****if deces: 0=death; 1 alive)*************

#Prepare: packages, data

library(foreign, lib.loc = "C:/Program Files/R/R-3.6.1/library")
library(survival)

#read data and group for variable

Read Data

data=read.csv("C:/Users/BINH THANG-TRAN/Dropbox/PhD/Data/BS TRi Ca/Data/data.csv")
head(data)
##   ID dossier chirurgien age sexe tabagisme Apparition ASIA_preop Frankel_preop
## 1  1  928667         ZW  55    1         1          2          D             D
## 2  2 5473345         ZW  71    1         0          2          E             E
## 3  3 5466545         GB  68    2         1          2          D             D
## 4  4  553346         DS  71    1         1          1          D             D
## 5  5  734329         DS  63    1         1          1          E             E
## 6  6  390270         GB  65    2         1          1          D             D
##   ambulation_preop fctSphincter approcheChx    dateChx ambulationPO
## 1                2            2           1  10/6/2018            2
## 2                1            2           1   1/7/2018            1
## 3                2            1           1   1/3/2018            2
## 4                2            2           1 16/12/2017            2
## 5                1            2           1 16/12/2017            1
## 6                2            2           2 28/07/2016            1
##   amelioration_ambulance ASIA_PO FrankelPO duree_sejour ATCD_RoRx RoRx_PO
## 1                      0       D         D           13         0       1
## 2                      0       E         E            4         1       0
## 3                      0       D         D           12         0       1
## 4                      0       D         D           25         1       1
## 5                      0       E         E            5         1       1
## 6                      1       E         D           16         1       0
##   TypeSurgery chimiotxPO Tokuhashi Tokuhashi_cat SINS SINS_cat
## 1          CO          1         5             1   12        2
## 2          CO          0         9             2    8        2
## 3          CO          1         6             1   10        2
## 4          LA          0         6             1    6        1
## 5          CO          0         6             1   11        2
## 6          CO          1         5             1   10        2
##   ImprovementPainPO deces date_deces SurviePosOpMOIS LastFollowUp BloodLoss
## 1                 1     0 31/10/2018            4.77   31/10/2018      1500
## 2                 1     0 31/10/2018            4.07   15/08/2018       500
## 3                 1     0 31/10/2018            8.13     1/4/2018       300
## 4                 1     1  12/2/2018            1.93     9/2/2018       200
## 5                 1     1   1/2/2018            1.57   30/01/2018      2000
## 6                 1     1  12/8/2017           12.67   18/07/2017       200
##   DurationSurgery Histology
## 1             299         1
## 2             124         1
## 3             108         1
## 4             110         1
## 5             126         1
## 6             120         1
names(data) #name of variables
##  [1] "ID"                     "dossier"                "chirurgien"            
##  [4] "age"                    "sexe"                   "tabagisme"             
##  [7] "Apparition"             "ASIA_preop"             "Frankel_preop"         
## [10] "ambulation_preop"       "fctSphincter"           "approcheChx"           
## [13] "dateChx"                "ambulationPO"           "amelioration_ambulance"
## [16] "ASIA_PO"                "FrankelPO"              "duree_sejour"          
## [19] "ATCD_RoRx"              "RoRx_PO"                "TypeSurgery"           
## [22] "chimiotxPO"             "Tokuhashi"              "Tokuhashi_cat"         
## [25] "SINS"                   "SINS_cat"               "ImprovementPainPO"     
## [28] "deces"                  "date_deces"             "SurviePosOpMOIS"       
## [31] "LastFollowUp"           "BloodLoss"              "DurationSurgery"       
## [34] "Histology"

##explaination

###subset dataset -Independent vars:

data1 <- subset(data, select=c(ID, sexe, age, tabagisme,Histology, ASIA_preop, ASIA_PO, Tokuhashi_cat,ambulation_preop, ambulationPO, RoRx_PO,chimiotxPO, SurviePosOpMOIS, deces))

