Get data into R
get the names of the covariates only removed
“log_na_admit”,“creatinine_admission”,“log_inr_admit”,“log_tb_admit”
covariates <- setdiff(names(ckd_data), c("time_90days","status_90days"))
covariates <- covariates[!covariates %in% c("log_na_admit","creatinine_admission","log_inr_admit","log_tb_admit")]
new_covariates <- paste(covariates, collapse='+')
Model 1 for all patients
ckd_variables <- ckd_data %>% select(ckd1,age_admission,White1,sex2,hispanic_race1,liver_transplant_listed1,siteindiana:siteyale,MELD_Na_baseline)
Model1 <- crr(ftime=ckd_data$time_90days,fstatus=ckd_data$status_90days,cov1=ckd_variables,failcode=1, cencode=0)
summary(Model1)
## Competing Risks Regression
##
## Call:
## crr(ftime = ckd_data$time_90days, fstatus = ckd_data$status_90days,
## cov1 = ckd_variables, failcode = 1, cencode = 0)
##
## coef exp(coef) se(coef) z p-value
## ckd1 -0.31038 0.733 0.09427 -3.29251 9.9e-04
## age_admission 0.01755 1.018 0.00366 4.79529 1.6e-06
## White1 -0.21653 0.805 0.10007 -2.16386 3.0e-02
## sex2 0.08753 1.091 0.07838 1.11667 2.6e-01
## hispanic_race1 -0.25750 0.773 0.15564 -1.65444 9.8e-02
## liver_transplant_listed1 -1.37648 0.252 0.15253 -9.02453 0.0e+00
## siteindiana 0.47444 1.607 0.15497 3.06143 2.2e-03
## sitejacksonville -0.35533 0.701 0.27499 -1.29215 2.0e-01
## sitekentukey 0.97202 2.643 0.17179 5.65817 1.5e-08
## siteMCW 0.35416 1.425 0.17776 1.99232 4.6e-02
## sitemgh 0.07352 1.076 0.14870 0.49439 6.2e-01
## sitemichigan 0.17726 1.194 0.19539 0.90721 3.6e-01
## siteoschner 0.56452 1.759 0.18102 3.11860 1.8e-03
## siterochester -0.04899 0.952 0.16781 -0.29193 7.7e-01
## siteusc 0.00154 1.002 0.27938 0.00553 1.0e+00
## siteyale -0.04056 0.960 0.16572 -0.24475 8.1e-01
## MELD_Na_baseline 0.06793 1.070 0.00542 12.53955 0.0e+00
##
## exp(coef) exp(-coef) 2.5% 97.5%
## ckd1 0.733 1.364 0.609 0.882
## age_admission 1.018 0.983 1.010 1.025
## White1 0.805 1.242 0.662 0.980
## sex2 1.091 0.916 0.936 1.273
## hispanic_race1 0.773 1.294 0.570 1.049
## liver_transplant_listed1 0.252 3.961 0.187 0.340
## siteindiana 1.607 0.622 1.186 2.178
## sitejacksonville 0.701 1.427 0.409 1.202
## sitekentukey 2.643 0.378 1.888 3.701
## siteMCW 1.425 0.702 1.006 2.019
## sitemgh 1.076 0.929 0.804 1.440
## sitemichigan 1.194 0.838 0.814 1.751
## siteoschner 1.759 0.569 1.233 2.508
## siterochester 0.952 1.050 0.685 1.323
## siteusc 1.002 0.998 0.579 1.732
## siteyale 0.960 1.041 0.694 1.329
## MELD_Na_baseline 1.070 0.934 1.059 1.082
##
## Num. cases = 1949
## Pseudo Log-likelihood = -5055
## Pseudo likelihood ratio test = 404 on 17 df,
Model 1 for all patients with log_na_admit creatinine_admission
log_inr_admit log_tb_admit
ckd_variables_lab <- ckd_data %>% select(ckd1,age_admission,White1,sex2,hispanic_race1,liver_transplant_listed1,siteindiana:siteyale,log_na_admit,creatinine_admission,log_inr_admit,log_tb_admit)
Model1_lab <- crr(ftime=ckd_data$time_90days,fstatus=ckd_data$status_90days,cov1=ckd_variables_lab,failcode=1, cencode=0)
summary(Model1_lab)
## Competing Risks Regression
##
## Call:
## crr(ftime = ckd_data$time_90days, fstatus = ckd_data$status_90days,
## cov1 = ckd_variables_lab, failcode = 1, cencode = 0)
##
## coef exp(coef) se(coef) z p-value
## ckd1 -0.1947 0.823 0.09859 -1.975 4.8e-02
## age_admission 0.0238 1.024 0.00385 6.173 6.7e-10
## White1 -0.2032 0.816 0.10018 -2.029 4.2e-02
## sex2 0.0877 1.092 0.07916 1.108 2.7e-01
## hispanic_race1 -0.2384 0.788 0.14900 -1.600 1.1e-01
## liver_transplant_listed1 -1.3895 0.249 0.15543 -8.940 0.0e+00
## siteindiana 0.3818 1.465 0.15509 2.462 1.4e-02
## sitejacksonville -0.3657 0.694 0.27137 -1.348 1.8e-01
## sitekentukey 0.9672 2.631 0.17247 5.608 2.0e-08
## siteMCW 0.2750 1.317 0.17889 1.537 1.2e-01
## sitemgh 0.0296 1.030 0.14943 0.198 8.4e-01
## sitemichigan 0.1391 1.149 0.19563 0.711 4.8e-01
## siteoschner 0.5236 1.688 0.18527 2.826 4.7e-03
## siterochester -0.0819 0.921 0.16999 -0.482 6.3e-01
## siteusc -0.1303 0.878 0.28825 -0.452 6.5e-01
## siteyale 0.0279 1.028 0.16716 0.167 8.7e-01
## log_na_admit -0.0993 0.905 0.08152 -1.218 2.2e-01
## creatinine_admission 0.0569 1.059 0.02622 2.169 3.0e-02
## log_inr_admit 0.6650 1.944 0.11452 5.807 6.4e-09
## log_tb_admit 0.4008 1.493 0.03824 10.481 0.0e+00
##
## exp(coef) exp(-coef) 2.5% 97.5%
## ckd1 0.823 1.215 0.678 0.999
## age_admission 1.024 0.977 1.016 1.032
## White1 0.816 1.225 0.671 0.993
## sex2 1.092 0.916 0.935 1.275
## hispanic_race1 0.788 1.269 0.588 1.055
## liver_transplant_listed1 0.249 4.013 0.184 0.338
## siteindiana 1.465 0.683 1.081 1.985
## sitejacksonville 0.694 1.442 0.408 1.181
## sitekentukey 2.631 0.380 1.876 3.689
## siteMCW 1.317 0.760 0.927 1.869
## sitemgh 1.030 0.971 0.768 1.380
## sitemichigan 1.149 0.870 0.783 1.686
## siteoschner 1.688 0.592 1.174 2.427
## siterochester 0.921 1.085 0.660 1.286
## siteusc 0.878 1.139 0.499 1.544
## siteyale 1.028 0.973 0.741 1.427
## log_na_admit 0.905 1.104 0.772 1.062
## creatinine_admission 1.059 0.945 1.005 1.114
## log_inr_admit 1.944 0.514 1.554 2.434
## log_tb_admit 1.493 0.670 1.385 1.609
##
## Num. cases = 1949
## Pseudo Log-likelihood = -5022
## Pseudo likelihood ratio test = 470 on 20 df,
model selection for all patients
Backward selection based on the BIC for all patients
sfgBIC$fit
