Fine and Gray event: death competing risk: liver transplant
multivariate
master$status_90days <- as.factor(master$status_90days)
tidycmprsk::crr(Surv(time_90days,status_90days) ~race_cat_new+age_admission+sex+MELD_Na_baseline+alb_admit+baseline_creatinine+encephalopathy_admission+alcoholic_hepatitis_admission,failcode=1,cencode=0, data = master) %>% tbl_regression(exp = TRUE)%>%
bold_p(0.01) %>% # bold p-values under a given threshold (default 0.05)
bold_labels() %>% add_n()
## 305 cases omitted due to missing values
| Characteristic |
N |
HR |
95% CI |
p-value |
| race_cat_new |
1,589 |
|
|
|
| White |
|
— |
— |
|
| Black or African American |
|
1.14 |
0.85, 1.52 |
0.4 |
| Other |
|
0.81 |
0.57, 1.14 |
0.2 |
| age_admission |
1,589 |
1.02 |
1.01, 1.03 |
<0.001 |
| sex |
1,589 |
|
|
|
| 1 |
|
— |
— |
|
| 2 |
|
1.06 |
0.89, 1.26 |
0.5 |
| MELD_Na_baseline |
1,589 |
1.06 |
1.04, 1.07 |
<0.001 |
| alb_admit |
1,589 |
0.61 |
0.53, 0.71 |
<0.001 |
| baseline_creatinine |
1,589 |
0.75 |
0.63, 0.90 |
0.002 |
| encephalopathy_admission |
1,589 |
|
|
|
| 0 |
|
— |
— |
|
| 1 |
|
1.45 |
1.21, 1.75 |
<0.001 |
| alcoholic_hepatitis_admission |
1,589 |
|
|
|
| 0 |
|
— |
— |
|
| 1 |
|
0.92 |
0.71, 1.19 |
0.5 |
Fine and Gray event: death competing risk: liver transplant
multivariate
master$status_90days <- as.factor(master$status_90days)
tidycmprsk::crr(Surv(time_90days,status_90days) ~hispanic_race+age_admission+sex+MELD_Na_baseline+alb_admit+baseline_creatinine+encephalopathy_admission+alcoholic_hepatitis_admission,failcode=1,cencode=0, data = master) %>% tbl_regression(exp = TRUE)%>%
bold_p(0.01) %>% # bold p-values under a given threshold (default 0.05)
bold_labels() %>% add_n()
## 305 cases omitted due to missing values
| Characteristic |
N |
HR |
95% CI |
p-value |
| hispanic_race |
1,589 |
0.76 |
0.55, 1.06 |
0.10 |
| age_admission |
1,589 |
1.02 |
1.01, 1.03 |
<0.001 |
| sex |
1,589 |
|
|
|
| 1 |
|
— |
— |
|
| 2 |
|
1.04 |
0.87, 1.24 |
0.7 |
| MELD_Na_baseline |
1,589 |
1.06 |
1.04, 1.07 |
<0.001 |
| alb_admit |
1,589 |
0.61 |
0.53, 0.71 |
<0.001 |
| baseline_creatinine |
1,589 |
0.76 |
0.63, 0.91 |
0.003 |
| encephalopathy_admission |
1,589 |
|
|
|
| 0 |
|
— |
— |
|
| 1 |
|
1.46 |
1.21, 1.76 |
<0.001 |
| alcoholic_hepatitis_admission |
1,589 |
|
|
|
| 0 |
|
— |
— |
|
| 1 |
|
0.93 |
0.72, 1.20 |
0.6 |
multivariate logistic regression outcome icu_admission
glm(icu_admission ~ hispanic_race+age_admission+sex+MELD_Na_baseline+alb_admit+encephalopathy_admission+alcoholic_hepatitis_admission+htn+cad+ckd, family = binomial(link = 'logit'),data=master) %>% tbl_regression(exponentiate = TRUE,
pvalue_fun = ~style_pvalue(.x, digits = 3)) %>% bold_p() %>% # bold p-values under a given threshold (default 0.05)
bold_labels()%>%
