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

#Prepare: packages, data

library("foreign")
library("survival")

require("moonBook")
## Loading required package: 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")

#read data and group for variable

Read Data

data=read.csv("D:/data/data csv.csv")
head(data)
##   ID Dossier age age_code Sexe sex_code Tabagisme localisation   Origin
## 1  1  983188  49        0    F        0         1            1   Kidney
## 2  2  821908  67        1    F        0         0            1   Breast
## 3  3  390270  65        1    F        0         1            1     Lung
## 4  4  938314  59        0    M        1         1            1 Prostate
## 5  5 7049669  69        1    F        0         0            1   Breast
## 6  6 7149632  56        0    M        1         1            1   Kidney
##   Pathologie Apparition ASIAPreOp AmbulationPreOP fctSphincter approcheChx
## 1  Carcinoma          2         2               1            1           1
## 2  Carcinoma          1         3               1            2           3
## 3  Carcinoma          1         2               2            2           2
## 4  Carcinoma          1         2               0            2           1
## 5  Carcinoma          1         3               2            2           1
## 6  Carcinoma          2         2               2            2           3
##   ScoreASA    DateChx DureeChx PertesSang MedicalComplic SurgicalComplic
## 1        3 22/11/2017      278       1200              0               0
## 2        3 27/09/2017      479        700              0               0
## 3        3 28/07/2016      120        200              1               0
## 4        3   6/2/2016      115        400              0               0
## 5        3  9/10/2015      155        200              0               0
## 6        3  7/11/2014      320       1500              1               0
##   WoundInfection AmbulationPO Improvement.of.ambulation PainImprovPO ASIA_PO
## 1              1            1                         0            3       4
## 2              0            1                         0            3       4
## 3              0            1                         1            3       4
## 4              0            1                         1            3       4
## 5              0            2                         0            3       4
## 6              0            0                         2            1       0
##   Improvement.of.ASIA.score DureeSejour RoRxPreOP RoRxPO SystemicTherapyPO
## 1                         1           5         0      1                 1
## 2                        NA           9         1      1                 1
## 3                         1          16         0      0                 1
## 4                         1           6         0      1                 1
## 5                        NA           5         0      1                 1
## 6                         2           8         0      0                 0
##   Tokuhashi Tokuhashi_cat Deces  dateDeces  dernierRV SurviePosOpMOIS
## 1         9             2     0 31/10/2019 24/05/2019           23.60
## 2        13             3     0 31/10/2019   9/2/2018           25.47
## 3         5             1     1  12/8/2017  12/8/2017           12.67
## 4         7             1     1 20/05/2016 20/05/2016            3.47
## 5        12             3     1 16/04/2016 30/11/2015            6.33
## 6         7             1     1  8/11/2014  8/11/2014            0.03
##   DureeSuivi DernSuiviDeces
## 1      18.27           5.33
## 2       4.50          20.97
## 3      12.67           0.00
## 4       3.47           0.00
## 5       1.73           4.60
## 6       0.03           0.00
names(data) #name of variables
##  [1] "ID"                        "Dossier"                  
##  [3] "age"                       "age_code"                 
##  [5] "Sexe"                      "sex_code"                 
##  [7] "Tabagisme"                 "localisation"             
##  [9] "Origin"                    "Pathologie"               
## [11] "Apparition"                "ASIAPreOp"                
## [13] "AmbulationPreOP"           "fctSphincter"             
## [15] "approcheChx"               "ScoreASA"                 
## [17] "DateChx"                   "DureeChx"                 
## [19] "PertesSang"                "MedicalComplic"           
## [21] "SurgicalComplic"           "WoundInfection"           
## [23] "AmbulationPO"              "Improvement.of.ambulation"
## [25] "PainImprovPO"              "ASIA_PO"                  
## [27] "Improvement.of.ASIA.score" "DureeSejour"              
## [29] "RoRxPreOP"                 "RoRxPO"                   
## [31] "SystemicTherapyPO"         "Tokuhashi"                
## [33] "Tokuhashi_cat"             "Deces"                    
## [35] "dateDeces"                 "dernierRV"                
## [37] "SurviePosOpMOIS"           "DureeSuivi"               
## [39] "DernSuiviDeces"

##explaination

###subset dataset -Independent vars:

a <- subset(data, select=c(localisation, sex_code, age_code, Tabagisme, ASIAPreOp, ASIA_PO, Tokuhashi_cat,AmbulationPreOP, AmbulationPO, RoRxPO,SystemicTherapyPO, Improvement.of.ambulation, SurviePosOpMOIS, Deces))

data1=a

Independent variables

data1$sex_code = factor(data1$sex_code) # Sex: male - female (sexe)
data1$age_code=factor(data1$age_code) 
data1$Tabagisme = factor(data1$Tabagisme) #Tobacco use: Yes - No (tabagisme)
data1$ASIAPreOp = factor(data1$ASIAPreOp) #Pre. ASIA score: A, C, D, E   (ASIAPr3Op)
data1$ASIA_PO = factor(data1$ASIA_PO) #Post. ASIA score: A, B C, D, E  (ASIA_PO)
data1$Tokuhashi_cat = factor(data1$Tokuhashi_cat) #Revised Tokuhashi score: 0-8; 9-11   (Tokuhashi_cat)
data1$AmbulationPreOP = factor(data1$AmbulationPreOP) #Pre. ambulatory status: No, With help; Independent   (ambulation_preop)
data1$AmbulationPO = factor(data1$AmbulationPO) #Post. ambulatory status: No, With help; Independent  (ambulationPO)
data1$RoRxPO = factor(data1$RoRxPO)  #Post. Radiotherapy: No- Yes   (RoRx_PO)
data1$SystemicTherapyPO = factor(data1$SystemicTherapyPO) #

####View new dataset again

head(data1)
##   localisation sex_code age_code Tabagisme ASIAPreOp ASIA_PO Tokuhashi_cat
## 1            1        0        0         1         2       4             2
## 2            1        0        1         0         3       4             3
## 3            1        0        1         1         2       4             1
## 4            1        1        0         1         2       4             1
## 5            1        0        1         0         3       4             3
## 6            1        1        0         1         2       0             1
##   AmbulationPreOP AmbulationPO RoRxPO SystemicTherapyPO
## 1               1            1      1                 1
## 2               1            1      1                 1
## 3               2            1      0                 1
## 4               0            1      1                 1
## 5               2            2      1                 1
## 6               2            0      0                 0
##   Improvement.of.ambulation SurviePosOpMOIS Deces
## 1                         0           23.60     0
## 2                         0           25.47     0
## 3                         1           12.67     1
## 4                         1            3.47     1
## 5                         0            6.33     1
## 6                         2            0.03     1
View(data1)