##
## Right-censored response of a competing.risks model
##
## No.Observations: 1949
##
## Pattern:
##
## Cause event right.censored
## 1 721 0
## 2 118 0
## unknown 0 1110
##
##
## Fine-Gray model: analysis of cause 1
##
## Competing Risks Regression
##
## Call:
## riskRegression::FGR(formula = Hist(time_90days, status_90days) ~
## age_admission + hispanic_race1 + liver_transplant_listed1 +
## final_type_of_aki2 + final_type_of_aki3 + final_type_of_aki4 +
## final_type_of_aki5 + siteindiana + sitejacksonville +
## sitekentukey + siterochester + siteyale + etiology_cirrhosis2 +
## etiology_cirrhosis4 + etiology_cirrhosis6 + etiology_cirrhosis7 +
## albumin_given_admission1 + lactulose1 + rifaximin1 +
## beta_blockers1 + ckd1 + encephalopathy_admission1 + hcc_admission1 +
## alcoholic_hepatitis_admission1 + MELD_Na_baseline + log_k_admit +
## log_co2_admit + log_alt_admit + log_alb_admit + log_wbc_admit +
## log_plt_admit + sbp_admission, data = data, cause = cause)
##
## coef exp(coef) se(coef) z p-value
## age_admission 0.01799 1.018 0.00399 4.51 6.6e-06
## hispanic_race1 -0.33023 0.719 0.14945 -2.21 2.7e-02
## liver_transplant_listed1 -1.45930 0.232 0.15696 -9.30 0.0e+00
## final_type_of_aki2 0.93663 2.551 0.13454 6.96 3.4e-12
## final_type_of_aki3 0.96587 2.627 0.09651 10.01 0.0e+00
## final_type_of_aki4 0.34526 1.412 0.19851 1.74 8.2e-02
## final_type_of_aki5 0.91647 2.500 0.14403 6.36 2.0e-10
## siteindiana 0.27965 1.323 0.11981 2.33 2.0e-02
## sitejacksonville -0.59462 0.552 0.26287 -2.26 2.4e-02
## sitekentukey 0.43787 1.549 0.14527 3.01 2.6e-03
## siterochester -0.21297 0.808 0.13364 -1.59 1.1e-01
## siteyale -0.23022 0.794 0.13403 -1.72 8.6e-02
## etiology_cirrhosis2 0.28128 1.325 0.12512 2.25 2.5e-02
## etiology_cirrhosis4 0.68845 1.991 0.27021 2.55 1.1e-02
## etiology_cirrhosis6 0.28606 1.331 0.14858 1.93 5.4e-02
## etiology_cirrhosis7 0.38090 1.464 0.12856 2.96 3.0e-03
## albumin_given_admission1 0.28246 1.326 0.11238 2.51 1.2e-02
## lactulose1 0.20598 1.229 0.09247 2.23 2.6e-02
## rifaximin1 -0.18315 0.833 0.09747 -1.88 6.0e-02
## beta_blockers1 -0.22105 0.802 0.08262 -2.68 7.5e-03
## ckd1 -0.16282 0.850 0.09708 -1.68 9.4e-02
## encephalopathy_admission1 0.44265 1.557 0.09701 4.56 5.0e-06
## hcc_admission1 0.49471 1.640 0.11075 4.47 7.9e-06
## alcoholic_hepatitis_admission1 -0.18547 0.831 0.11835 -1.57 1.2e-01
## MELD_Na_baseline 0.03795 1.039 0.00622 6.10 1.1e-09
## log_k_admit -0.49053 0.612 0.17785 -2.76 5.8e-03
## log_co2_admit -0.45364 0.635 0.15171 -2.99 2.8e-03
## log_alt_admit 0.16529 1.180 0.04466 3.70 2.1e-04
## log_alb_admit -0.72783 0.483 0.15881 -4.58 4.6e-06
## log_wbc_admit 0.24670 1.280 0.07687 3.21 1.3e-03
## log_plt_admit -0.22072 0.802 0.07105 -3.11 1.9e-03
## sbp_admission -0.00569 0.994 0.00234 -2.43 1.5e-02
##
## exp(coef) exp(-coef) 2.5% 97.5%
## age_admission 1.018 0.982 1.010 1.026
## hispanic_race1 0.719 1.391 0.536 0.963
## liver_transplant_listed1 0.232 4.303 0.171 0.316
## final_type_of_aki2 2.551 0.392 1.960 3.321
## final_type_of_aki3 2.627 0.381 2.174 3.174
## final_type_of_aki4 1.412 0.708 0.957 2.084
## final_type_of_aki5 2.500 0.400 1.885 3.316
## siteindiana 1.323 0.756 1.046 1.673
## sitejacksonville 0.552 1.812 0.330 0.924
## sitekentukey 1.549 0.645 1.165 2.060
## siterochester 0.808 1.237 0.622 1.050
## siteyale 0.794 1.259 0.611 1.033
## etiology_cirrhosis2 1.325 0.755 1.037 1.693
## etiology_cirrhosis4 1.991 0.502 1.172 3.381
## etiology_cirrhosis6 1.331 0.751 0.995 1.781
## etiology_cirrhosis7 1.464 0.683 1.138 1.883
## albumin_given_admission1 1.326 0.754 1.064 1.653
## lactulose1 1.229 0.814 1.025 1.473
## rifaximin1 0.833 1.201 0.688 1.008
## beta_blockers1 0.802 1.247 0.682 0.943
## ckd1 0.850 1.177 0.703 1.028
## encephalopathy_admission1 1.557 0.642 1.287 1.883
## hcc_admission1 1.640 0.610 1.320 2.038
## alcoholic_hepatitis_admission1 0.831 1.204 0.659 1.048
## MELD_Na_baseline 1.039 0.963 1.026 1.051
## log_k_admit 0.612 1.633 0.432 0.868
## log_co2_admit 0.635 1.574 0.472 0.855
## log_alt_admit 1.180 0.848 1.081 1.288
## log_alb_admit 0.483 2.071 0.354 0.659
## log_wbc_admit 1.280 0.781 1.101 1.488
## log_plt_admit 0.802 1.247 0.698 0.922
## sbp_admission 0.994 1.006 0.990 0.999
##
## Num. cases = 1949
## Pseudo Log-likelihood = -4889
## Pseudo likelihood ratio test = 736 on 32 df,
##
## Convergence: TRUE
# Backward selection based on the BIC forcing ckd
sfgBIC_ckd <- pec::selectFGR(ff, cause = 1, data = ckd_data,rule = "BIC",scope.min=~ckd1, direction = "backward")
Backward selection based on the BIC forcing ckd for all
patients
sfgBIC_ckd$fit
##
## Right-censored response of a competing.risks model
##
## No.Observations: 1949
##
## Pattern:
##
## Cause event right.censored
## 1 721 0
## 2 118 0
## unknown 0 1110
##
##
## Fine-Gray model: analysis of cause 1
##
## Competing Risks Regression
##
## Call:
## riskRegression::FGR(formula = Hist(time_90days, status_90days) ~
## age_admission + hispanic_race1 + liver_transplant_listed1 +
## final_type_of_aki2 + final_type_of_aki3 + final_type_of_aki4 +
## final_type_of_aki5 + siteindiana + sitejacksonville +
## sitekentukey + siterochester + siteyale + etiology_cirrhosis2 +
## etiology_cirrhosis4 + etiology_cirrhosis6 + etiology_cirrhosis7 +
## albumin_given_admission1 + lactulose1 + rifaximin1 +
## beta_blockers1 + ckd1 + encephalopathy_admission1 + hcc_admission1 +
## alcoholic_hepatitis_admission1 + MELD_Na_baseline + log_k_admit +
## log_co2_admit + log_alt_admit + log_alb_admit + log_wbc_admit +
## log_plt_admit + sbp_admission, data = data, cause = cause)
##
## coef exp(coef) se(coef) z p-value
## age_admission 0.01799 1.018 0.00399 4.51 6.6e-06
## hispanic_race1 -0.33023 0.719 0.14945 -2.21 2.7e-02
## liver_transplant_listed1 -1.45930 0.232 0.15696 -9.30 0.0e+00
## final_type_of_aki2 0.93663 2.551 0.13454 6.96 3.4e-12
## final_type_of_aki3 0.96587 2.627 0.09651 10.01 0.0e+00
## final_type_of_aki4 0.34526 1.412 0.19851 1.74 8.2e-02
## final_type_of_aki5 0.91647 2.500 0.14403 6.36 2.0e-10
## siteindiana 0.27965 1.323 0.11981 2.33 2.0e-02
## sitejacksonville -0.59462 0.552 0.26287 -2.26 2.4e-02
## sitekentukey 0.43787 1.549 0.14527 3.01 2.6e-03
## siterochester -0.21297 0.808 0.13364 -1.59 1.1e-01
## siteyale -0.23022 0.794 0.13403 -1.72 8.6e-02
## etiology_cirrhosis2 0.28128 1.325 0.12512 2.25 2.5e-02
## etiology_cirrhosis4 0.68845 1.991 0.27021 2.55 1.1e-02
## etiology_cirrhosis6 0.28606 1.331 0.14858 1.93 5.4e-02
## etiology_cirrhosis7 0.38090 1.464 0.12856 2.96 3.0e-03
## albumin_given_admission1 0.28246 1.326 0.11238 2.51 1.2e-02
## lactulose1 0.20598 1.229 0.09247 2.23 2.6e-02
## rifaximin1 -0.18315 0.833 0.09747 -1.88 6.0e-02
## beta_blockers1 -0.22105 0.802 0.08262 -2.68 7.5e-03
## ckd1 -0.16282 0.850 0.09708 -1.68 9.4e-02
## encephalopathy_admission1 0.44265 1.557 0.09701 4.56 5.0e-06
## hcc_admission1 0.49471 1.640 0.11075 4.47 7.9e-06
## alcoholic_hepatitis_admission1 -0.18547 0.831 0.11835 -1.57 1.2e-01
## MELD_Na_baseline 0.03795 1.039 0.00622 6.10 1.1e-09
## log_k_admit -0.49053 0.612 0.17785 -2.76 5.8e-03
## log_co2_admit -0.45364 0.635 0.15171 -2.99 2.8e-03
## log_alt_admit 0.16529 1.180 0.04466 3.70 2.1e-04
## log_alb_admit -0.72783 0.483 0.15881 -4.58 4.6e-06
## log_wbc_admit 0.24670 1.280 0.07687 3.21 1.3e-03
## log_plt_admit -0.22072 0.802 0.07105 -3.11 1.9e-03
## sbp_admission -0.00569 0.994 0.00234 -2.43 1.5e-02
##
## exp(coef) exp(-coef) 2.5% 97.5%
## age_admission 1.018 0.982 1.010 1.026
## hispanic_race1 0.719 1.391 0.536 0.963
## liver_transplant_listed1 0.232 4.303 0.171 0.316
## final_type_of_aki2 2.551 0.392 1.960 3.321
## final_type_of_aki3 2.627 0.381 2.174 3.174
## final_type_of_aki4 1.412 0.708 0.957 2.084
## final_type_of_aki5 2.500 0.400 1.885 3.316
## siteindiana 1.323 0.756 1.046 1.673
## sitejacksonville 0.552 1.812 0.330 0.924
## sitekentukey 1.549 0.645 1.165 2.060
## siterochester 0.808 1.237 0.622 1.050
## siteyale 0.794 1.259 0.611 1.033
## etiology_cirrhosis2 1.325 0.755 1.037 1.693
## etiology_cirrhosis4 1.991 0.502 1.172 3.381
## etiology_cirrhosis6 1.331 0.751 0.995 1.781
## etiology_cirrhosis7 1.464 0.683 1.138 1.883
## albumin_given_admission1 1.326 0.754 1.064 1.653
## lactulose1 1.229 0.814 1.025 1.473
## rifaximin1 0.833 1.201 0.688 1.008
## beta_blockers1 0.802 1.247 0.682 0.943
## ckd1 0.850 1.177 0.703 1.028
## encephalopathy_admission1 1.557 0.642 1.287 1.883
## hcc_admission1 1.640 0.610 1.320 2.038
## alcoholic_hepatitis_admission1 0.831 1.204 0.659 1.048
## MELD_Na_baseline 1.039 0.963 1.026 1.051
## log_k_admit 0.612 1.633 0.432 0.868
## log_co2_admit 0.635 1.574 0.472 0.855
## log_alt_admit 1.180 0.848 1.081 1.288
## log_alb_admit 0.483 2.071 0.354 0.659
## log_wbc_admit 1.280 0.781 1.101 1.488
## log_plt_admit 0.802 1.247 0.698 0.922
## sbp_admission 0.994 1.006 0.990 0.999
##
## Num. cases = 1949
## Pseudo Log-likelihood = -4889
## Pseudo likelihood ratio test = 736 on 32 df,
##
## Convergence: TRUE
get the names of the covariates only removed MELD_Na_baseline
covariates_lab <- setdiff(names(ckd_data), c("time_90days","status_90days"))
covariates_lab <- covariates_lab[!covariates_lab %in% c("MELD_Na_baseline")]
new_covariates_lab <- paste(covariates_lab, collapse='+')
Backward selection based on the BIC for all patients with
log_na_admit creatinine_admission log_inr_admit log_tb_admit
sfgBIC_lab$fit
##
## Right-censored response of a competing.risks model
##
## No.Observations: 1949
##
## Pattern:
##
## Cause event right.censored
## 1 721 0
## 2 118 0
## unknown 0 1110
##
##
## Fine-Gray model: analysis of cause 1
##
## Competing Risks Regression
##
## Call:
## riskRegression::FGR(formula = Hist(time_90days, status_90days) ~
## age_admission + hispanic_race1 + liver_transplant_listed1 +
## final_type_of_aki2 + final_type_of_aki3 + final_type_of_aki4 +
## final_type_of_aki5 + sitejacksonville + sitekentukey +
## sitemgh + siterochester + siteusc + siteyale + etiology_cirrhosis2 +
## etiology_cirrhosis4 + etiology_cirrhosis5 + etiology_cirrhosis6 +
## etiology_cirrhosis7 + albumin_given_admission1 + lactulose1 +
## rifaximin1 + beta_blockers1 + encephalopathy_admission1 +
## hcc_admission1 + alcoholic_hepatitis_admission1 + log_co2_admit +
## log_alt_admit + log_tb_admit + log_alb_admit + log_inr_admit +
## log_wbc_admit + log_plt_admit + sbp_admission, data = data,
## cause = cause)
##
## coef exp(coef) se(coef) z p-value
## age_admission 0.01967 1.020 0.0040 4.92 8.8e-07
## hispanic_race1 -0.30113 0.740 0.1551 -1.94 5.2e-02
## liver_transplant_listed1 -1.48610 0.226 0.1621 -9.17 0.0e+00
## final_type_of_aki2 0.91430 2.495 0.1307 7.00 2.6e-12
## final_type_of_aki3 0.92650 2.526 0.0966 9.60 0.0e+00
## final_type_of_aki4 0.34498 1.412 0.1984 1.74 8.2e-02
## final_type_of_aki5 0.87545 2.400 0.1511 5.80 6.8e-09
## sitejacksonville -0.77322 0.462 0.2676 -2.89 3.9e-03
## sitekentukey 0.28848 1.334 0.1449 1.99 4.7e-02
## sitemgh -0.28861 0.749 0.1174 -2.46 1.4e-02
## siterochester -0.41614 0.660 0.1385 -3.00 2.7e-03
## siteusc -0.46441 0.629 0.2648 -1.75 7.9e-02
## siteyale -0.39158 0.676 0.1380 -2.84 4.5e-03
## etiology_cirrhosis2 0.28147 1.325 0.1316 2.14 3.2e-02
## etiology_cirrhosis4 0.69581 2.005 0.2807 2.48 1.3e-02
## etiology_cirrhosis5 0.21784 1.243 0.1360 1.60 1.1e-01
