cox day0_meld_3
## Call:
## coxph(formula = Surv(time_to_death_90days_init, death_status_90days_init) ~
## day0_meld_3, data = final_master, x = TRUE)
##
## n= 243, number of events= 102
##
## coef exp(coef) se(coef) z Pr(>|z|)
## day0_meld_3 0.05484 1.05637 0.01271 4.316 1.59e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## day0_meld_3 1.056 0.9466 1.03 1.083
##
## Concordance= 0.642 (se = 0.027 )
## Likelihood ratio test= 18.89 on 1 df, p=1e-05
## Wald test = 18.63 on 1 df, p=2e-05
## Score (logrank) test = 18.65 on 1 df, p=2e-05
cox day0_meld
## Call:
## coxph(formula = Surv(time_to_death_90days_init, death_status_90days_init) ~
## day0_meld, data = final_master, x = TRUE)
##
## n= 243, number of events= 102
##
## coef exp(coef) se(coef) z Pr(>|z|)
## day0_meld 0.04998 1.05125 0.01229 4.067 4.76e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## day0_meld 1.051 0.9513 1.026 1.077
##
## Concordance= 0.633 (se = 0.027 )
## Likelihood ratio test= 16.11 on 1 df, p=6e-05
## Wald test = 16.54 on 1 df, p=5e-05
## Score (logrank) test = 16.63 on 1 df, p=5e-05
cox day0_meld_na
## Call:
## coxph(formula = Surv(time_to_death_90days_init, death_status_90days_init) ~
## day0_meld_na, data = final_master, x = TRUE)
##
## n= 243, number of events= 102
##
## coef exp(coef) se(coef) z Pr(>|z|)
## day0_meld_na 0.06247 1.06446 0.01418 4.404 1.06e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## day0_meld_na 1.064 0.9394 1.035 1.094
##
## Concordance= 0.647 (se = 0.027 )
## Likelihood ratio test= 19.15 on 1 df, p=1e-05
## Wald test = 19.4 on 1 df, p=1e-05
## Score (logrank) test = 19.37 on 1 df, p=1e-05
cox clif_c_score_new
## Call:
## coxph(formula = Surv(time_to_death_90days_init, death_status_90days_init) ~
## clif_c_score_new, data = final_master, x = TRUE)
##
## n= 243, number of events= 102
##
## coef exp(coef) se(coef) z Pr(>|z|)
## clif_c_score_new 0.05691 1.05856 0.01229 4.631 3.64e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## clif_c_score_new 1.059 0.9447 1.033 1.084
##
## Concordance= 0.632 (se = 0.028 )
## Likelihood ratio test= 21.12 on 1 df, p=4e-06
## Wald test = 21.45 on 1 df, p=4e-06
## Score (logrank) test = 21.71 on 1 df, p=3e-06
Table 1 function
Table 1
ROC_day0_meld_3
## Time-dependent-Roc curve estimated using IPCW (n=243, without competing risks).
## Cases Survivors Censored AUC (%) se
## t=7 11 227 5 62.14 8.61
## t=15 40 193 10 67.09 4.36
## t=30 72 157 14 69.83 3.52
## t=60 93 135 15 67.29 3.51
## t=85 101 121 21 65.40 3.64
##
## Method used for estimating IPCW:marginal
##
## Total computation time : 0.06 secs.
ROC_day0_meld
## Time-dependent-Roc curve estimated using IPCW (n=243, without competing risks).
## Cases Survivors Censored AUC (%) se
## t=7 11 227 5 57.52 9.05
## t=15 40 193 10 65.72 4.37
## t=30 72 157 14 69.60 3.51
## t=60 93 135 15 66.27 3.55
## t=85 101 121 21 64.57 3.67
##
## Method used for estimating IPCW:marginal
##
## Total computation time : 0.06 secs.
ROC_day0_meld_na
## Time-dependent-Roc curve estimated using IPCW (n=243, without competing risks).
## Cases Survivors Censored AUC (%) se
## t=7 11 227 5 61.19 8.60
## t=15 40 193 10 68.02 4.31
## t=30 72 157 14 70.58 3.46
## t=60 93 135 15 67.61 3.49
## t=85 101 121 21 65.97 3.62
##
## Method used for estimating IPCW:marginal
##
## Total computation time : 0.08 secs.
ROC_clif_c_score_new
## Time-dependent-Roc curve estimated using IPCW (n=243, without competing risks).
## Cases Survivors Censored AUC (%) se
## t=7 11 227 5 71.39 6.94
## t=15 40 193 10 62.94 5.00
## t=30 72 157 14 67.49 3.90
## t=60 93 135 15 65.78 3.67
## t=85 101 121 21 65.39 3.68
##
## Method used for estimating IPCW:marginal
##
## Total computation time : 0.06 secs.
ROC_day0_meld,ROC_day0_meld_3
## $p_values_AUC
## t=7 t=15 t=30 t=60 t=85
## Non-adjusted 0.2701947 0.4772911 0.8812400 0.4797078 0.5684626
## Adjusted 0.6278438 0.8816171 0.9998212 0.8836043 0.9425718
##
## $Cor
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.0000000 0.6360371 0.4285497 0.3244855 0.3013237
## [2,] 0.6360371 1.0000000 0.6980366 0.5138373 0.4846124
## [3,] 0.4285497 0.6980366 1.0000000 0.7483698 0.7013024
## [4,] 0.3244855 0.5138373 0.7483698 1.0000000 0.9427008
## [5,] 0.3013237 0.4846124 0.7013024 0.9427008 1.0000000
ROC_day0_meld_na,ROC_day0_meld_3
## $p_values_AUC
## t=7 t=15 t=30 t=60 t=85
## Non-adjusted 0.6908888 0.4547431 0.4404209 0.7280115 0.5373013
## Adjusted 0.9877454 0.8750328 0.8623280 0.9930671 0.9336449
##
## $Cor
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.0000000 0.5419346 0.3414262 0.2509966 0.2307707
## [2,] 0.5419346 1.0000000 0.6753606 0.5032827 0.4543196
## [3,] 0.3414262 0.6753606 1.0000000 0.7610873 0.6844070
## [4,] 0.2509966 0.5032827 0.7610873 1.0000000 0.9135018
## [5,] 0.2307707 0.4543196 0.6844070 0.9135018 1.0000000
ROC_clif_c_score_new,ROC_day0_meld_3
## $p_values_AUC
## t=7 t=15 t=30 t=60 t=85
## Non-adjusted 0.2915186 0.4885859 0.5920953 0.6984960 0.9976813
## Adjusted 0.6626041 0.8890526 0.9525597 0.9861802 1.0000000
##
## $Cor
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.0000000 0.3725705 0.2407046 0.2133469 0.1979553
## [2,] 0.3725705 1.0000000 0.7211449 0.6256998 0.5784714
## [3,] 0.2407046 0.7211449 1.0000000 0.8686202 0.8065766
## [4,] 0.2133469 0.6256998 0.8686202 1.0000000 0.9310685
## [5,] 0.1979553 0.5784714 0.8065766 0.9310685 1.0000000