Independent variables

data1$age_gr <- ifelse(data1$age >= 60,c("0"), c("1")) #Age: 60+; <60;  (age) -need to group

data1$sexe = factor(data1$sexe) # Sex: male - female (sexe)
data1$tabagisme = factor(data1$tabagisme) #Tobacco use: Yes - No (tabagisme)
data1$Histology = factor(data1$Histology) #Histologic type: ADC;  Non-ADC; SCLC  (Histology)
data1$ASIA_preop = factor(data1$ASIA_preop) #Pre. ASIA score: A, C, D, E   (ASIA_preop)
data1$ASIA_PO = factor(data1$ASIA_PO) #Post. ASIA score: C, D, E  (ASIA_PO)
data1$Tokuhashi_cat = factor(data1$Tokuhashi_cat) #Revised Tokuhashi score: 0-8; 9-11   (Tokuhashi_cat)
data1$ambulation_preop = factor(data1$ambulation_preop) #Pre. ambulatory status: No, With help; Independent   (ambulation_preop)
data1$ambulationPO = factor(data1$ambulationPO) #Post. ambulatory status: No, With help; Independent  (ambulationPO)
data1$RoRx_PO = factor(data1$RoRx_PO)  #Post. Radiotherapy: No- Yes   (RoRx_PO)
data1$chimiotxPO = factor(data1$chimiotxPO) #Post. Chemotherapy: No- Yes  (chimiotxPO)

####View new dataset again

head(data1)
##   ID sexe age tabagisme Histology ASIA_preop ASIA_PO Tokuhashi_cat
## 1  1    1  55         1         1          D       D             1
## 2  2    1  71         0         1          E       E             2
## 3  3    2  68         1         1          D       D             1
## 4  4    1  71         1         1          D       D             1
## 5  5    1  63         1         1          E       E             1
## 6  6    2  65         1         1          D       E             1
##   ambulation_preop ambulationPO RoRx_PO chimiotxPO SurviePosOpMOIS deces age_gr
## 1                2            2       1          1            4.77     0      1
## 2                1            1       0          0            4.07     0      0
## 3                2            2       1          1            8.13     0      0
## 4                2            2       1          0            1.93     1      0
## 5                1            1       1          0            1.57     1      0
## 6                2            1       0          1           12.67     1      0
View(data1)

###Outcome: SurviePosOpMOIS: time-to-event deces: 1: censorted (alive); 0 event (die)

baseline = Surv(data1$SurviePosOpMOIS, data1$deces==0)
km = survfit(baseline ~ 1)
summary(km)
## Call: survfit(formula = baseline ~ 1)
## 
##   time n.risk n.event survival std.err lower 95% CI upper 95% CI
##   4.07     44       1    0.977  0.0225        0.934            1
##   4.77     36       1    0.950  0.0345        0.885            1
##   8.13     22       1    0.907  0.0536        0.808            1
##  28.67      5       1    0.726  0.1678        0.461            1

Including Plots

Kaplan – Meier plot:

plot(km, xlab="Time to death", ylab="Prob of survival")

#table 1: general chracteristics of participants

#install package "moonBook", ztable
library(moonBook)
require(ztable)
## Loading required package: ztable
## Welcome to package ztable ver 0.2.0
require(magrittr)
## Loading required package: magrittr
options(ztable.type="html")
#table 1: general chracteristics of participants

mytable(data1)
## 
##       Descriptive Statistics      
## ----------------------------------- 
##                     N      Total   
## ----------------------------------- 
##  ID                87 44.0 ± 25.3 
##  sexe              87              
##    - 1                 45  (51.7%) 
##    - 2                 42  (48.3%) 
##  age               87  61.3 ± 8.8 
##  tabagisme         87              
##    - 0                 35  (40.2%) 
##    - 1                 52  (59.8%) 
##  Histology         87              
##    - 0                 22  (25.3%) 
##    - 1                 58  (66.7%) 
##    - 2                   7  (8.0%) 
##  ASIA_preop        87              
##    - A                   1  (1.1%) 
##    - C                   5  (5.7%) 
##    - D                 39  (44.8%) 
##    - E                 42  (48.3%) 
##  ASIA_PO           87              
##    - C                   2  (2.3%) 
##    - D                 27  (31.0%) 
##    - E                 58  (66.7%) 
##  Tokuhashi_cat     87              
##    - 1                 72  (82.8%) 
##    - 2                 15  (17.2%) 
##  ambulation_preop  87              
##    - 0                 22  (25.3%) 
##    - 1                 41  (47.1%) 
##    - 2                 24  (27.6%) 
##  ambulationPO      87              
##    - 0                   4  (4.6%) 
##    - 1                 48  (55.2%) 
##    - 2                 35  (40.2%) 
##  RoRx_PO           87              
##    - 0                 28  (32.2%) 
##    - 1                 59  (67.8%) 
##  chimiotxPO        87              
##    - 0                 54  (62.1%) 
##    - 1                 33  (37.9%) 
##  SurviePosOpMOIS   87  7.5 ± 12.0 
##  deces             87              
##    - 0                   4  (4.6%) 
##    - 1                 83  (95.4%) 
##  age_gr            87              
##    - 0                 51  (58.6%) 
##    - 1                 36  (41.4%) 
## -----------------------------------