#participant chracteristics

mytable(localisation~sex_code+age_code+Tabagisme+ ASIAPreOp+ ASIA_PO+ Tokuhashi_cat+AmbulationPreOP+ AmbulationPO+ RoRxPO+SystemicTherapyPO+ Improvement.of.ambulation,data=data1)
## 
##             Descriptive Statistics by 'localisation'            
## _________________________________________________________________ 
##                                1          2          3        p  
##                              (N=47)     (N=96)     (N=48)  
## ----------------------------------------------------------------- 
##  sex_code                                                   0.877
##    - 0                     23 (48.9%) 43 (44.8%) 23 (47.9%)      
##    - 1                     24 (51.1%) 53 (55.2%) 25 (52.1%)      
##  age_code                                                   0.444
##    - 0                     22 (46.8%) 35 (36.5%) 21 (43.8%)      
##    - 1                     25 (53.2%) 61 (63.5%) 27 (56.2%)      
##  Tabagisme                                                  0.056
##    - 0                     23 (48.9%) 57 (59.4%) 35 (72.9%)      
##    - 1                     24 (51.1%) 39 (40.6%) 13 (27.1%)      
##  ASIAPreOp                                                  0.383
##    - 0                      0 ( 0.0%) 1 ( 1.0%)   0 ( 0.0%)      
##    - 1                     2 ( 4.3%)  13 (13.5%) 8 (16.7%)       
##    - 2                     22 (46.8%) 40 (41.7%) 24 (50.0%)      
##    - 3                     23 (48.9%) 42 (43.8%) 16 (33.3%)      
##  ASIA_PO                                                    0.848
##    - 0                     1 ( 2.1%)  1 ( 1.0%)   0 ( 0.0%)      
##    - 1                      0 ( 0.0%) 1 ( 1.0%)   0 ( 0.0%)      
##    - 2                      0 ( 0.0%) 1 ( 1.0%)  1 ( 2.1%)       
##    - 3                     14 (29.8%) 35 (36.5%) 19 (39.6%)      
##    - 4                     32 (68.1%) 58 (60.4%) 28 (58.3%)      
##  Tokuhashi_cat                                              0.325
##    - 1                     17 (36.2%) 42 (43.8%) 17 (35.4%)      
##    - 2                     17 (36.2%) 38 (39.6%) 16 (33.3%)      
##    - 3                     13 (27.7%) 16 (16.7%) 15 (31.2%)      
##  AmbulationPreOP                                            0.503
##    - 0                     7 (14.9%)  21 (21.9%) 14 (29.2%)      
##    - 1                     29 (61.7%) 52 (54.2%) 22 (45.8%)      
##    - 2                     11 (23.4%) 23 (24.0%) 12 (25.0%)      
##  AmbulationPO                                               0.039
##    - 0                     5 (10.6%)  5 ( 5.2%)  3 ( 6.2%)       
##    - 1                     30 (63.8%) 51 (53.1%) 18 (37.5%)      
##    - 2                     12 (25.5%) 40 (41.7%) 27 (56.2%)      
##  RoRxPO                                                     0.239
##    - 0                     16 (34.0%) 25 (26.3%) 9 (18.8%)       
##    - 1                     31 (66.0%) 70 (73.7%) 39 (81.2%)      
##  SystemicTherapyPO                                          0.769
##    - 0                     21 (44.7%) 47 (49.0%) 25 (52.1%)      
##    - 1                     26 (55.3%) 49 (51.0%) 23 (47.9%)      
##  Improvement.of.ambulation                                  0.521
##    - 0                     30 (63.8%) 58 (60.4%) 23 (47.9%)      
##    - 1                     10 (21.3%) 25 (26.0%) 15 (31.2%)      
##    - 2                     7 (14.9%)  13 (13.5%) 10 (20.8%)      
## -----------------------------------------------------------------