## etiology_cirrhosis6 0.37072 1.449 0.1481 2.50 1.2e-02
## etiology_cirrhosis7 0.42700 1.533 0.1341 3.18 1.5e-03
## albumin_given_admission1 0.28522 1.330 0.1118 2.55 1.1e-02
## lactulose1 0.15201 1.164 0.0932 1.63 1.0e-01
## rifaximin1 -0.17739 0.837 0.0969 -1.83 6.7e-02
## beta_blockers1 -0.21299 0.808 0.0829 -2.57 1.0e-02
## encephalopathy_admission1 0.41881 1.520 0.0970 4.32 1.6e-05
## hcc_admission1 0.50300 1.654 0.1120 4.49 7.1e-06
## alcoholic_hepatitis_admission1 -0.21267 0.808 0.1187 -1.79 7.3e-02
## log_co2_admit -0.54464 0.580 0.1493 -3.65 2.6e-04
## log_alt_admit 0.09039 1.095 0.0476 1.90 5.7e-02
## log_tb_admit 0.25826 1.295 0.0449 5.76 8.6e-09
## log_alb_admit -0.65015 0.522 0.1578 -4.12 3.8e-05
## log_inr_admit 0.54067 1.717 0.1068 5.06 4.2e-07
## log_wbc_admit 0.18522 1.203 0.0783 2.37 1.8e-02
## log_plt_admit -0.15258 0.858 0.0730 -2.09 3.7e-02
## sbp_admission -0.00737 0.993 0.0024 -3.07 2.2e-03
##
## exp(coef) exp(-coef) 2.5% 97.5%
## age_admission 1.020 0.981 1.012 1.028
## hispanic_race1 0.740 1.351 0.546 1.003
## liver_transplant_listed1 0.226 4.420 0.165 0.311
## final_type_of_aki2 2.495 0.401 1.931 3.223
## final_type_of_aki3 2.526 0.396 2.090 3.052
## final_type_of_aki4 1.412 0.708 0.957 2.083
## final_type_of_aki5 2.400 0.417 1.785 3.227
## sitejacksonville 0.462 2.167 0.273 0.780
## sitekentukey 1.334 0.749 1.004 1.773
## sitemgh 0.749 1.335 0.595 0.943
## siterochester 0.660 1.516 0.503 0.865
## siteusc 0.629 1.591 0.374 1.056
## siteyale 0.676 1.479 0.516 0.886
## etiology_cirrhosis2 1.325 0.755 1.024 1.715
## etiology_cirrhosis4 2.005 0.499 1.157 3.476
## etiology_cirrhosis5 1.243 0.804 0.953 1.623
## etiology_cirrhosis6 1.449 0.690 1.084 1.937
## etiology_cirrhosis7 1.533 0.652 1.178 1.993
## albumin_given_admission1 1.330 0.752 1.068 1.656
## lactulose1 1.164 0.859 0.970 1.397
## rifaximin1 0.837 1.194 0.693 1.013
## beta_blockers1 0.808 1.237 0.687 0.951
## encephalopathy_admission1 1.520 0.658 1.257 1.839
## hcc_admission1 1.654 0.605 1.328 2.060
## alcoholic_hepatitis_admission1 0.808 1.237 0.641 1.020
## log_co2_admit 0.580 1.724 0.433 0.777
## log_alt_admit 1.095 0.914 0.997 1.202
## log_tb_admit 1.295 0.772 1.186 1.414
## log_alb_admit 0.522 1.916 0.383 0.711
## log_inr_admit 1.717 0.582 1.393 2.117
## log_wbc_admit 1.203 0.831 1.032 1.403
## log_plt_admit 0.858 1.165 0.744 0.990
## sbp_admission 0.993 1.007 0.988 0.997
##
## Num. cases = 1949
## Pseudo Log-likelihood = -4873
## Pseudo likelihood ratio test = 768 on 33 df,
##
## Convergence: TRUE
# Backward selection based on the BIC forcing ckd with log_na_admit creatinine_admission log_inr_admit log_tb_admit
sfgBIC_ckd_lab <- pec::selectFGR(ff_lab, cause = 1, data = ckd_data,rule = "BIC",scope.min=~ckd1, direction = "backward")
Backward selection based on the BIC forcing ckd for all
patients
sfgBIC_ckd_lab$fit
##
## Right-censored response of a competing.risks model
##
## No.Observations: 1949
##
## Pattern:
##
## Cause event right.censored
## 1 721 0
## 2 118 0
## unknown 0 1110
##
##
## Fine-Gray model: analysis of cause 1
##
## Competing Risks Regression
##
## Call:
## riskRegression::FGR(formula = Hist(time_90days, status_90days) ~
## age_admission + hispanic_race1 + liver_transplant_listed1 +
## final_type_of_aki2 + final_type_of_aki3 + final_type_of_aki4 +
## final_type_of_aki5 + sitejacksonville + sitekentukey +
## sitemgh + siterochester + siteusc + siteyale + etiology_cirrhosis2 +
## etiology_cirrhosis4 + etiology_cirrhosis5 + etiology_cirrhosis6 +
## etiology_cirrhosis7 + albumin_given_admission1 + lactulose1 +
## rifaximin1 + beta_blockers1 + ckd1 + encephalopathy_admission1 +
## hcc_admission1 + alcoholic_hepatitis_admission1 + log_co2_admit +
## log_alt_admit + log_tb_admit + log_alb_admit + log_inr_admit +
## log_wbc_admit + log_plt_admit + sbp_admission, data = data,
## cause = cause)
##
## coef exp(coef) se(coef) z p-value
## age_admission 0.02011 1.020 0.00409 4.920 8.7e-07
## hispanic_race1 -0.30251 0.739 0.15514 -1.950 5.1e-02
## liver_transplant_listed1 -1.48210 0.227 0.16205 -9.146 0.0e+00
## final_type_of_aki2 0.92535 2.523 0.13277 6.969 3.2e-12
## final_type_of_aki3 0.93388 2.544 0.09740 9.588 0.0e+00
## final_type_of_aki4 0.34897 1.418 0.19869 1.756 7.9e-02
## final_type_of_aki5 0.88328 2.419 0.15168 5.823 5.8e-09
## sitejacksonville -0.77716 0.460 0.26725 -2.908 3.6e-03
## sitekentukey 0.29934 1.349 0.14586 2.052 4.0e-02
## sitemgh -0.28232 0.754 0.11766 -2.400 1.6e-02
## siterochester -0.41374 0.661 0.13888 -2.979 2.9e-03
## siteusc -0.47215 0.624 0.26503 -1.781 7.5e-02
## siteyale -0.39143 0.676 0.13784 -2.840 4.5e-03
## etiology_cirrhosis2 0.28120 1.325 0.13166 2.136 3.3e-02
## etiology_cirrhosis4 0.70462 2.023 0.28282 2.491 1.3e-02
## etiology_cirrhosis5 0.21319 1.238 0.13644 1.563 1.2e-01
## etiology_cirrhosis6 0.37153 1.450 0.14788 2.512 1.2e-02
## etiology_cirrhosis7 0.42498 1.530 0.13401 3.171 1.5e-03
## albumin_given_admission1 0.27627 1.318 0.11249 2.456 1.4e-02
## lactulose1 0.15560 1.168 0.09313 1.671 9.5e-02
## rifaximin1 -0.17066 0.843 0.09726 -1.755 7.9e-02
## beta_blockers1 -0.21100 0.810 0.08311 -2.539 1.1e-02
## ckd1 -0.07332 0.929 0.09998 -0.733 4.6e-01
## encephalopathy_admission1 0.41483 1.514 0.09714 4.270 2.0e-05
## hcc_admission1 0.49803 1.645 0.11252 4.426 9.6e-06
## alcoholic_hepatitis_admission1 -0.20959 0.811 0.11896 -1.762 7.8e-02
## log_co2_admit -0.54581 0.579 0.14888 -3.666 2.5e-04
## log_alt_admit 0.08832 1.092 0.04772 1.851 6.4e-02
## log_tb_admit 0.25442 1.290 0.04538 5.606 