modify_spanning_header(
c(estimate, ci, p.value) ~
"**icu_admission mutilvariate logistic regression**")%>% add_n()
| Characteristic |
N |
icu_admission mutilvariate logistic regression
|
| OR |
95% CI |
p-value |
| hispanic_race |
1,876 |
0.97 |
0.70, 1.36 |
0.877 |
| age_admission |
1,876 |
0.99 |
0.98, 1.00 |
0.004 |
| sex |
1,876 |
|
|
|
| 1 |
|
— |
— |
|
| 2 |
|
0.99 |
0.81, 1.21 |
0.907 |
| MELD_Na_baseline |
1,876 |
1.04 |
1.03, 1.05 |
<0.001 |
| alb_admit |
1,876 |
0.70 |
0.61, 0.81 |
<0.001 |
| encephalopathy_admission |
1,876 |
|
|
|
| 0 |
|
— |
— |
|
| 1 |
|
1.29 |
1.06, 1.57 |
0.012 |
| alcoholic_hepatitis_admission |
1,876 |
|
|
|
| 0 |
|
— |
— |
|
| 1 |
|
0.91 |
0.69, 1.21 |
0.522 |
| htn |
1,876 |
|
|
|
| 0 |
|
— |
— |
|
| 1 |
|
0.98 |
0.80, 1.21 |
0.876 |
| cad |
1,876 |
|
|
|
| 0 |
|
— |
— |
|
| 1 |
|
1.03 |
0.79, 1.33 |
0.833 |
| ckd |
1,876 |
|
|
|
| 0 |
|
— |
— |
|
| 1 |
|
0.68 |
0.54, 0.84 |
<0.001 |
multivariate logistic regression outcome
transplant_evaluation_outcome
glm(transplant_evaluation_outcome ~ hispanic_race+age_admission+sex+MELD_Na_baseline+alb_admit+encephalopathy_admission+alcoholic_hepatitis_admission+htn+cad+ckd, family = binomial(link = 'logit'),data=master) %>% tbl_regression(exponentiate = TRUE,
pvalue_fun = ~style_pvalue(.x, digits = 3)) %>% bold_p() %>% # bold p-values under a given threshold (default 0.05)
bold_labels()%>%
modify_spanning_header(
c(estimate, ci, p.value) ~
"**transplant_evaluation_outcome mutilvariate logistic regression**") %>% add_n()
| Characteristic |
N |
transplant_evaluation_outcome mutilvariate logistic regression
|
| OR |
95% CI |
p-value |
| hispanic_race |
1,878 |
2.87 |
1.67, 4.75 |
<0.001 |
| age_admission |
1,878 |
1.00 |
0.98, 1.01 |
0.675 |
| sex |
1,878 |
|
|
|
| 1 |
|
— |
— |
|
| 2 |
|
1.73 |
1.16, 2.58 |
0.007 |
| MELD_Na_baseline |
1,878 |
1.10 |
1.08, 1.13 |
<0.001 |
| alb_admit |
1,878 |
1.28 |
0.98, 1.67 |
0.072 |
| encephalopathy_admission |
1,878 |
|
|
|
| 0 |
|
— |
— |
|
| 1 |
|
0.91 |
0.61, 1.38 |
0.652 |
| alcoholic_hepatitis_admission |
1,878 |
|
|
|
| 0 |
|
— |
— |
|
| 1 |
|
0.50 |
0.26, 0.91 |
0.030 |
| htn |
1,878 |
|
|
|
| 0 |
|
— |
— |
|
| 1 |
|
1.02 |
0.67, 1.54 |
0.937 |
| cad |
1,878 |
|
|
|
| 0 |
|
— |
— |
|
| 1 |
|
0.80 |
0.40, 1.46 |
0.483 |
| ckd |
1,878 |
|
|
|
| 0 |
|
— |
— |
|
| 1 |
|
0.85 |
0.52, 1.37 |
0.521 |
multivariate logistic regression outcome
transplant_evaluation_outcome
glm(transplant_evaluation_outcome ~ hispanic_race+age_admission+sex+MELD_Na_baseline+alb_admit+encephalopathy_admission+alcoholic_hepatitis_admission+htn+cad+ckd, family = binomial(link = 'logit'),data=master) %>% tbl_regression(exponentiate = TRUE,
pvalue_fun = ~style_pvalue(.x, digits = 3)) %>% bold_p() %>% # bold p-values under a given threshold (default 0.05)