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

library("survival")
baseline = Surv(data1$SurviePosOpMOIS, data1$Deces==1)
km = survfit(baseline ~ 1)
km
## Call: survfit(formula = baseline ~ 1)
## 
##       n  events  median 0.95LCL 0.95UCL 
##   191.0   176.0     7.0     5.7    10.0
summary(km)
## Call: survfit(formula = baseline ~ 1)
## 
##   time n.risk n.event survival std.err lower 95% CI upper 95% CI
##   0.03    191       1   0.9948 0.00522       0.9846        1.000
##   0.07    190       1   0.9895 0.00737       0.9752        1.000
##   0.43    189       1   0.9843 0.00900       0.9668        1.000
##   0.60    188       1   0.9791 0.01036       0.9590        1.000
##   0.70    187       1   0.9738 0.01155       0.9514        0.997
##   0.77    186       1   0.9686 0.01262       0.9442        0.994
##   0.80    185       1   0.9634 0.01360       0.9371        0.990
##   0.83    184       2   0.9529 0.01533       0.9233        0.983
##   0.87    182       1   0.9476 0.01612       0.9166        0.980
##   0.90    181       1   0.9424 0.01686       0.9099        0.976
##   1.00    180       2   0.9319 0.01822       0.8969        0.968
##   1.07    178       1   0.9267 0.01886       0.8905        0.964
##   1.10    177       1   0.9215 0.01946       0.8841        0.960
##   1.13    176       1   0.9162 0.02005       0.8778        0.956
##   1.17    175       1   0.9110 0.02060       0.8715        0.952
##   1.20    174       1   0.9058 0.02114       0.8653        0.948
##   1.27    173       1   0.9005 0.02166       0.8591        0.944
##   1.37    172       1   0.8953 0.02215       0.8529        0.940
##   1.57    171       1   0.8901 0.02264       0.8468        0.936
##   1.60    170       2   0.8796 0.02355       0.8346        0.927
##   1.63    168       1   0.8743 0.02398       0.8286        0.923
##   1.67    167       2   0.8639 0.02481       0.8166        0.914
##   1.70    165       1   0.8586 0.02521       0.8106        0.909
##   1.73    164       1   0.8534 0.02559       0.8047        0.905
##   1.77    163       2   0.8429 0.02633       0.7929        0.896
##   1.80    161       1   0.8377 0.02668       0.7870        0.892
##   1.83    160       1   0.8325 0.02702       0.7811        0.887
##   1.87    159       1   0.8272 0.02735       0.7753        0.883
##   1.93    158       1   0.8220 0.02768       0.7695        0.878
##   1.97    157       1   0.8168 0.02799       0.7637        0.874
##   2.20    156       1   0.8115 0.02830       0.7579        0.869
##   2.27    155       1   0.8063 0.02860       0.7521        0.864
##   2.30    154       2   0.7958 0.02917       0.7406        0.855
##   2.33    152       1   0.7906 0.02944       0.7349        0.850
##   2.37    151       1   0.7853 0.02971       0.7292        0.846
##   2.40    150       1   0.7801 0.02997       0.7235        0.841
##   2.57    149       1   0.7749 0.03022       0.7178        0.836
##   2.67    148       1   0.7696 0.03047       0.7122        0.832
##   2.70    147       2   0.7592 0.03094       0.7009        0.822
##   2.90    145       2   0.7487 0.03139       0.6896        0.813
##   3.20    143       1   0.7435 0.03160       0.6840        0.808
##   3.37    142       1   0.7382 0.03181       0.6784        0.803
##   3.43    141       2   0.7277 0.03221       0.6673        0.794
##   3.47    139       1   0.7225 0.03240       0.6617        0.789
##   3.50    138       1   0.7173 0.03258       0.6562        0.784
##   3.63    137       1   0.7120 0.03276       0.6506        0.779
##   3.67    136       1   0.7068 0.03294       0.6451        0.774
##   3.70    135       1   0.7016 0.03311       0.6396        0.770
##   3.80    134       1   0.6963 0.03327       0.6341        0.765
##   3.83    133       1   0.6911 0.03343       0.6286        0.760
##   3.87    132       1   0.6859 0.03359       0.6231        0.755
##   3.97    131       1   0.6806 0.03374       0.6176        0.750
##   4.00    130       1   0.6754 0.03388       0.6121        0.745
##   4.10    129       2   0.6649 0.03415       0.6012        0.735
##   4.13    127       1   0.6597 0.03428       0.5958        0.730
##   4.17    126       2   0.6492 0.03453       0.5849        0.721
##   4.30    124       1   0.6440 0.03465       0.5795        0.716
##   4.33    123       1   0.6387 0.03476       0.5741        0.711
##   4.37    122       1   0.6335 0.03487       0.5687        0.706
##   4.43    121       1   0.6283 0.03497       0.5633        0.701
##   4.50    120       1   0.6230 0.03507       0.5580        0.696
##   4.53    119       1   0.6178 0.03516       0.5526        0.691
##   4.57    118       1   0.6126 0.03525       0.5472        0.686
##   4.70    117       1   0.6073 0.03534       0.5419        0.681
##   4.73    116       3   0.5916 0.03557       0.5259        0.666
##   4.90    113       1   0.5864 0.03563       0.5205        0.661
##   4.97    112       1   0.5812 0.03570       0.5152        0.656
##   5.43    111       1   0.5759 0.03576       0.5099        0.650
##   5.67    110       1   0.5707 0.03582       0.5046        0.645
##   5.70    109       2   0.5602 0.03592       0.4941        0.635
##   5.80    107       2   0.5497 0.03600       0.4835        0.625
##   6.00    105       1   0.5445 0.03604       0.4783        0.620
##   6.10    104       1   0.5393 0.03607       0.4730        0.615
##   6.20    103       1   0.5340 0.03609       0.4678        0.610
##   6.33    102       2   0.5236 0.03614       0.4573        0.599
##   6.37    100       1   0.5183 0.03615       0.4521        0.594
##   6.47     99       1   0.5131 0.03617       0.4469        0.589
##   6.80     98       1   0.5079 0.03617       0.4417        0.584
##   6.87     97       1   0.5026 0.03618       0.4365        0.579
##   7.00     96       1   0.4974 0.03618       0.4313        0.574
##   7.47     95       1   0.4921 0.03617       0.4261        0.568
##   7.60     94       1   0.4869 0.03617       0.4209        0.563
##   7.67     93       1   0.4817 0.03615       0.4158        0.558
##   7.77     92       1   0.4764 0.03614       0.4106        0.553
##   7.93     91       1   0.4712 0.03612       0.4055        0.548
##   8.30     90       2   0.4607 0.03607       0.3952        0.537
##   8.67     88       1   0.4555 0.03604       0.3901        0.532
##   8.87     87       1   0.4503 0.03600       0.3850        0.527
##   9.03     86       1   0.4450 0.03596       0.3798        0.521
##   9.33     85       1   0.4398 0.03592       0.3747        0.516
##   9.47     84       1   0.4346 0.03587       0.3696        0.511
##   9.87     83       2   0.4241 0.03576       0.3595        0.500
##  10.03     81       2   0.4136 0.03563       0.3493        0.490
##  10.07     79       1   0.4084 0.03557       0.3443        0.484
##  10.10     78       2   0.3979 0.03542       0.3342        0.474
##  10.27     76       1   0.3927 0.03534       0.3292        0.468
##  10.37     75       1   0.3874 0.03525       0.3242        0.463
##  11.03     74       1   0.3822 0.03516       0.3191        0.458
##  11.17     73       1   0.3770 0.03507       0.3141        0.452
##  11.43     72       1   0.3717 0.03497       0.3091        0.447
##  11.97     71       1   0.3665 0.03487       0.3042        0.442
##  12.20     70       1   0.3613 0.03476       0.2992        0.436
##  12.43     69       1   0.3560 0.03465       0.2942        0.431
##  12.67     68       1   0.3508 0.03453       0.2892        0.425
##  12.87     67       1   0.3455 0.03441       0.2843        0.420
##  12.90     66       2   0.3351 0.03415       0.2744        0.409
##  13.07     64       1   0.3298 0.03402       0.2695        0.404
##  13.10     63       1   0.3246 0.03388       0.2646        0.398
##  13.60     62       1   0.3194 0.03374       0.2596        0.393
##  13.70     61       1   0.3141 0.03359       0.2547        0.387
##  13.93     60       1   0.3089 0.03343       0.2499        0.382
##  15.63     59       1   0.3037 0.03327       0.2450        0.376
##  16.13     58       1   0.2984 0.03311       0.2401        0.371
##  16.20     57       1   0.2932 0.03294       0.2352        0.365
##  16.60     56       1   0.2880 0.03276       0.2304        0.360
##  16.67     55       1   0.2827 0.03258       0.2256        0.354
##  16.87     54       1   0.2775 0.03240       0.2207        0.349
##  17.27     53       1   0.2723 0.03221       0.2159        0.343
##  19.17     52       1   0.2670 0.03201       0.2111        0.338
##  19.60     51       1   0.2618 0.03181       0.2063        0.332
##  19.67     50       1   0.2565 0.03160       0.2015        0.327
##  20.63     48       1   0.2512 0.03139       0.1966        0.321
##  20.80     47       1   0.2459 0.03117       0.1918        0.315
##  21.20     46       1   0.2405 0.03095       0.1869        0.310
##  22.00     45       1   0.2352 0.03072       0.1820        0.304
##  24.87     43       1   0.2297 0.03049       0.1771        0.298
##  24.97     42       1   0.2242 0.03025       0.1721        0.292
##  29.10     40       1   0.2186 0.03001       0.1671        0.286
##  29.27     39       1   0.2130 0.02976       0.1620        0.280
##  30.37     38       1   0.2074 0.02950       0.1570        0.274
##  30.60     37       1   0.2018 0.02923       0.1519        0.268
##  31.13     36       1   0.1962 0.02895       0.1469        0.262
##  31.87     35       1   0.1906 0.02866       0.1419        0.256
##  32.00     34       3   0.1738 0.02773       0.1271        0.238
##  32.83     31       1   0.1682 0.02739       0.1222        0.231
##  35.83     29       1   0.1624 0.02706       0.1171        0.225
##  35.93     28       1   0.1566 0.02670       0.1121        0.219
##  36.73     27       1   0.1508 0.02634       0.1071        0.212
##  37.97     25       1   0.1447 0.02597       0.1018        0.206
##  38.33     24       1   0.1387 0.02557       0.0966        0.199
##  39.27     23       1   0.1327 0.02516       0.0915        0.192
##  40.27     22       1   0.1267 0.02473       0.0864        0.186
##  44.20     20       1   0.1203 0.02429       0.0810        0.179
##  45.50     19       1   0.1140 0.02382       0.0757        0.172
##  46.30     18       1   0.1077 0.02333       0.0704        0.165
##  46.80     17       1   0.1013 0.02280       0.0652        0.157
##  57.73     14       1   0.0941 0.02229       0.0591        0.150
##  58.63     13       2   0.0796 0.02108       0.0474        0.134
##  58.83     11       1   0.0724 0.02037       0.0417        0.126
##  82.53      4       1   0.0543 0.02188       0.0246        0.120
##  93.60      2       1   0.0271 0.02209       0.0055        0.134
##  97.63      1       1   0.0000     NaN           NA           NA