#Univariate cox-model

library(survival)
#model 1: sex
cox1 = coxph(Surv(data1$SurviePosOpMOIS, data1$deces==0) ~ data1$sexe)
summary(cox1)
## Call:
## coxph(formula = Surv(data1$SurviePosOpMOIS, data1$deces == 0) ~ 
##     data1$sexe)
## 
##   n= 87, number of events= 4 
## 
##                coef exp(coef) se(coef)      z Pr(>|z|)
## data1$sexe2 -0.5513    0.5762   1.0309 -0.535    0.593
## 
##             exp(coef) exp(-coef) lower .95 upper .95
## data1$sexe2    0.5762      1.736    0.0764     4.346
## 
## Concordance= 0.665  (se = 0.097 )
## Likelihood ratio test= 0.28  on 1 df,   p=0.6
## Wald test            = 0.29  on 1 df,   p=0.6
## Score (logrank) test = 0.29  on 1 df,   p=0.6
#model 2: age group
cox2 = coxph(Surv(data1$SurviePosOpMOIS, data1$deces==0) ~ data1$age_gr)
summary(cox2)
## Call:
## coxph(formula = Surv(data1$SurviePosOpMOIS, data1$deces == 0) ~ 
##     data1$age_gr)
## 
##   n= 87, number of events= 4 
## 
##                  coef exp(coef) se(coef)      z Pr(>|z|)
## data1$age_gr1 -0.5595    0.5715   1.2260 -0.456    0.648
## 
##               exp(coef) exp(-coef) lower .95 upper .95
## data1$age_gr1    0.5715       1.75   0.05169     6.319
## 
## Concordance= 0.558  (se = 0.148 )
## Likelihood ratio test= 0.22  on 1 df,   p=0.6
## Wald test            = 0.21  on 1 df,   p=0.6
## Score (logrank) test = 0.21  on 1 df,   p=0.6
#model 3: tobacco use 
cox3 = coxph(Surv(data1$SurviePosOpMOIS, data1$deces==0) ~ data1$tabagisme)
summary(cox3)
## Call:
## coxph(formula = Surv(data1$SurviePosOpMOIS, data1$deces == 0) ~ 
##     data1$tabagisme)
## 
##   n= 87, number of events= 4 
## 
##                   coef exp(coef) se(coef)     z Pr(>|z|)
## data1$tabagisme1 1.635     5.129    1.194 1.369    0.171
## 
##                  exp(coef) exp(-coef) lower .95 upper .95
## data1$tabagisme1     5.129      0.195    0.4937     53.29
## 
## Concordance= 0.568  (se = 0.162 )
## Likelihood ratio test= 2.22  on 1 df,   p=0.1
## Wald test            = 1.87  on 1 df,   p=0.2
## Score (logrank) test = 2.23  on 1 df,   p=0.1
#Model 4: Histology
cox4 = coxph(Surv(data1$SurviePosOpMOIS, data1$deces==0) ~ data1$Histology)
## Warning in fitter(X, Y, strats, offset, init, control, weights = weights, :
## Loglik converged before variable 1 ; coefficient may be infinite.
summary(cox4)