2.1e-08
## log_alb_admit -0.64454 0.525 0.15834 -4.071 4.7e-05
## log_inr_admit 0.53929 1.715 0.10705 5.038 4.7e-07
## log_wbc_admit 0.18135 1.199 0.07820 2.319 2.0e-02
## log_plt_admit -0.15323 0.858 0.07312 -2.096 3.6e-02
## sbp_admission -0.00722 0.993 0.00241 -2.992 2.8e-03
##
## exp(coef) exp(-coef) 2.5% 97.5%
## age_admission 1.020 0.980 1.012 1.029
## hispanic_race1 0.739 1.353 0.545 1.002
## liver_transplant_listed1 0.227 4.402 0.165 0.312
## final_type_of_aki2 2.523 0.396 1.945 3.273
## final_type_of_aki3 2.544 0.393 2.102 3.080
## final_type_of_aki4 1.418 0.705 0.960 2.093
## final_type_of_aki5 2.419 0.413 1.797 3.256
## sitejacksonville 0.460 2.175 0.272 0.776
## sitekentukey 1.349 0.741 1.014 1.795
## sitemgh 0.754 1.326 0.599 0.950
## siterochester 0.661 1.512 0.504 0.868
## siteusc 0.624 1.603 0.371 1.048
## siteyale 0.676 1.479 0.516 0.886
## etiology_cirrhosis2 1.325 0.755 1.023 1.715
## etiology_cirrhosis4 2.023 0.494 1.162 3.522
## etiology_cirrhosis5 1.238 0.808 0.947 1.617
## etiology_cirrhosis6 1.450 0.690 1.085 1.937
## etiology_cirrhosis7 1.530 0.654 1.176 1.989
## albumin_given_admission1 1.318 0.759 1.057 1.643
## lactulose1 1.168 0.856 0.973 1.402
## rifaximin1 0.843 1.186 0.697 1.020
## beta_blockers1 0.810 1.235 0.688 0.953
## ckd1 0.929 1.076 0.764 1.130
## encephalopathy_admission1 1.514 0.660 1.252 1.832
## hcc_admission1 1.645 0.608 1.320 2.052
## alcoholic_hepatitis_admission1 0.811 1.233 0.642 1.024
## log_co2_admit 0.579 1.726 0.433 0.776
## log_alt_admit 1.092 0.915 0.995 1.199
## log_tb_admit 1.290 0.775 1.180 1.410
## log_alb_admit 0.525 1.905 0.385 0.716
## log_inr_admit 1.715 0.583 1.390 2.115
## log_wbc_admit 1.199 0.834 1.028 1.397
## log_plt_admit 0.858 1.166 0.743 0.990
## sbp_admission 0.993 1.007 0.988 0.998
##
## Num. cases = 1949
## Pseudo Log-likelihood = -4872
## Pseudo likelihood ratio test = 769 on 34 df,
##
## Convergence: TRUE
Subgroup
Subgroup
Model1 for ckd patients
ckd_model_covariates <- ckd_data_with_ckd%>%select(age_admission,White1,sex2,hispanic_race1,liver_transplant_listed1,siteindiana:siteyale,log_na_admit,creatinine_admission,log_inr_admit,log_tb_admit)
Model1_with_ckd <- crr(ftime=ckd_data_with_ckd$time_90days,fstatus=ckd_data_with_ckd$status_90days,cov1=ckd_model_covariates,failcode=1, cencode=0)
summary(Model1_with_ckd )
## Competing Risks Regression
##
## Call:
## crr(ftime = ckd_data_with_ckd$time_90days, fstatus = ckd_data_with_ckd$status_90days,
## cov1 = ckd_model_covariates, failcode = 1, cencode = 0)
##
## coef exp(coef) se(coef) z p-value
## age_admission 0.02417 1.024 0.00922 2.62100 8.8e-03
## White1 0.43536 1.546 0.24985 1.74250 8.1e-02
## sex2 -0.09437 0.910 0.16138 -0.58475 5.6e-01
## hispanic_race1 -0.23057 0.794 0.42576 -0.54154 5.9e-01
## liver_transplant_listed1 -1.47587 0.229 0.33136 -4.45403 8.4e-06
## siteindiana 0.89163 2.439 0.34199 2.60720 9.1e-03
## sitejacksonville -1.34399 0.261 1.04671 -1.28402 2.0e-01
## sitekentukey 1.28338 3.609 0.33183 3.86761 1.1e-04
## siteMCW -0.00854 0.992 1.06117 -0.00804 9.9e-01
## sitemgh 0.36806 1.445 0.30996 1.18744 2.4e-01
## sitemichigan 0.47108 1.602 0.39427 1.19481 2.3e-01
## siteoschner 1.03342 2.811 0.43635 2.36831 1.8e-02
## siterochester 0.57132 1.771 0.32000 1.78539 7.4e-02
## siteusc 0.53496 1.707 0.86467 0.61869 5.4e-01
## siteyale 0.50458 1.656 0.35198 1.43356 1.5e-01
## log_na_admit -0.03191 0.969 0.10281 -0.31036 7.6e-01
## creatinine_admission 0.06350 1.066 0.05540 1.14615 2.5e-01
## log_inr_admit 0.27473 1.316 0.20110 1.36612 1.7e-01
## log_tb_admit 0.50010 1.649 0.08216 6.08728 1.1e-09
##
## exp(coef) exp(-coef) 2.5% 97.5%
## age_admission 1.024 0.976 1.0061 1.043
## White1 1.546 0.647 0.9471 2.522
## sex2 0.910 1.099 0.6632 1.248
## hispanic_race1 0.794 1.259 0.3447 1.829
## liver_transplant_listed1 0.229 4.375 0.1194 0.438
## siteindiana 2.439 0.410 1.2478 4.768
## sitejacksonville 0.261 3.834 0.0335 2.029
## sitekentukey 3.609 0.277 1.8833 6.915
## siteMCW 0.992 1.009 0.1239 7.935
## sitemgh 1.445 0.692 0.7871 2.653
## sitemichigan 1.602 0.624 0.7396 3.469
## siteoschner 2.811 0.356 1.1950 6.611
## siterochester 1.771 0.565 0.9457 3.315
## siteusc 1.707 0.586 0.3136 9.297
## siteyale 1.656 0.604 0.8309 3.302
## log_na_admit 0.969 1.032 0.7918 1.185
## creatinine_admission 1.066 0.938 0.9559 1.188
## log_inr_admit 1.316 0.760 0.8874 1.952
## log_tb_admit 1.649 0.606 1.4037 1.937
##
## Num. cases = 577
## Pseudo Log-likelihood = -1040
## Pseudo likelihood ratio test = 135 on 19 df,
Stepwise model for ckd patients
Backward selection based on the BIC ckd patients
sfgBIC_with_ckd$fit
##
## Right-censored response of a competing.risks model
##
## No.Observations: 577
##
## Pattern:
##
## Cause event right.censored
## 1 181 0
## 2 24 0
## unknown 0 372
##
##
## Fine-Gray model: analysis of cause 1
##
## Competing Risks Regression
##
## Call:
## riskRegression::FGR(formula = Hist(time_90days, status_90days) ~
## age_admission + White1 + liver_transplant_listed1 + final_type_of_aki2 +
## final_type_of_aki3 + final_type_of_aki4 + final_type_of_aki5 +
## siteindiana + sitejacksonville + sitekentukey + sitemgh +
## sitemichigan + siteoschner + siterochester + siteyale +
## etiology_cirrhosis2 + etiology_cirrhosis4 + etiology_cirrhosis7 +
## albumin_given_admission1 + lactulose1 + rifaximin1 +
## prophylactic_antibiotic1 + cad1 + encephalopathy_admission1 +
## hcc_admission1 + alcoholic_hepatitis_admission1 + log_k_admit +
## log_co2_admit + log_bun_admit + log_tb_admit + log_alb_admit +
## log_wbc_admit, data = data, cause = cause)
##
## coef exp(coef) se(coef) z p-value
## age_admission 0.0145 1.015 0.00744 1.94 5.2e-02
## White1 0.3430 1.409 0.24852 1.38 1.7e-01
## liver_transplant_listed1 -1.5088 0.221 0.38705 -3.90 9.7e-05
## final_type_of_aki2 0.4774 1.612 0.26580 1.80 7.2e-02
## final_type_of_aki3 0.2984 1.348 0.20418 1.46 1.4e-01
## final_type_of_aki4 0.6110 1.842 0.31017 1.97 4.9e-02
## final_type_of_aki5 0.6566 1.928 0.26850 2.45 1.4e-02
## siteindiana 1.1306 3.097 0.34913 3.24 1.2e-03
## sitejacksonville -1.3321 0.264 1.08319 -1.23 2.2e-01
## sitekentukey 1.2463 3.477 0.33838 3.68 2.3e-04
## sitemgh 0.9690 2.635 0.30568 3.17 1.5e-03
## sitemichigan 0.8990 2.457 0.39791 2.26 2.4e-02
## siteoschner 1.1661 3.209 0.36727 3.17 1.5e-03
## siterochester 0.8642 2.373 0.31774 2.72 6.5e-03
## siteyale 0.6290 1.876 0.36036 1.75 8.1e-02
## etiology_cirrhosis2 0.4067 1.502 0.25188 1.61 1.1e-01
## etiology_cirrhosis4 0.8068 2.241 0.32274 2.50 1.2e-02
## etiology_cirrhosis7 0.7193 2.053 0.26241 2.74 6.1e-03
## albumin_given_admission1 0.5885 1.801 0.20138 2.92 3.5e-03
## lactulose1 0.5596 1.750 0.21265 2.63 8.5e-03
## rifaximin1 -0.6156 0.540 0.19996 -3.08 2.1e-03
## prophylactic_antibiotic1 -0.8934 0.409 0.38350 -2.33 2.0e-02
## cad1 0.3908 1.478 0.18946 2.06 3.9e-02
## encephalopathy_admission1 0.5776 1.782 0.18474 3.13 1.8e-03
## hcc_admission1 0.7076 2.029 0.19866 3.56 3.7e-04
## alcoholic_hepatitis_admission1 -0.5980 0.550 0.34406 -1.74 8.2e-02
## log_k_admit -0.8347 0.434 0.46199 -1.81 7.1e-02
## log_co2_admit -0.5512 0.576 0.33343 -1.65 9.8e-02
## log_bun_admit 0.7213 2.057 0.16955 4.25 2.1e-05
## log_tb_admit 0.4929 1.637 0.08556 5.76 8.4e-09
## log_alb_admit -1.6176 0.198 0.29160 -5.55 2.9e-08
## log_wbc_admit 0.3576 1.430 0.14859 2.41 1.6e-02
##
## exp(coef) exp(-coef) 2.5% 97.5%
## age_admission 1.015 0.986 0.9999 1.029
## White1 1.409 0.710 0.8658 2.294
## liver_transplant_listed1 0.221 4.521 0.1036 0.472
## final_type_of_aki2 1.612 0.620 0.9573 2.714
## final_type_of_aki3 1.348 0.742 0.9032 2.011
## final_type_of_aki4 1.842 0.543 1.0031 3.384
## final_type_of_aki5 1.928 0.519 1.1392 3.264
## siteindiana 3.097 0.323 1.5625 6.140
## sitejacksonville 0.264 3.789 0.0316 2.205
## sitekentukey 3.477 0.288 1.7915 6.750
## sitemgh 2.635 0.379 1.4475 4.798
## sitemichigan 2.457 0.407 1.1265 5.360
## siteoschner 3.209 0.312 1.5624 6.592
## siterochester 2.373 0.421 1.2731 4.424
## siteyale 1.876 0.533 0.9256 3.801
## etiology_cirrhosis2 1.502 0.666 0.9167 2.461
## etiology_cirrhosis4 2.241 0.446 1.1903 4.218
## etiology_cirrhosis7 2.053 0.487 1.2275 3.434
## albumin_given_admission1 1.801 0.555 1.2139 2.673
## lactulose1 1.750 0.571 1.1535 2.655
## rifaximin1 0.540 1.851 0.3651 0.800
## prophylactic_antibiotic1 0.409 2.443 0.1930 0.868
## cad1 1.478 0.677 1.0196 2.143
## encephalopathy_admission1 1.782 0.561 1.2405 2.559
## hcc_admission1 2.029 0.493 1.3747 2.995
## alcoholic_hepatitis_admission1 0.550 1.818 0.2802 1.079
## log_k_admit 0.434 2.304 0.1755 1.073
## log_co2_admit 0.576 1.735 0.2998 1.108
## log_bun_admit 2.057 0.486 1.4755 2.868
## log_tb_admit 1.637 0.611 1.3843 1.936
## log_alb_admit 0.198 5.041 0.1120 0.351
## log_wbc_admit 1.430 0.699 1.0686 1.913
##
## Num. cases = 577
## Pseudo Log-likelihood = -977
## Pseudo likelihood ratio test = 260 on 32 df,
##
## Convergence: TRUE
Model1 for no ckd patients
no_ckd_model_covariates <- ckd_data_without_ckd%>%select(age_admission,White1,sex2,hispanic_race1,liver_transplant_listed1,siteindiana:siteyale,log_na_admit,creatinine_admission,log_inr_admit,log_tb_admit)
Model1_without_ckd <- crr(ftime=ckd_data_without_ckd$time_90days,fstatus=ckd_data_without_ckd$status_90days,cov1=no_ckd_model_covariates,failcode=1, cencode=0)
summary(Model1_without_ckd )
## Competing Risks Regression
##
## Call:
## crr(ftime = ckd_data_without_ckd$time_90days, fstatus = ckd_data_without_ckd$status_90days,
## cov1 = no_ckd_model_covariates, failcode = 1, cencode = 0)
##
## coef exp(coef) se(coef) z p-value
## age_admission 0.0241 1.024 0.00431 5.595 2.2e-08
## White1 -0.3990 0.671 0.11497 -3.471 5.2e-04
## sex2 0.1223 1.130 0.09058 1.350 1.8e-01
## hispanic_race1 -0.2531 0.776 0.15973 -1.585 1.1e-01
## liver_transplant_listed1 -1.3204 0.267 0.17638 -7.486 7.1e-14
## siteindiana 0.1995 1.221 0.17543 1.137 2.6e-01
## sitejacksonville -0.3150 0.730 0.28494 -1.106 2.7e-01
## sitekentukey 0.8789 2.408 0.20861 4.213 2.5e-05
## siteMCW 0.0902 1.094 0.19156 0.471 6.4e-01
## sitemgh -0.0868 0.917 0.17596 -0.493 6.2e-01
## sitemichigan 0.0256 1.026 0.22521 0.114 9.1e-01
## siteoschner 0.2436 1.276 0.20386 1.195 2.3e-01
## siterochester -0.3792 0.684 0.20380 -1.861 6.3e-02
## siteusc -0.3962 0.673 0.30869 -1.283 2.0e-01
## siteyale -0.1354 0.873 0.19026 -0.712 4.8e-01
## log_na_admit 0.2984 1.348 0.94298 0.316 7.5e-01
## creatinine_admission 0.0549 1.056 0.03258 1.684 9.2e-02
## log_inr_admit 0.8049 2.236 0.14656 5.492 4.0e-08
## log_tb_admit 0.3739 1.453 0.04473 8.358 0.0e+00
##
## exp(coef) exp(-coef) 2.5% 97.5%
## age_admission 1.024 0.976 1.016 1.033
## White1 0.671 1.490 0.536 0.841
## sex2 1.130 0.885 0.946 1.350
## hispanic_race1 0.776 1.288 0.568 1.062
## liver_transplant_listed1 0.267 3.745 0.189 0.377
## siteindiana 1.221 0.819 0.866 1.722
## sitejacksonville 0.730 1.370 0.417 1.276
## sitekentukey 2.408 0.415 1.600 3.625
## siteMCW 1.094 0.914 0.752 1.593
## sitemgh 0.917 1.091 0.649 1.294
## sitemichigan 1.026 0.975 0.660 1.595
## siteoschner 1.276 0.784 0.856 1.902
## siterochester 0.684 1.461 0.459 1.020
## siteusc 0.673 1.486 0.367 1.232
## siteyale 0.873 1.145 0.601 1.268
## log_na_admit 1.348 0.742 0.212 8.556
## creatinine_admission 1.056 0.947 0.991 1.126
## log_inr_admit 2.236 0.447 1.678 2.981
## log_tb_admit 1.453 0.688 1.331 1.586
##
## Num. cases = 1372
## Pseudo Log-likelihood = -3563
## Pseudo likelihood ratio test = 349 on 19 df,
Stepwise model for ckd patients