`

Including Plots

library(ranger)
## Warning: package 'ranger' was built under R version 3.6.3
library(ggplot2)
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(ggfortify)
## Warning: package 'ggfortify' was built under R version 3.6.3

Kaplan – Meier plot:

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

km_trt_fit <- survfit(Surv(SurviePosOpMOIS, Deces) ~ localisation, data=data1)
print(km_trt_fit)
## Call: survfit(formula = Surv(SurviePosOpMOIS, Deces) ~ localisation, 
##     data = data1)
## 
##                 n events median 0.95LCL 0.95UCL
## localisation=1 47     42   6.20    3.97    12.7
## localisation=2 96     89   8.12    4.97    11.2
## localisation=3 48     45   6.63    5.43    13.7
autoplot(km_trt_fit)

#table 1: general chracteristics of participants

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

mytable(data1)
## 
##             Descriptive Statistics           
## ---------------------------------------------- 
##                               N      Total    
## ---------------------------------------------- 
##  localisation               191               
##    - 1                            47  (24.6%) 
##    - 2                            96  (50.3%) 
##    - 3                            48  (25.1%) 
##  sex_code                   191               
##    - 0                            89  (46.6%) 
##    - 1                           102  (53.4%) 
##  age_code                   191               
##    - 0                            78  (40.8%) 
##    - 1                           113  (59.2%) 
##  Tabagisme                  191               
##    - 0                           115  (60.2%) 
##    - 1                            76  (39.8%) 
##  ASIAPreOp                  191               
##    - 0                              1  (0.5%) 
##    - 1                            23  (12.0%) 
##    - 2                            86  (45.0%) 
##    - 3                            81  (42.4%) 
##  ASIA_PO                    191               
##    - 0                              2  (1.0%) 
##    - 1                              1  (0.5%) 
##    - 2                              2  (1.0%) 
##    - 3                            68  (35.6%) 
##    - 4                           118  (61.8%) 
##  Tokuhashi_cat              191               
##    - 1                            76  (39.8%) 
##    - 2                            71  (37.2%) 
##    - 3                            44  (23.0%) 
##  AmbulationPreOP            191               
##    - 0                            42  (22.0%) 
##    - 1                           103  (53.9%) 
##    - 2                            46  (24.1%) 
##  AmbulationPO               191               
##    - 0                             13  (6.8%) 
##    - 1                            99  (51.8%) 
##    - 2                            79  (41.4%) 
##  RoRxPO                     190               
##    - 0                            50  (26.3%) 
##    - 1                           140  (73.7%) 
##  SystemicTherapyPO          191               
##    - 0                            93  (48.7%) 
##    - 1                            98  (51.3%) 
##  Improvement.of.ambulation  191               
##    - 0                           111  (58.1%) 
##    - 1                            50  (26.2%) 
##    - 2                            30  (15.7%) 
##  SurviePosOpMOIS            191  15.7 ± 19.5 
##  Deces                      191               
##    - 0                             15  (7.9%) 
##    - 1                           176  (92.1%) 
## ----------------------------------------------

##Table 2

attach(data1)

data1a <- subset(data1, data1$localisation==1)
data1b <- subset(data1, data1$localisation==2)
data1c <- subset(data1, data1$localisation==3)

Cervical spine surgery No. (1a)

age_sur <- survfit(Surv(SurviePosOpMOIS, Deces) ~ age_code, data=data1a)

sex_code <- survfit(Surv(SurviePosOpMOIS, Deces) ~ sex_code, data=data1a)
Tabagisme <- survfit(Surv(SurviePosOpMOIS, Deces) ~ Tabagisme, data=data1a)

AmbulationPreOP <- survfit(Surv(SurviePosOpMOIS, Deces) ~ AmbulationPreOP, data=data1a)
ASIAPreOp <- survfit(Surv(SurviePosOpMOIS, Deces) ~ ASIAPreOp, data=data1a)
AmbulationPO <- survfit(Surv(SurviePosOpMOIS, Deces) ~ AmbulationPO, data=data1a)
ASIA_PO <- survfit(Surv(SurviePosOpMOIS, Deces) ~ ASIA_PO, data=data1a)
Tokuhashi_cat <- survfit(Surv(SurviePosOpMOIS, Deces) ~ Tokuhashi_cat, data=data1a)
RoRxPO <- survfit(Surv(SurviePosOpMOIS, Deces) ~ RoRxPO, data=data1a)
SystemicTherapyPO <- survfit(Surv(SurviePosOpMOIS, Deces) ~ SystemicTherapyPO, data=data1a)