bold_labels()%>%
modify_spanning_header(
c(estimate, ci, p.value) ~
"**transplant_evaluation_outcome mutilvariate logistic regression**")%>% add_n()
| Characteristic |
N |
transplant_evaluation_outcome mutilvariate logistic regression
|
| OR |
95% CI |
p-value |
| hispanic_race |
1,878 |
2.87 |
1.67, 4.75 |
<0.001 |
| age_admission |
1,878 |
1.00 |
0.98, 1.01 |
0.675 |
| sex |
1,878 |
|
|
|
| 1 |
|
— |
— |
|
| 2 |
|
1.73 |
1.16, 2.58 |
0.007 |
| MELD_Na_baseline |
1,878 |
1.10 |
1.08, 1.13 |
<0.001 |
| alb_admit |
1,878 |
1.28 |
0.98, 1.67 |
0.072 |
| encephalopathy_admission |
1,878 |
|
|
|
| 0 |
|
— |
— |
|
| 1 |
|
0.91 |
0.61, 1.38 |
0.652 |
| alcoholic_hepatitis_admission |
1,878 |
|
|
|
| 0 |
|
— |
— |
|
| 1 |
|
0.50 |
0.26, 0.91 |
0.030 |
| htn |
1,878 |
|
|
|
| 0 |
|
— |
— |
|
| 1 |
|
1.02 |
0.67, 1.54 |
0.937 |
| cad |
1,878 |
|
|
|
| 0 |
|
— |
— |
|
| 1 |
|
0.80 |
0.40, 1.46 |
0.483 |
| ckd |
1,878 |
|
|
|
| 0 |
|
— |
— |
|
| 1 |
|
0.85 |
0.52, 1.37 |
0.521 |
multivariate logistic regression outcome rrt
glm(rrt ~ hispanic_race+age_admission+sex+MELD_Na_baseline+alb_admit+encephalopathy_admission+alcoholic_hepatitis_admission+htn+cad+ckd, family = binomial(link = 'logit'),data=master) %>% tbl_regression(exponentiate = TRUE,
pvalue_fun = ~style_pvalue(.x, digits = 3)) %>% bold_p() %>% # bold p-values under a given threshold (default 0.05)
bold_labels()%>%
modify_spanning_header(
c(estimate, ci, p.value) ~
"**rrt mutilvariate logistic regression**")%>% add_n()
| Characteristic |
N |
rrt mutilvariate logistic regression
|
| OR |
95% CI |
p-value |
| hispanic_race |
1,875 |
1.80 |
1.20, 2.66 |
0.004 |
| age_admission |
1,875 |
0.98 |
0.97, 0.99 |
<0.001 |
| sex |
1,875 |
|
|
|
| 1 |
|
— |
— |
|
| 2 |
|
1.41 |
1.08, 1.82 |
0.010 |
| MELD_Na_baseline |
1,875 |
1.09 |
1.07, 1.11 |
<0.001 |
| alb_admit |
1,875 |
1.06 |
0.89, 1.26 |
0.543 |
| encephalopathy_admission |
1,875 |
|
|
|
| 0 |
|
— |
— |
|
| 1 |
|
1.16 |
0.89, 1.52 |
0.278 |
| alcoholic_hepatitis_admission |
1,875 |
|
|
|
| 0 |
|
— |
— |
|
| 1 |
|
0.71 |
0.49, 1.01 |
0.064 |
| htn |
1,875 |
|
|
|
| 0 |
|
— |
— |
|
| 1 |
|
0.83 |
0.63, 1.08 |
0.158 |
| cad |
1,875 |
|
|
|
| 0 |
|
— |
— |
|
| 1 |
|
1.23 |
0.85, 1.77 |
0.269 |
| ckd |
1,875 |
|
|
|
| 0 |
|
— |
— |
|
| 1 |
|
1.02 |
0.75, 1.37 |
0.903 |
multivariate logistic regression outcome icu_admission
glm(icu_admission ~ race_cat_new+age_admission+sex+MELD_Na_baseline+alb_admit+encephalopathy_admission+alcoholic_hepatitis_admission+htn+cad+ckd, family = binomial(link = 'logit'),data=master) %>% tbl_regression(exponentiate = TRUE,
pvalue_fun = ~style_pvalue(.x, digits = 3)) %>% bold_p() %>% # bold p-values under a given threshold (default 0.05)
bold_labels()%>%
modify_spanning_header(