Backward selection based on the BIC ckd
sfgBIC_without_ckd$fit
##
## Right-censored response of a competing.risks model
##
## No.Observations: 1372
##
## Pattern:
##
## Cause event right.censored
## 1 540 0
## 2 94 0
## unknown 0 738
##
##
## Fine-Gray model: analysis of cause 1
##
## Competing Risks Regression
##
## Call:
## riskRegression::FGR(formula = Hist(time_90days, status_90days) ~
## age_admission + White1 + hispanic_race1 + liver_transplant_listed1 +
## final_type_of_aki2 + final_type_of_aki3 + final_type_of_aki5 +
## sitejacksonville + sitekentukey + sitemgh + siteoschner +
## siterochester + siteusc + siteyale + etiology_cirrhosis2 +
## etiology_cirrhosis3 + etiology_cirrhosis5 + etiology_cirrhosis6 +
## etiology_cirrhosis7 + beta_blockers1 + diabetes1 + encephalopathy_admission1 +
## hcc_admission1 + lvp_admission1 + log_co2_admit + log_alt_admit +
## log_tb_admit + log_alb_admit + log_inr_admit + log_wbc_admit +
## log_plt_admit + sbp_admission, data = data, cause = cause)
##
## coef exp(coef) se(coef) z p-value
## age_admission 0.02159 1.022 0.00456 4.74 2.2e-06
## White1 -0.28262 0.754 0.11137 -2.54 1.1e-02
## hispanic_race1 -0.30074 0.740 0.17526 -1.72 8.6e-02
## liver_transplant_listed1 -1.37415 0.253 0.18205 -7.55 4.4e-14
## final_type_of_aki2 0.99953 2.717 0.15657 6.38 1.7e-10
## final_type_of_aki3 1.11756 3.057 0.10915 10.24 0.0e+00
## final_type_of_aki5 0.96915 2.636 0.16922 5.73 1.0e-08
## sitejacksonville -0.53736 0.584 0.27196 -1.98 4.8e-02
## sitekentukey 0.32345 1.382 0.17375 1.86 6.3e-02
## sitemgh -0.48640 0.615 0.14517 -3.35 8.1e-04
## siteoschner -0.34839 0.706 0.18123 -1.92 5.5e-02
## siterochester -0.63068 0.532 0.16680 -3.78 1.6e-04
## siteusc -0.77380 0.461 0.30812 -2.51 1.2e-02
## siteyale -0.46793 0.626 0.15416 -3.04 2.4e-03
## etiology_cirrhosis2 0.41202 1.510 0.15776 2.61 9.0e-03
## etiology_cirrhosis3 0.29399 1.342 0.15657 1.88 6.0e-02
## etiology_cirrhosis5 0.35499 1.426 0.15264 2.33 2.0e-02
## etiology_cirrhosis6 0.52188 1.685 0.16562 3.15 1.6e-03
## etiology_cirrhosis7 0.54669 1.728 0.15990 3.42 6.3e-04
## beta_blockers1 -0.21485 0.807 0.09834 -2.18 2.9e-02
## diabetes1 -0.15616 0.855 0.10969 -1.42 1.5e-01
## encephalopathy_admission1 0.38420 1.468 0.10777 3.57 3.6e-04
## hcc_admission1 0.42132 1.524 0.13276 3.17 1.5e-03
## lvp_admission1 0.17695 1.194 0.09874 1.79 7.3e-02
## log_co2_admit -0.49150 0.612 0.16336 -3.01 2.6e-03
## log_alt_admit 0.07127 1.074 0.05363 1.33 1.8e-01
## log_tb_admit 0.23038 1.259 0.05084 4.53 5.8e-06
## log_alb_admit -0.60122 0.548 0.18163 -3.31 9.3e-04
## log_inr_admit 0.60036 1.823 0.12719 4.72 2.4e-06
## log_wbc_admit 0.16469 1.179 0.08865 1.86 6.3e-02
## log_plt_admit -0.16763 0.846 0.08004 -2.09 3.6e-02
## sbp_admission -0.00869 0.991 0.00301 -2.89 3.9e-03
##
## exp(coef) exp(-coef) 2.5% 97.5%
## age_admission 1.022 0.979 1.013 1.031
## White1 0.754 1.327 0.606 0.938
## hispanic_race1 0.740 1.351 0.525 1.044
## liver_transplant_listed1 0.253 3.952 0.177 0.362
## final_type_of_aki2 2.717 0.368 1.999 3.693
## final_type_of_aki3 3.057 0.327 2.469 3.787
## final_type_of_aki5 2.636 0.379 1.892 3.672
## sitejacksonville 0.584 1.711 0.343 0.996
## sitekentukey 1.382 0.724 0.983 1.943
## sitemgh 0.615 1.626 0.463 0.817
## siteoschner 0.706 1.417 0.495 1.007
## siterochester 0.532 1.879 0.384 0.738
## siteusc 0.461 2.168 0.252 0.844
## siteyale 0.626 1.597 0.463 0.847
## etiology_cirrhosis2 1.510 0.662 1.108 2.057
## etiology_cirrhosis3 1.342 0.745 0.987 1.824
## etiology_cirrhosis5 1.426 0.701 1.057 1.924
## etiology_cirrhosis6 1.685 0.593 1.218 2.331
## etiology_cirrhosis7 1.728 0.579 1.263 2.363
## beta_blockers1 0.807 1.240 0.665 0.978
## diabetes1 0.855 1.169 0.690 1.061
## encephalopathy_admission1 1.468 0.681 1.189 1.814
## hcc_admission1 1.524 0.656 1.175 1.977
## lvp_admission1 1.194 0.838 0.984 1.448
## log_co2_admit 0.612 1.635 0.444 0.843
## log_alt_admit 1.074 0.931 0.967 1.193
## log_tb_admit 1.259 0.794 1.140 1.391
## log_alb_admit 0.548 1.824 0.384 0.783
## log_inr_admit 1.823 0.549 1.421 2.339
## log_wbc_admit 1.179 0.848 0.991 1.403
## log_plt_admit 0.846 1.183 0.723 0.989
## sbp_admission 0.991 1.009 0.986 0.997
##
## Num. cases = 1372
## Pseudo Log-likelihood = -3447
## Pseudo likelihood ratio test = 581 on 32 df,
##
## Convergence: TRUE
Group for CKD/DM/HTN/NASH vs CKD/DM/HTN/Alcohol & no
CKD/DM/HTN/NASH vs no CKD/DM/HTN/Alcohol
ckd_subgroup_with_ckd <- ckd_all %>% mutate(group_etiology=case_when(
ckd==1 & diabetes==1 & htn==1 & etiology_cirrhosis==3 ~"CKD/DM/HTN/NASH",
ckd==1 & diabetes==1 & htn==1 & etiology_cirrhosis==1 ~"CKD/DM/HTN/Alcohol")) %>% filter(!is.na(group_etiology))
ckd_subgroup_without_ckd <- ckd_all %>% mutate(group_etiology=case_when(
ckd==0 & diabetes==1 & htn==1 & etiology_cirrhosis==3 ~"noCKD/DM/HTN/NASH",
ckd==0 & diabetes==1 & htn==1 & etiology_cirrhosis==1 ~"noCKD/DM/HTN/Alcohol")) %>% filter(!is.na(group_etiology))
######
all_var_subgroup <- names(ckd_subgroup_with_ckd)
cat_var_subgroup <- c("sex","White",
"hispanic_race","liver_transplant_listed",
"site",
"final_type_of_aki",