table 2 (ressult): cacs ket qua bang 2

print(age_sur)
## Call: survfit(formula = Surv(SurviePosOpMOIS, Deces) ~ age_code, data = data1a)
## 
##             n events median 0.95LCL 0.95UCL
## age_code=0 22     21   4.07    2.30    10.3
## age_code=1 25     21  10.07    4.73    40.3
print(sex_code)
## Call: survfit(formula = Surv(SurviePosOpMOIS, Deces) ~ sex_code, data = data1a)
## 
##             n events median 0.95LCL 0.95UCL
## sex_code=0 23     20   6.33    4.73    38.3
## sex_code=1 24     22   3.72    2.40    10.4
print(Tabagisme)
## Call: survfit(formula = Surv(SurviePosOpMOIS, Deces) ~ Tabagisme, data = data1a)
## 
##              n events median 0.95LCL 0.95UCL
## Tabagisme=0 23     19  12.87    6.33    58.6
## Tabagisme=1 24     23   3.18    2.27     5.7
print(AmbulationPreOP)
## Call: survfit(formula = Surv(SurviePosOpMOIS, Deces) ~ AmbulationPreOP, 
##     data = data1a)
## 
##                    n events median 0.95LCL 0.95UCL
## AmbulationPreOP=0  7      6   3.97    2.27      NA
## AmbulationPreOP=1 29     25  10.27    4.50    35.8
## AmbulationPreOP=2 11     11   6.20    0.87      NA
print(ASIAPreOp)
## Call: survfit(formula = Surv(SurviePosOpMOIS, Deces) ~ ASIAPreOp, data = data1a)
## 
##              n events median 0.95LCL 0.95UCL
## ASIAPreOp=1  2      1   4.17    4.17      NA
## ASIAPreOp=2 22     20   3.88    2.30    12.9
## ASIAPreOp=3 23     21   6.33    4.50    35.8
print(AmbulationPO)
## Call: survfit(formula = Surv(SurviePosOpMOIS, Deces) ~ AmbulationPO, 
##     data = data1a)
## 
##                 n events median 0.95LCL 0.95UCL
## AmbulationPO=0  5      5   0.90    0.87      NA
## AmbulationPO=1 30     26  10.32    6.33    35.8
## AmbulationPO=2 12     11   4.23    2.30      NA
print(ASIA_PO)
## Call: survfit(formula = Surv(SurviePosOpMOIS, Deces) ~ ASIA_PO, data = data1a)
## 
##            n events median 0.95LCL 0.95UCL
## ASIA_PO=0  1      1   0.03      NA      NA
## ASIA_PO=3 14     12   5.19     2.3      NA
## ASIA_PO=4 32     29   6.33     4.3    16.9
print(Tokuhashi_cat)
## Call: survfit(formula = Surv(SurviePosOpMOIS, Deces) ~ Tokuhashi_cat, 
##     data = data1a)
## 
##                  n events median 0.95LCL 0.95UCL
## Tokuhashi_cat=1 17     17   2.90    2.30     6.2
## Tokuhashi_cat=2 17     13   6.33    4.17      NA
## Tokuhashi_cat=3 13     12  16.87    9.33      NA
print(RoRxPO)
## Call: survfit(formula = Surv(SurviePosOpMOIS, Deces) ~ RoRxPO, data = data1a)
## 
##           n events median 0.95LCL 0.95UCL
## RoRxPO=0 16     16   2.01    0.87    9.33
## RoRxPO=1 31     26  10.27    6.20   35.83
print(SystemicTherapyPO)
## Call: survfit(formula = Surv(SurviePosOpMOIS, Deces) ~ SystemicTherapyPO, 
##     data = data1a)
## 
##                      n events median 0.95LCL 0.95UCL
## SystemicTherapyPO=0 21     21    2.9    1.73     8.3
## SystemicTherapyPO=1 26     21   11.5    6.20    36.7

#Univariate cox-model (gia tri P cho moi bang tren)

library(survival)
#model 1: sex
cox1 = coxph(Surv(data1a$SurviePosOpMOIS, data1a$Deces==1) ~ data1a$sex_code)
summary(cox1)
## Call:
## coxph(formula = Surv(data1a$SurviePosOpMOIS, data1a$Deces == 
##     1) ~ data1a$sex_code)
## 
##   n= 47, number of events= 42 
## 
##                    coef exp(coef) se(coef)     z Pr(>|z|)
## data1a$sex_code1 0.2882    1.3341   0.3112 0.926    0.354
## 
##                  exp(coef) exp(-coef) lower .95 upper .95
## data1a$sex_code1     1.334     0.7496     0.725     2.455
## 
## Concordance= 0.57  (se = 0.043 )
## Likelihood ratio test= 0.86  on 1 df,   p=0.4
## Wald test            = 0.86  on 1 df,   p=0.4
## Score (logrank) test = 0.86  on 1 df,   p=0.4
#model 2: age group
cox2 = coxph(Surv(data1a$SurviePosOpMOIS, data1a$Deces==1) ~ data1a$age_code)
summary(cox2)
## Call:
## coxph(formula = Surv(data1a$SurviePosOpMOIS, data1a$Deces == 
##     1) ~ data1a$age_code)
## 
##   n= 47, number of events= 42 
## 
##                     coef exp(coef) se(coef)      z Pr(>|z|)  
## data1a$age_code1 -0.6879    0.5026   0.3261 -2.109   0.0349 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##                  exp(coef) exp(-coef) lower .95 upper .95
## data1a$age_code1    0.5026      1.989    0.2653    0.9525
## 
## Concordance= 0.575  (se = 0.043 )
## Likelihood ratio test= 4.45  on 1 df,   p=0.03
## Wald test            = 4.45  on 1 df,   p=0.03
## Score (logrank) test = 4.61  on 1 df,   p=0.03
#model 3: tobacco use 
cox3 = coxph(Surv(data1a$SurviePosOpMOIS, data1a$Deces==1) ~ data1a$Tabagisme)
summary(cox3)
## Call:
## coxph(formula = Surv(data1a$SurviePosOpMOIS, data1a$Deces == 
##     1) ~ data1a$Tabagisme)
## 
##   n= 47, number of events= 42 
## 
##                     coef exp(coef) se(coef)     z Pr(>|z|)   
## data1a$Tabagisme1 1.0830    2.9535   0.3321 3.261  0.00111 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##                   exp(coef) exp(-coef) lower .95 upper .95
## data1a$Tabagisme1     2.953     0.3386      1.54     5.663
## 
## Concordance= 0.649  (se = 0.035 )
## Likelihood ratio test= 10.75  on 1 df,   p=0.001
## Wald test            = 10.63  on 1 df,   p=0.001
## Score (logrank) test = 11.51  on 1 df,   p=7e-04
#model 5: Pre. ASIA score
cox5 = coxph(Surv(data1a$SurviePosOpMOIS, data1a$Deces==1) ~ data1a$ASIAPreOp)
summary(cox5)
## Call:
## coxph(formula = Surv(data1a$SurviePosOpMOIS, data1a$Deces == 
##     1) ~ data1a$ASIAPreOp)
## 
##   n= 47, number of events= 42 
## 
##                      coef exp(coef) se(coef)      z Pr(>|z|)
## data1a$ASIAPreOp1 -1.2028    0.3004   1.0395 -1.157    0.247
## data1a$ASIAPreOp2  0.0872    1.0911   0.3219  0.271    0.786
## data1a$ASIAPreOp3      NA        NA   0.0000     NA       NA
## 
##                   exp(coef) exp(-coef) lower .95 upper .95
## data1a$ASIAPreOp1    0.3003     3.3295   0.03916     2.304
## data1a$ASIAPreOp2    1.0911     0.9165   0.58058     2.051
## data1a$ASIAPreOp3        NA         NA        NA        NA
## 
## Concordance= 0.563  (se = 0.046 )
## Likelihood ratio test= 2.32  on 2 df,   p=0.3
## Wald test            = 1.58  on 2 df,   p=0.5
## Score (logrank) test = 1.78  on 2 df,   p=0.4
#model 6: Post. ASIA score
cox6 = coxph(Surv(data1a$SurviePosOpMOIS, data1a$Deces==1) ~ data1a$ASIA_PO)
summary(cox6)
## Call:
## coxph(formula = Surv(data1a$SurviePosOpMOIS, data1a$Deces == 
##     1) ~ data1a$ASIA_PO)
## 
##   n= 47, number of events= 42 
## 
##                       coef  exp(coef)   se(coef)      z Pr(>|z|)    
## data1a$ASIA_PO1         NA         NA  0.000e+00     NA       NA    
## data1a$ASIA_PO2         NA         NA  0.000e+00     NA       NA    
## data1a$ASIA_PO3 -4.699e+01  3.927e-21  3.483e-01 -134.9   <2e-16 ***
## data1a$ASIA_PO4         NA         NA  0.000e+00     NA       NA    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##                 exp(coef) exp(-coef) lower .95 upper .95
## data1a$ASIA_PO1        NA         NA        NA        NA
## data1a$ASIA_PO2        NA         NA        NA        NA
## data1a$ASIA_PO3 3.927e-21  2.547e+20 1.984e-21 7.772e-21
## data1a$ASIA_PO4        NA         NA        NA        NA
## 
## Concordance= 0.553  (se = 0.045 )
## Likelihood ratio test= 7.7  on 1 df,   p=0.006
## Wald test            = 18193  on 1 df,   p=<2e-16
## Score (logrank) test = 46  on 1 df,   p=1e-11
#model 7: Revised Tokuhashi score

