-01 1.821660e+00
## raceAsian raceOther
## 6.414924e-08 3.523321e+00
## raceUnknown ethnicityHispanic
## 1.147629e+00 3.886994e-08
## ethnicityDeclined or Unknown
## 1.179545e+00
#### CIs
model3 %>% confint() %>% exp()
## Waiting for profiling to be done...
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## 2.5 % 97.5 %
## (Intercept) 1.057829e-03 1.098705e-01
## age 1.007050e+00 1.069977e+00
## sexFemale 2.197710e-01 3.261261e+00
## raceBlack 4.917931e-01 7.381047e+00
## raceAsian NA 4.025383e+299
## raceOther 5.014278e-01 2.382420e+01
## raceUnknown 4.448734e-02 1.442147e+01
## ethnicityHispanic 1.960837e-199 6.279297e+41
## ethnicityDeclined or Unknown 2.505681e-01 4.661995e+00
#### Tabulated ORs and 95% CIs
table3 <- tidy(model3, conf.int = T) %>% mutate(.,
or = exp(estimate),
lb = exp(conf.low),
ub = exp(conf.high)) %>%
filter(., term != "(Intercept)") %>%
dplyr::select(., term, or, lb, ub, p.value)
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
table3
## # A tibble: 8 × 5
## term or lb ub p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 age 1.04 1.01e+ 0 1.07e 0 0.0205
## 2 sexFemale 0.896 2.20e- 1 3.26e 0 0.871
## 3 raceBlack 1.82 4.92e- 1 7.38e 0 0.377
## 4 raceAsian 0.0000000641 NA 4.03e299 0.997
## 5 raceOther 3.52 5.01e- 1 2.38e 1 0.190
## 6 raceUnknown 1.15 4.45e- 2 1.44e 1 0.920
## 7 ethnicityHispanic 0.0000000389 1.96e-199 6.28e 41 0.994
## 8 ethnicityDeclined or Unknown 1.18 2.51e- 1 4.66e 0 0.822
kable(table3, digits = 4, col.names = c("Variables", "OR", "Lower Bound", "Upper Bound", "P value"),
caption = ("Table 3 showing odds of death"))
Table 3 showing odds of death
| age |
1.0360 |
1.0070 |
1.070000e+00 |
0.0205 |
| sexFemale |
0.8957 |
0.2198 |
3.261300e+00 |
0.8709 |
| raceBlack |
1.8217 |
0.4918 |
7.381000e+00 |
0.3774 |
| raceAsian |
0.0000 |
NA |
4.025383e+299 |
0.9971 |
| raceOther |
3.5233 |
0.5014 |
2.382420e+01 |
0.1899 |
| raceUnknown |
1.1476 |
0.0445 |
1.442150e+01 |
0.9197 |
| ethnicityHispanic |
0.0000 |
0.0000 |
6.279297e+41 |
0.9937 |
| ethnicityDeclined or Unknown |
1.1795 |
0.2506 |
4.662000e+00 |
0.8216 |