c(estimate, ci, p.value) ~
"**icu_admission mutilvariate logistic regression**")%>% add_n()
| Characteristic |
N |
icu_admission mutilvariate logistic regression
|
| OR |
95% CI |
p-value |
| race_cat_new |
1,876 |
|
|
|
| White |
|
— |
— |
|
| Black or African American |
|
0.82 |
0.59, 1.15 |
0.258 |
| Other |
|
1.17 |
0.81, 1.68 |
0.397 |
| age_admission |
1,876 |
0.99 |
0.98, 1.00 |
0.004 |
| sex |
1,876 |
|
|
|
| 1 |
|
— |
— |
|
| 2 |
|
0.99 |
0.81, 1.20 |
0.886 |
| MELD_Na_baseline |
1,876 |
1.04 |
1.03, 1.05 |
<0.001 |
| alb_admit |
1,876 |
0.70 |
0.61, 0.80 |
<0.001 |
| encephalopathy_admission |
1,876 |
|
|
|
| 0 |
|
— |
— |
|
| 1 |
|
1.29 |
1.06, 1.57 |
0.012 |
| alcoholic_hepatitis_admission |
1,876 |
|
|
|
| 0 |
|
— |
— |
|
| 1 |
|
0.91 |
0.69, 1.21 |
0.536 |
| htn |
1,876 |
|
|
|
| 0 |
|
— |
— |
|
| 1 |
|
1.00 |
0.81, 1.22 |
0.980 |
| cad |
1,876 |
|
|
|
| 0 |
|
— |
— |
|
| 1 |
|
1.03 |
0.79, 1.34 |
0.828 |
| ckd |
1,876 |
|
|
|
| 0 |
|
— |
— |
|
| 1 |
|
0.68 |
0.55, 0.85 |
<0.001 |
multivariate logistic regression outcome
transplant_evaluation_outcome
glm(transplant_evaluation_outcome ~ race_cat_new+age_admission+sex+MELD_Na_baseline+alb_admit+encephalopathy_admission+alcoholic_hepatitis_admission+htn+cad+ckd, family = binomial(link = 'logit'),data=master) %>% tbl_regression(exponentiate = TRUE,
pvalue_fun = ~style_pvalue(.x, digits = 3)) %>% bold_p() %>% # bold p-values under a given threshold (default 0.05)
bold_labels()%>%
modify_spanning_header(
c(estimate, ci, p.value) ~
"**transplant_evaluation_outcome mutilvariate logistic regression**") %>% add_n()
| Characteristic |
N |
transplant_evaluation_outcome mutilvariate logistic regression
|
| OR |
95% CI |
p-value |
| race_cat_new |
1,878 |
|
|
|
| White |
|
— |
— |
|
| Black or African American |
|
0.97 |
0.42, 1.96 |
0.931 |
| Other |
|
3.28 |
1.90, 5.51 |
<0.001 |
| age_admission |
1,878 |
1.00 |
0.98, 1.01 |
0.606 |
| sex |
1,878 |
|
|
|
| 1 |
|
— |
— |
|
| 2 |
|
1.62 |
1.09, 2.42 |
0.017 |
| MELD_Na_baseline |
1,878 |
1.10 |
1.07, 1.13 |
<0.001 |
| alb_admit |
1,878 |
1.27 |
0.97, 1.65 |
0.081 |
| encephalopathy_admission |
1,878 |
|
|
|
| 0 |
|
— |
— |
|
| 1 |
|
0.92 |
0.61, 1.39 |
0.678 |
| alcoholic_hepatitis_admission |
1,878 |
|
|
|
| 0 |
|
— |
— |
|
| 1 |
|
0.51 |
0.27, 0.92 |
0.034 |
| htn |
1,878 |
|
|
|
| 0 |
|
— |
— |
|
| 1 |
|
1.08 |
0.71, 1.64 |
0.733 |
| cad |
1,878 |
|
|
|
| 0 |
|
— |
— |
|
| 1 |
|
0.80 |
0.41, 1.48 |
0.504 |
| ckd |
1,878 |
|
|
|
| 0 |
|
— |
— |
|
| 1 |
|
0.88 |
0.53, 1.42 |
0.615 |
multivariate logistic regression outcome
transplant_evaluation_outcome
glm(transplant_evaluation_outcome ~ race_cat_new+age_admission+sex+MELD_Na_baseline+alb_admit+encephalopathy_admission+alcoholic_hepatitis_admission+htn+cad+ckd, family = binomial(link = 'logit'),data=master) %>% tbl_regression(exponentiate = TRUE,