cox7 = coxph(Surv(data1a$SurviePosOpMOIS, data1a$Deces==1) ~ data1a$Tokuhashi_cat)
summary(cox7)
## Call:
## coxph(formula = Surv(data1a$SurviePosOpMOIS, data1a$Deces == 
##     1) ~ data1a$Tokuhashi_cat)
## 
##   n= 47, number of events= 42 
## 
##                          coef exp(coef) se(coef)      z Pr(>|z|)   
## data1a$Tokuhashi_cat2 -1.3271    0.2652   0.4152 -3.196  0.00139 **
## data1a$Tokuhashi_cat3 -1.3746    0.2529   0.4291 -3.204  0.00136 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##                       exp(coef) exp(-coef) lower .95 upper .95
## data1a$Tokuhashi_cat2    0.2652      3.770    0.1175    0.5985
## data1a$Tokuhashi_cat3    0.2529      3.954    0.1091    0.5865
## 
## Concordance= 0.644  (se = 0.042 )
## Likelihood ratio test= 12.93  on 2 df,   p=0.002
## Wald test            = 13.26  on 2 df,   p=0.001
## Score (logrank) test = 15.03  on 2 df,   p=5e-04
#model 8: Pre. ambulatory status

cox8 = coxph(Surv(data1a$SurviePosOpMOIS, data1a$Deces==1) ~ data1a$AmbulationPreOP)
summary(cox8)
## Call:
## coxph(formula = Surv(data1a$SurviePosOpMOIS, data1a$Deces == 
##     1) ~ data1a$AmbulationPreOP)
## 
##   n= 47, number of events= 42 
## 
##                            coef exp(coef) se(coef)      z Pr(>|z|)
## data1a$AmbulationPreOP1 -0.3430    0.7096   0.4654 -0.737    0.461
## data1a$AmbulationPreOP2  0.1996    1.2209   0.5148  0.388    0.698
## 
##                         exp(coef) exp(-coef) lower .95 upper .95
## data1a$AmbulationPreOP1    0.7096     1.4092    0.2850     1.767
## data1a$AmbulationPreOP2    1.2209     0.8191    0.4451     3.349
## 
## Concordance= 0.576  (se = 0.045 )
## Likelihood ratio test= 2.21  on 2 df,   p=0.3
## Wald test            = 2.32  on 2 df,   p=0.3
## Score (logrank) test = 2.37  on 2 df,   p=0.3
#model 9: Post. ambulatory status
cox9 = coxph(Surv(data1a$SurviePosOpMOIS, data1a$Deces==1) ~ data1a$AmbulationPO)
summary(cox9)
## Call:
## coxph(formula = Surv(data1a$SurviePosOpMOIS, data1a$Deces == 
##     1) ~ data1a$AmbulationPO)
## 
##   n= 47, number of events= 42 
## 
##                          coef exp(coef) se(coef)      z Pr(>|z|)    
## data1a$AmbulationPO1 -2.78118   0.06197  0.64585 -4.306 1.66e-05 ***
## data1a$AmbulationPO2 -2.35122   0.09525  0.67712 -3.472 0.000516 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##                      exp(coef) exp(-coef) lower .95 upper .95
## data1a$AmbulationPO1   0.06197      16.14   0.01747    0.2197
## data1a$AmbulationPO2   0.09525      10.50   0.02526    0.3591
## 
## Concordance= 0.653  (se = 0.042 )
## Likelihood ratio test= 15.1  on 2 df,   p=5e-04
## Wald test            = 18.63  on 2 df,   p=9e-05
## Score (logrank) test = 30.38  on 2 df,   p=3e-07
#model 10: Post. Radiotherapy

cox10 = coxph(Surv(data1a$SurviePosOpMOIS, data1a$Deces==1) ~ data1a$RoRxPO)
summary(cox10)
## Call:
## coxph(formula = Surv(data1a$SurviePosOpMOIS, data1a$Deces == 
##     1) ~ data1a$RoRxPO)
## 
##   n= 47, number of events= 42 
## 
##                   coef exp(coef) se(coef)      z Pr(>|z|)    
## data1a$RoRxPO1 -1.1448    0.3183   0.3315 -3.453 0.000554 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##                exp(coef) exp(-coef) lower .95 upper .95
## data1a$RoRxPO1    0.3183      3.142    0.1662    0.6096
## 
## Concordance= 0.649  (se = 0.035 )
## Likelihood ratio test= 10.72  on 1 df,   p=0.001
## Wald test            = 11.92  on 1 df,   p=6e-04
## Score (logrank) test = 13.14  on 1 df,   p=3e-04
#model 11: Post. Chemotherapy