## Call:
## coxph(formula = Surv(data1$SurviePosOpMOIS, data1$deces == 0) ~ 
##     data1$Histology)
## 
##   n= 87, number of events= 4 
## 
##                       coef exp(coef)  se(coef)     z Pr(>|z|)
## data1$Histology1 1.965e+01 3.411e+08 1.548e+04 0.001    0.999
## data1$Histology2 1.120e-01 1.119e+00 3.365e+04 0.000    1.000
## 
##                  exp(coef) exp(-coef) lower .95 upper .95
## data1$Histology1 3.411e+08  2.932e-09         0       Inf
## data1$Histology2 1.118e+00  8.941e-01         0       Inf
## 
## Concordance= 0.646  (se = 0.038 )
## Likelihood ratio test= 2.89  on 2 df,   p=0.2
## Wald test            = 0  on 2 df,   p=1
## Score (logrank) test = 1.75  on 2 df,   p=0.4
#model 5: Pre. ASIA score
cox5 = coxph(Surv(data1$SurviePosOpMOIS, data1$deces==0) ~ data1$ASIA_preop)
## Warning in fitter(X, Y, strats, offset, init, control, weights = weights, :
## Loglik converged before variable 1 ; coefficient may be infinite.
summary(cox5)
## Call:
## coxph(formula = Surv(data1$SurviePosOpMOIS, data1$deces == 0) ~ 
##     data1$ASIA_preop)
## 
##   n= 87, number of events= 4 
## 
##                         coef  exp(coef)   se(coef)      z Pr(>|z|)
## data1$ASIA_preopC -1.562e+01  1.652e-07  1.408e+04 -0.001    0.999
## data1$ASIA_preopD  9.303e-01  2.535e+00  1.227e+00  0.758    0.448
## data1$ASIA_preopE         NA         NA  0.000e+00     NA       NA
## 
##                   exp(coef) exp(-coef) lower .95 upper .95
## data1$ASIA_preopC 1.652e-07  6.052e+06    0.0000       Inf
## data1$ASIA_preopD 2.535e+00  3.944e-01    0.2288      28.1
## data1$ASIA_preopE        NA         NA        NA        NA
## 
## Concordance= 0.568  (se = 0.157 )
## Likelihood ratio test= 0.72  on 2 df,   p=0.7
## Wald test            = 0.57  on 2 df,   p=0.8
## Score (logrank) test = 0.68  on 2 df,   p=0.7
#model 6: Post. ASIA score
cox6 = coxph(Surv(data1$SurviePosOpMOIS, data1$deces==0) ~ data1$ASIA_PO)
## Warning in fitter(X, Y, strats, offset, init, control, weights = weights, :
## Loglik converged before variable 1,2 ; coefficient may be infinite.
summary(cox6)
## Call:
## coxph(formula = Surv(data1$SurviePosOpMOIS, data1$deces == 0) ~ 
##     data1$ASIA_PO)
## 
##   n= 87, number of events= 4 
## 
##                     coef exp(coef)  se(coef)     z Pr(>|z|)
## data1$ASIA_POD 1.733e+01 3.368e+07 1.429e+04 0.001    0.999
## data1$ASIA_POE 1.519e+01 3.950e+06 1.429e+04 0.001    0.999
## 
##                exp(coef) exp(-coef) lower .95 upper .95
## data1$ASIA_POD  33676279  2.969e-08         0       Inf
## data1$ASIA_POE   3950104  2.532e-07         0       Inf
## 
## Concordance= 0.684  (se = 0.155 )
## Likelihood ratio test= 3.28  on 2 df,   p=0.2
## Wald test            = 2.99  on 2 df,   p=0.2
## Score (logrank) test = 4.37  on 2 df,   p=0.1
#model 7: Revised Tokuhashi score