"encephalopathy_admission",
"etiology_cirrhosis" ,
"albumin_given_admission",
"loop_diuretic" ,
"aldosterone_antagonist" ,
"lactulose" ,
"rifaximin" ,
"prophylactic_antibiotic",
"nsaids",
"beta_blockers",
"diabetes" ,
"cad" ,
"ckd",
"htn",
"ascites_admission",
"encephalopathy_admission",
"gi_bleed_admission" ,
"peritonitis_admission" ,
"hcc_admission" ,
"tips_admission" ,
"lvp_admission",
"alcoholic_hepatitis_admission",
"group_etiology")
num_var_subgroup <- setdiff(all_var,cat_var)
ckd_subgroup_with_ckd <- ckd_subgroup_with_ckd %>% mutate_at(cat_var_subgroup,factor)
ckd_subgroup_without_ckd <- ckd_subgroup_without_ckd %>% mutate_at(cat_var_subgroup,factor)
summary CKD/DM/HTN/NASH vs CKD/DM/HTN/Alcohol
table(ckd_subgroup_with_ckd$group_etiology)
##
## CKD/DM/HTN/Alcohol CKD/DM/HTN/NASH
## 47 115
summary no CKD/DM/HTN/NASH vs no CKD/DM/HTN/Alcohol
table(ckd_subgroup_without_ckd$group_etiology)
##
## noCKD/DM/HTN/Alcohol noCKD/DM/HTN/NASH
## 81 117
Remove covariates with only one level in both ckd and no ckd
group
one_level_subgroup_with_ckd <- names(ckd_subgroup_with_ckd[, sapply(ckd_subgroup_with_ckd, nlevels) == 1] )
ckd_subgroup_with_ckd <- ckd_subgroup_with_ckd %>% select(-all_of(one_level_subgroup_with_ckd))
one_level_subgroup_with_ckd
## [1] "diabetes" "htn" "ckd"
one_level_subgroup_without_ckd <- names(ckd_subgroup_without_ckd[, sapply(ckd_subgroup_without_ckd, nlevels) == 1] )
ckd_subgroup_without_ckd <- ckd_subgroup_without_ckd %>% select(-all_of(one_level_subgroup_without_ckd))
one_level_subgroup_without_ckd
## [1] "diabetes" "htn" "ckd"
remove intercept
ckd_subgroup_with_ckd_test <- as.data.frame(model.matrix(as.formula(paste0("~ ", paste(names(ckd_subgroup_with_ckd), collapse='+'))), data =ckd_subgroup_with_ckd)[,-1])
ckd_subgroup_without_ckd_test <- as.data.frame(model.matrix(as.formula(paste0("~ ", paste(names(ckd_subgroup_without_ckd), collapse='+'))), data =ckd_subgroup_without_ckd)[,-1])
Model1 for CKD/DM/HTN/NASH vs CKD/DM/HTN/Alcoho
ckd_subgroup_with_ckd_test_covariates <- ckd_subgroup_with_ckd_test%>%select(age_admission,White1,sex2,hispanic_race1,liver_transplant_listed1,siteindiana:siteyale,log_na_admit,creatinine_admission,log_inr_admit)
Model1_ckd_subgroup_with_ckd <- crr(ftime=ckd_subgroup_with_ckd_test$time_90days,fstatus=ckd_subgroup_with_ckd_test$status_90days,cov1=ckd_subgroup_with_ckd_test_covariates,failcode=1, cencode=0)
summary(Model1_ckd_subgroup_with_ckd )
## Competing Risks Regression
##
## Call:
## crr(ftime = ckd_subgroup_with_ckd_test$time_90days, fstatus = ckd_subgroup_with_ckd_test$status_90days,
## cov1 = ckd_subgroup_with_ckd_test_covariates, failcode = 1,
## cencode = 0)
##
## crr converged: FALSE
Model1 for no CKD/DM/HTN/NASH vs no CKD/DM/HTN/Alcoho
ckd_subgroup_without_ckd_test_covariates <- ckd_subgroup_without_ckd_test%>%select(age_admission,White1,sex2,hispanic_race1,liver_transplant_listed1,siteindiana:siteyale,log_na_admit,creatinine_admission,log_inr_admit)
Model1_ckd_subgroup_without_ckd <- crr(ftime=ckd_subgroup_without_ckd_test$time_90days,fstatus=ckd_subgroup_without_ckd_test$status_90days,cov1=ckd_subgroup_without_ckd_test_covariates,failcode=1, cencode=0)
summary(Model1_ckd_subgroup_without_ckd )
## Competing Risks Regression
##
## Call:
## crr(ftime = ckd_subgroup_without_ckd_test$time_90days, fstatus = ckd_subgroup_without_ckd_test$status_90days,
## cov1 = ckd_subgroup_without_ckd_test_covariates, failcode = 1,
## cencode = 0)
##
## coef exp(coef) se(coef) z p-value
## age_admission -0.00844 9.92e-01 0.0134 -0.6318 0.530
## White1 -0.25279 7.77e-01 0.4094 -0.6175 0.540
## sex2 0.07369 1.08e+00 0.2651 0.2779 0.780
## hispanic_race1 -0.09431 9.10e-01 0.6350 -0.1485 0.880
## liver_transplant_listed1 -1.10479 3.31e-01 0.4932 -2.2400 0.025
## siteindiana 0.96858 2.63e+00 0.4973 1.9478 0.051
## sitejacksonville 0.68788 1.99e+00 0.6764 1.0170 0.310
## sitekentukey 1.01102 2.75e+00 0.6218 1.6259 0.100
## siteMCW -0.29397 7.45e-01 0.6955 -0.4227 0.670
## sitemgh 0.30912 1.36e+00 0.4892 0.6319 0.530
## sitemichigan 0.11002 1.12e+00 0.6257 0.1758 0.860
## siteoschner 0.88534 2.42e+00 0.6420 1.3790 0.170
## siterochester -1.43842 2.37e-01 0.8600 -1.6726 0.094
## siteusc -10.56708 2.57e-05 0.9885 -10.6898 0.000
## siteyale 0.02130 1.02e+00 0.6762 0.0315 0.970
## log_na_admit 2.15506 8.63e+00 3.3021 0.6526 0.510
## creatinine_admission 0.01571 1.02e+00 0.0736 0.2135 0.830
## log_inr_admit 1.03539 2.82e+00 0.4738 2.1854 0.029
##
## exp(coef) exp(-coef) 2.5% 97.5%
## age_admission 9.92e-01 1.01e+00 9.66e-01 1.02e+00
## White1 7.77e-01 1.29e+00 3.48e-01 1.73e+00
## sex2 1.08e+00 9.29e-01 6.40e-01 1.81e+00
## hispanic_race1 9.10e-01 1.10e+00 2.62e-01 3.16e+00
## liver_transplant_listed1 3.31e-01 3.02e+00 1.26e-01 8.71e-01
## siteindiana 2.63e+00 3.80e-01 9.94e-01 6.98e+00
## sitejacksonville 1.99e+00 5.03e-01 5.28e-01 7.49e+00
## sitekentukey 2.75e+00 3.64e-01 8.12e-01 9.30e+00
## siteMCW 7.45e-01 1.34e+00 1.91e-01 2.91e+00
## sitemgh 1.36e+00 7.34e-01 5.22e-01 3.55e+00
## sitemichigan 1.12e+00 8.96e-01 3.28e-01 3.80e+00
## siteoschner 2.42e+00 4.13e-01 6.89e-01 8.53e+00
## siterochester 2.37e-01 4.21e+00 4.40e-02 1.28e+00
## siteusc 2.57e-05 3.88e+04 3.71e-06 1.79e-04
## siteyale 1.02e+00 9.79e-01 2.71e-01 3.84e+00
## log_na_admit 8.63e+00 1.16e-01 1.33e-02 5.58e+03
## creatinine_admission 1.02e+00 9.84e-01 8.79e-01 1.17e+00
## log_inr_admit 2.82e+00 3.55e-01 1.11e+00 7.13e+00
##
## Num. cases = 190
## Pseudo Log-likelihood = -291
## Pseudo likelihood ratio test = 36.5 on 18 df,