cox11 = coxph(Surv(data1a$SurviePosOpMOIS, data1a$Deces==1) ~ data1a$SystemicTherapyPO)
summary(cox11)
## Call:
## coxph(formula = Surv(data1a$SurviePosOpMOIS, data1a$Deces == 
##     1) ~ data1a$SystemicTherapyPO)
## 
##   n= 47, number of events= 42 
## 
##                              coef exp(coef) se(coef)      z Pr(>|z|)   
## data1a$SystemicTherapyPO1 -0.9122    0.4016   0.3150 -2.896  0.00378 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##                           exp(coef) exp(-coef) lower .95 upper .95
## data1a$SystemicTherapyPO1    0.4016       2.49    0.2166    0.7446
## 
## Concordance= 0.636  (se = 0.037 )
## Likelihood ratio test= 8.14  on 1 df,   p=0.004
## Wald test            = 8.39  on 1 df,   p=0.004
## Score (logrank) test = 8.92  on 1 df,   p=0.003

1.b: Thoracic spine surgery. (1b)

age_sur1 <- survfit(Surv(SurviePosOpMOIS, Deces) ~ age_code, data=data1b)
sex_code1 <- survfit(Surv(SurviePosOpMOIS, Deces) ~ sex_code, data=data1b)
Tabagisme1 <- survfit(Surv(SurviePosOpMOIS, Deces) ~ Tabagisme, data=data1b)

AmbulationPreOP1 <- survfit(Surv(SurviePosOpMOIS, Deces) ~ AmbulationPreOP, data=data1b)
ASIAPreOp1 <- survfit(Surv(SurviePosOpMOIS, Deces) ~ ASIAPreOp, data=data1b)
AmbulationPO1 <- survfit(Surv(SurviePosOpMOIS, Deces) ~ AmbulationPO, data=data1b)
ASIA_PO1 <- survfit(Surv(SurviePosOpMOIS, Deces) ~ ASIA_PO, data=data1b)
Tokuhashi_cat1 <- survfit(Surv(SurviePosOpMOIS, Deces) ~ Tokuhashi_cat, data=data1b)
RoRxPO1 <- survfit(Surv(SurviePosOpMOIS, Deces) ~ RoRxPO, data=data1b)
SystemicTherapyPO1 <- survfit(Surv(SurviePosOpMOIS, Deces) ~ SystemicTherapyPO, data=data1b)

table 2 (ressult): cacs ket qua bang 2b

print(age_sur1)
## Call: survfit(formula = Surv(SurviePosOpMOIS, Deces) ~ age_code, data = data1b)
## 
##             n events median 0.95LCL 0.95UCL
## age_code=0 35     31   7.93    4.73    19.2
## age_code=1 61     58   8.30    4.37    12.4
print(sex_code1)
## Call: survfit(formula = Surv(SurviePosOpMOIS, Deces) ~ sex_code, data = data1b)
## 
##             n events median 0.95LCL 0.95UCL
## sex_code=0 43     39  10.03    5.80    21.2
## sex_code=1 53     50   6.37    4.33    10.1
print(Tabagisme1)
## Call: survfit(formula = Surv(SurviePosOpMOIS, Deces) ~ Tabagisme, data = data1b)
## 
##              n events median 0.95LCL 0.95UCL
## Tabagisme=0 57     51   10.0    5.67    12.4
## Tabagisme=1 39     38    5.8    3.83    12.9
print(AmbulationPreOP1)
## Call: survfit(formula = Surv(SurviePosOpMOIS, Deces) ~ AmbulationPreOP, 
##     data = data1b)
## 
##                    n events median 0.95LCL 0.95UCL
## AmbulationPreOP=0 21     20   4.33    3.37    12.9
## AmbulationPreOP=1 52     47  11.23    7.77    20.8
## AmbulationPreOP=2 23     22   7.00    2.67    12.0
print(ASIAPreOp1)
## Call: survfit(formula = Surv(SurviePosOpMOIS, Deces) ~ ASIAPreOp, data = data1b)
## 
##              n events median 0.95LCL 0.95UCL
## ASIAPreOp=0  1      1   0.83      NA      NA
## ASIAPreOp=1 13     13   4.33    2.20      NA
## ASIAPreOp=2 40     38   7.65    4.73    12.0
## ASIAPreOp=3 42     37  10.56    7.60    19.7
print(AmbulationPO1)
## Call: survfit(formula = Surv(SurviePosOpMOIS, Deces) ~ AmbulationPO, 
##     data = data1b)
## 
##                 n events median 0.95LCL 0.95UCL
## AmbulationPO=0  5      5   0.83    0.43      NA
## AmbulationPO=1 51     45  11.43    8.30    20.8
## AmbulationPO=2 40     39   4.47    3.70    10.1
print(ASIA_PO1)
## Call: survfit(formula = Surv(SurviePosOpMOIS, Deces) ~ ASIA_PO, data = data1b)
## 
##            n events median 0.95LCL 0.95UCL
## ASIA_PO=0  1      1   0.07      NA      NA
## ASIA_PO=1  1      1   0.83      NA      NA
## ASIA_PO=2  1      1   7.93      NA      NA
## ASIA_PO=3 35     34   4.57    3.63    9.87
## ASIA_PO=4 58     52  10.56    7.77   17.27
print(Tokuhashi_cat1)
## Call: survfit(formula = Surv(SurviePosOpMOIS, Deces) ~ Tokuhashi_cat, 
##     data = data1b)
## 
##                  n events median 0.95LCL 0.95UCL
## Tokuhashi_cat=1 42     42   3.98    3.37    5.67
## Tokuhashi_cat=2 38     33  12.66   10.03   31.13
## Tokuhashi_cat=3 16     14  25.15    9.87      NA
print(RoRxPO1)
## Call: survfit(formula = Surv(SurviePosOpMOIS, Deces) ~ RoRxPO, data = data1b)
## 
##    1 observation deleted due to missingness 
##           n events median 0.95LCL 0.95UCL
## RoRxPO=0 25     24    4.9     2.7    9.03
## RoRxPO=1 70     64   10.0     7.0   17.27
print(SystemicTherapyPO1)
## Call: survfit(formula = Surv(SurviePosOpMOIS, Deces) ~ SystemicTherapyPO, 
##     data = data1b)
## 
##                      n events median 0.95LCL 0.95UCL
## SystemicTherapyPO=0 47     45    3.7     2.2    4.73
## SystemicTherapyPO=1 49     44   13.1    11.4   30.37