cox7 = coxph(Surv(data1$SurviePosOpMOIS, data1$deces==0) ~ data1$Tokuhashi_cat)
summary(cox7)
## Call:
## coxph(formula = Surv(data1$SurviePosOpMOIS, data1$deces == 0) ~ 
##     data1$Tokuhashi_cat)
## 
##   n= 87, number of events= 4 
## 
##                        coef exp(coef) se(coef)     z Pr(>|z|)
## data1$Tokuhashi_cat2 0.4573    1.5798   1.1112 0.412    0.681
## 
##                      exp(coef) exp(-coef) lower .95 upper .95
## data1$Tokuhashi_cat2      1.58      0.633     0.179     13.95
## 
## Concordance= 0.587  (se = 0.161 )
## Likelihood ratio test= 0.17  on 1 df,   p=0.7
## Wald test            = 0.17  on 1 df,   p=0.7
## Score (logrank) test = 0.17  on 1 df,   p=0.7
#model 8: Pre. ambulatory status

cox8 = coxph(Surv(data1$SurviePosOpMOIS, data1$deces==0) ~ data1$ambulation_preop)
## Warning in fitter(X, Y, strats, offset, init, control, weights = weights, :
## Loglik converged before variable 1,2 ; coefficient may be infinite.
summary(cox8)
## Call:
## coxph(formula = Surv(data1$SurviePosOpMOIS, data1$deces == 0) ~ 
##     data1$ambulation_preop)
## 
##   n= 87, number of events= 4 
## 
##                              coef exp(coef)  se(coef)     z Pr(>|z|)
## data1$ambulation_preop1 1.777e+01 5.239e+07 1.731e+04 0.001    0.999
## data1$ambulation_preop2 1.969e+01 3.546e+08 1.731e+04 0.001    0.999
## 
##                         exp(coef) exp(-coef) lower .95 upper .95
## data1$ambulation_preop1  52385120  1.909e-08         0       Inf
## data1$ambulation_preop2 354636157  2.820e-09         0       Inf
## 
## Concordance= 0.694  (se = 0.127 )
## Likelihood ratio test= 4.2  on 2 df,   p=0.1
## Wald test            = 2.67  on 2 df,   p=0.3
## Score (logrank) test = 4.52  on 2 df,   p=0.1
#model 9: Post. ambulatory status
cox9 = coxph(Surv(data1$SurviePosOpMOIS, data1$deces==0) ~ data1$ambulationPO)
summary(cox9)
## Call:
## coxph(formula = Surv(data1$SurviePosOpMOIS, data1$deces == 0) ~ 
##     data1$ambulationPO)
## 
##   n= 87, number of events= 4 
## 
##                        coef exp(coef) se(coef)      z Pr(>|z|)
## data1$ambulationPO1 -1.7728    0.1699   1.2388 -1.431    0.152
## data1$ambulationPO2      NA        NA   0.0000     NA       NA
## 
##                     exp(coef) exp(-coef) lower .95 upper .95
## data1$ambulationPO1    0.1699      5.887   0.01498     1.925
## data1$ambulationPO2        NA         NA        NA        NA
## 
## Concordance= 0.646  (se = 0.153 )
## Likelihood ratio test= 2.21  on 1 df,   p=0.1
## Wald test            = 2.05  on 1 df,   p=0.2
## Score (logrank) test = 2.6  on 1 df,   p=0.1
#model 10: Post. Radiotherapy

cox10 = coxph(Surv(data1$SurviePosOpMOIS, data1$deces==0) ~ data1$RoRx_PO)
summary(cox10)
## Call:
## coxph(formula = Surv(data1$SurviePosOpMOIS, data1$deces == 0) ~ 
##     data1$RoRx_PO)
## 
##   n= 87, number of events= 4 
## 
##                   coef exp(coef) se(coef)      z Pr(>|z|)
## data1$RoRx_PO1 -0.7463    0.4741   1.2372 -0.603    0.546
## 
##                exp(coef) exp(-coef) lower .95 upper .95
## data1$RoRx_PO1    0.4741      2.109   0.04196     5.357
## 
## Concordance= 0.612  (se = 0.14 )
## Likelihood ratio test= 0.33  on 1 df,   p=0.6
## Wald test            = 0.36  on 1 df,   p=0.5
## Score (logrank) test = 0.38  on 1 df,   p=0.5
#model 11: Post. Chemotherapy

cox11 = coxph(Surv(data1$SurviePosOpMOIS, data1$deces==0) ~ data1$chimiotxPO)
summary(cox11)
## Call:
## coxph(formula = Surv(data1$SurviePosOpMOIS, data1$deces == 0) ~ 
##     data1$chimiotxPO)
## 
##   n= 87, number of events= 4 
## 
##                       coef exp(coef) se(coef)      z Pr(>|z|)
## data1$chimiotxPO1 -0.05409   0.94735  1.23130 -0.044    0.965
## 
##                   exp(coef) exp(-coef) lower .95 upper .95
## data1$chimiotxPO1    0.9474      1.056   0.08481     10.58
## 
## Concordance= 0.549  (se = 0.138 )
## Likelihood ratio test= 0  on 1 df,   p=1
## Wald test            = 0  on 1 df,   p=1
## Score (logrank) test = 0  on 1 df,   p=1