pvalue_fun = ~style_pvalue(.x, digits = 3)) %>% bold_p() %>% # bold p-values under a given threshold (default 0.05)
bold_labels()%>%
modify_spanning_header(
c(estimate, ci, p.value) ~
"**transplant_evaluation_outcome mutilvariate logistic regression**")%>% add_n()
| Characteristic |
N |
transplant_evaluation_outcome mutilvariate logistic regression
|
| OR |
95% CI |
p-value |
| race_cat_new |
1,878 |
|
|
|
| White |
|
— |
— |
|
| Black or African American |
|
0.97 |
0.42, 1.96 |
0.931 |
| Other |
|
3.28 |
1.90, 5.51 |
<0.001 |
| age_admission |
1,878 |
1.00 |
0.98, 1.01 |
0.606 |
| sex |
1,878 |
|
|
|
| 1 |
|
— |
— |
|
| 2 |
|
1.62 |
1.09, 2.42 |
0.017 |
| MELD_Na_baseline |
1,878 |
1.10 |
1.07, 1.13 |
<0.001 |
| alb_admit |
1,878 |
1.27 |
0.97, 1.65 |
0.081 |
| encephalopathy_admission |
1,878 |
|
|
|
| 0 |
|
— |
— |
|
| 1 |
|
0.92 |
0.61, 1.39 |
0.678 |
| alcoholic_hepatitis_admission |
1,878 |
|
|
|
| 0 |
|
— |
— |
|
| 1 |
|
0.51 |
0.27, 0.92 |
0.034 |
| htn |
1,878 |
|
|
|
| 0 |
|
— |
— |
|
| 1 |
|
1.08 |
0.71, 1.64 |
0.733 |
| cad |
1,878 |
|
|
|
| 0 |
|
— |
— |
|
| 1 |
|
0.80 |
0.41, 1.48 |
0.504 |
| ckd |
1,878 |
|
|
|
| 0 |
|
— |
— |
|
| 1 |
|
0.88 |
0.53, 1.42 |
0.615 |
multivariate logistic regression outcome rrt
table(master$rrt,master$aki_stage_4)
##
## 1 2 3 4
## 0 641 442 462 0
## 1 0 0 0 334
glm(rrt ~ race_cat_new+age_admission+sex+MELD_Na_baseline+alb_admit+encephalopathy_admission+alcoholic_hepatitis_admission+htn+cad+ckd, family = binomial(link = 'logit'),data=master) %>% tbl_regression(exponentiate = TRUE,
pvalue_fun = ~style_pvalue(.x, digits = 3)) %>% bold_p() %>% # bold p-values under a given threshold (default 0.05)
bold_labels()%>%
modify_spanning_header(
c(estimate, ci, p.value) ~
"**rrt mutilvariate logistic regression**")%>% add_n()
| Characteristic |
N |
rrt mutilvariate logistic regression
|
| OR |
95% CI |
p-value |
| race_cat_new |
1,875 |
|
|
|
| White |
|
— |
— |
|
| Black or African American |
|
1.28 |
0.82, 1.95 |
0.269 |
| Other |
|
2.46 |
1.63, 3.70 |
<0.001 |
| age_admission |
1,875 |
0.98 |
0.97, 0.99 |
<0.001 |
| sex |
1,875 |
|
|
|
| 1 |
|
— |
— |
|
| 2 |
|
1.36 |
1.04, 1.76 |
0.022 |
| MELD_Na_baseline |
1,875 |
1.09 |
1.07, 1.11 |
<0.001 |
| alb_admit |
1,875 |
1.06 |
0.89, 1.27 |
0.508 |
| encephalopathy_admission |
1,875 |
|
|
|
| 0 |
|
— |
— |
|
| 1 |
|
1.15 |
0.88, 1.51 |
0.310 |
| alcoholic_hepatitis_admission |
1,875 |
|
|
|
| 0 |
|
— |
— |
|
| 1 |
|
0.73 |
0.50, 1.04 |
0.082 |
| htn |
1,875 |
|
|
|
| 0 |
|
— |
— |
|
| 1 |
|
0.84 |
0.64, 1.10 |
0.201 |
| cad |
1,875 |
|
|
|
| 0 |
|
— |
— |
|
| 1 |
|
1.23 |
0.85, 1.78 |
0.267 |
| ckd |
1,875 |
|
|
|
| 0 |
|
— |
— |
|
| 1 |
|
1.03 |
0.76, 1.40 |
0.837 |