#multivariate model

#model 12: multivariate model

cox12 = coxph(Surv(data1$SurviePosOpMOIS, data1$deces==0) ~ data1$sexe+data1$age_gr+data1$RoRx_PO +data1$tabagisme+   data1$Histology+data1$ASIA_PO+data1$ASIA_preop +data1$Tokuhashi_cat+ data1$ambulation_preop+  data1$ambulationPO+data1$RoRx_PO+data1$chimiotxPO)
## Warning in fitter(X, Y, strats, offset, init, control, weights = weights, : Ran
## out of iterations and did not converge
summary(cox12)
## Call:
## coxph(formula = Surv(data1$SurviePosOpMOIS, data1$deces == 0) ~ 
##     data1$sexe + data1$age_gr + data1$RoRx_PO + data1$tabagisme + 
##         data1$Histology + data1$ASIA_PO + data1$ASIA_preop + 
##         data1$Tokuhashi_cat + data1$ambulation_preop + data1$ambulationPO + 
##         data1$RoRx_PO + data1$chimiotxPO)
## 
##   n= 87, number of events= 4 
## 
##                               coef  exp(coef)   se(coef)      z Pr(>|z|)
## data1$sexe2             -4.301e+01  2.085e-19  2.737e+03 -0.016    0.987
## data1$age_gr1           -2.617e+01  4.324e-12  2.684e+03 -0.010    0.992
## data1$RoRx_PO1          -4.354e+01  1.238e-19  4.676e+03 -0.009    0.993
## data1$tabagisme1         3.550e+01  2.603e+15  2.956e+03  0.012    0.990
## data1$Histology1        -3.404e+01  1.651e-15  7.646e+03 -0.004    0.996
## data1$Histology2        -1.116e+02  3.350e-49  2.618e+04 -0.004    0.997
## data1$ASIA_POD          -1.255e+02  3.030e-55  3.176e+03 -0.040    0.968
## data1$ASIA_POE          -1.827e+02  4.454e-80  3.177e+03 -0.058    0.954
## data1$ASIA_preopC       -1.810e+01  1.373e-08  1.788e+05  0.000    1.000
## data1$ASIA_preopD       -2.928e+01  1.926e-13  3.301e+03 -0.009    0.993
## data1$ASIA_preopE       -1.334e+01  1.601e-06  3.302e+03 -0.004    0.997
## data1$Tokuhashi_cat2     5.517e+01  9.158e+23  3.467e+03  0.016    0.987
## data1$ambulation_preop1  2.257e+01  6.349e+09  2.956e+03  0.008    0.994
## data1$ambulation_preop2  4.683e+01  2.173e+20  2.921e+03  0.016    0.987
## data1$ambulationPO1     -2.708e+00  6.666e-02  3.193e+03 -0.001    0.999
## data1$ambulationPO2      2.373e+00  1.073e+01  3.193e+03  0.001    0.999
## data1$chimiotxPO1        1.513e+00  4.541e+00  4.296e+03  0.000    1.000
## 
##                         exp(coef) exp(-coef) lower .95 upper .95
## data1$sexe2             2.085e-19  4.797e+18         0       Inf
## data1$age_gr1           4.324e-12  2.313e+11         0       Inf
## data1$RoRx_PO1          1.238e-19  8.079e+18         0       Inf
## data1$tabagisme1        2.603e+15  3.841e-16         0       Inf
## data1$Histology1        1.651e-15  6.056e+14         0       Inf
## data1$Histology2        3.350e-49  2.985e+48         0       Inf
## data1$ASIA_POD          3.030e-55  3.301e+54         0       Inf
## data1$ASIA_POE          4.454e-80  2.245e+79         0       Inf
## data1$ASIA_preopC       1.373e-08  7.284e+07         0       Inf
## data1$ASIA_preopD       1.926e-13  5.192e+12         0       Inf
## data1$ASIA_preopE       1.601e-06  6.246e+05         0       Inf
## data1$Tokuhashi_cat2    9.158e+23  1.092e-24         0       Inf
## data1$ambulation_preop1 6.349e+09  1.575e-10         0       Inf
## data1$ambulation_preop2 2.173e+20  4.601e-21         0       Inf
## data1$ambulationPO1     6.666e-02  1.500e+01         0       Inf
## data1$ambulationPO2     1.073e+01  9.324e-02         0       Inf
## data1$chimiotxPO1       4.541e+00  2.202e-01         0       Inf
## 
## Concordance= 1  (se = 0 )
## Likelihood ratio test= 24.14  on 17 df,   p=0.1
## Wald test            = 0.01  on 17 df,   p=1
## Score (logrank) test = 15.5  on 17 df,   p=0.6
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