#Univariate cox-model (gia tri P cho moi bang tren)

library(survival)
#model 1: sex
cox1b = coxph(Surv(data1b$SurviePosOpMOIS, data1b$Deces==1) ~ data1b$sex_code)
summary(cox1b)
## Call:
## coxph(formula = Surv(data1b$SurviePosOpMOIS, data1b$Deces == 
##     1) ~ data1b$sex_code)
## 
##   n= 96, number of events= 89 
## 
##                    coef exp(coef) se(coef)     z Pr(>|z|)
## data1b$sex_code1 0.2912    1.3380   0.2159 1.349    0.177
## 
##                  exp(coef) exp(-coef) lower .95 upper .95
## data1b$sex_code1     1.338     0.7474    0.8764     2.043
## 
## Concordance= 0.55  (se = 0.03 )
## Likelihood ratio test= 1.83  on 1 df,   p=0.2
## Wald test            = 1.82  on 1 df,   p=0.2
## Score (logrank) test = 1.83  on 1 df,   p=0.2
#model 2: age group
cox2b = coxph(Surv(data1b$SurviePosOpMOIS, data1b$Deces==1) ~ data1b$age_code)
summary(cox2b)
## Call:
## coxph(formula = Surv(data1b$SurviePosOpMOIS, data1b$Deces == 
##     1) ~ data1b$age_code)
## 
##   n= 96, number of events= 89 
## 
##                    coef exp(coef) se(coef)     z Pr(>|z|)
## data1b$age_code1 0.1434    1.1541   0.2252 0.637    0.524
## 
##                  exp(coef) exp(-coef) lower .95 upper .95
## data1b$age_code1     1.154     0.8664    0.7423     1.794
## 
## Concordance= 0.513  (se = 0.029 )
## Likelihood ratio test= 0.41  on 1 df,   p=0.5
## Wald test            = 0.41  on 1 df,   p=0.5
## Score (logrank) test = 0.41  on 1 df,   p=0.5
#model 3: tobacco use 
cox3b = coxph(Surv(data1b$SurviePosOpMOIS, data1b$Deces==1) ~ data1b$Tabagisme)
summary(cox3b)
## Call:
## coxph(formula = Surv(data1b$SurviePosOpMOIS, data1b$Deces == 
##     1) ~ data1b$Tabagisme)
## 
##   n= 96, number of events= 89 
## 
##                     coef exp(coef) se(coef)     z Pr(>|z|)
## data1b$Tabagisme1 0.1453    1.1564   0.2190 0.664    0.507
## 
##                   exp(coef) exp(-coef) lower .95 upper .95
## data1b$Tabagisme1     1.156     0.8648    0.7529     1.776
## 
## Concordance= 0.539  (se = 0.03 )
## Likelihood ratio test= 0.44  on 1 df,   p=0.5
## Wald test            = 0.44  on 1 df,   p=0.5
## Score (logrank) test = 0.44  on 1 df,   p=0.5
#model 5: Pre. ASIA score
cox5b = coxph(Surv(data1b$SurviePosOpMOIS, data1b$Deces==1) ~ data1b$ASIAPreOp)
summary(cox5b)
## Call:
## coxph(formula = Surv(data1b$SurviePosOpMOIS, data1b$Deces == 
##     1) ~ data1b$ASIAPreOp)
## 
##   n= 96, number of events= 89 
## 
##                       coef exp(coef) se(coef)      z Pr(>|z|)    
## data1b$ASIAPreOp1 -3.33260   0.03570  1.24794 -2.670 0.007575 ** 
## data1b$ASIAPreOp2 -3.83735   0.02155  1.23128 -3.117 0.001830 ** 
## data1b$ASIAPreOp3 -4.08717   0.01679  1.23313 -3.314 0.000918 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##                   exp(coef) exp(-coef) lower .95 upper .95
## data1b$ASIAPreOp1   0.03570      28.01  0.003093    0.4120
## data1b$ASIAPreOp2   0.02155      46.40  0.001929    0.2407
## data1b$ASIAPreOp3   0.01679      59.57  0.001497    0.1882
## 
## Concordance= 0.566  (se = 0.032 )
## Likelihood ratio test= 10.17  on 3 df,   p=0.02
## Wald test            = 14.99  on 3 df,   p=0.002
## Score (logrank) test = 35.39  on 3 df,   p=1e-07
#model 6: Post. ASIA score
cox6b = coxph(Surv(data1b$SurviePosOpMOIS, data1b$Deces==1) ~ data1b$ASIA_PO)
## Warning in fitter(X, Y, strats, offset, init, control, weights = weights, :
## Loglik converged before variable 1,2,3,4 ; coefficient may be infinite.
summary(cox6b)
## Call:
## coxph(formula = Surv(data1b$SurviePosOpMOIS, data1b$Deces == 
##     1) ~ data1b$ASIA_PO)
## 
##   n= 96, number of events= 89 
## 
##                       coef  exp(coef)   se(coef)      z Pr(>|z|)
## data1b$ASIA_PO1 -1.704e+01  3.969e-08  3.543e+03 -0.005    0.996
## data1b$ASIA_PO2 -2.120e+01  6.197e-10  3.543e+03 -0.006    0.995
## data1b$ASIA_PO3 -2.130e+01  5.604e-10  3.543e+03 -0.006    0.995
## data1b$ASIA_PO4 -2.180e+01  3.403e-10  3.543e+03 -0.006    0.995
## 
##                 exp(coef) exp(-coef) lower .95 upper .95
## data1b$ASIA_PO1 3.969e-08  2.520e+07         0       Inf
## data1b$ASIA_PO2 6.197e-10  1.614e+09         0       Inf
## data1b$ASIA_PO3 5.604e-10  1.784e+09         0       Inf
## data1b$ASIA_PO4 3.403e-10  2.939e+09         0       Inf
## 
## Concordance= 0.581  (se = 0.03 )
## Likelihood ratio test= 20.38  on 4 df,   p=4e-04
## Wald test            = 15.25  on 4 df,   p=0.004
## Score (logrank) test = 130.7  on 4 df,   p=<2e-16
#model 7: Revised Tokuhashi score

cox7b = coxph(Surv(data1b$SurviePosOpMOIS, data1b$Deces==1) ~ data1b$Tokuhashi_cat)
summary(cox7b)
## Call:
## coxph(formula = Surv(data1b$SurviePosOpMOIS, data1b$Deces == 
##     1) ~ data1b$Tokuhashi_cat)
## 
##   n= 96, number of events= 89 
## 
##                          coef exp(coef) se(coef)      z Pr(>|z|)    
## data1b$Tokuhashi_cat2 -1.3753    0.2528   0.2627 -5.235 1.65e-07 ***
## data1b$Tokuhashi_cat3 -1.5740    0.2072   0.3361 -4.683 2.83e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##                       exp(coef) exp(-coef) lower .95 upper .95
## data1b$Tokuhashi_cat2    0.2528      3.956    0.1510    0.4230
## data1b$Tokuhashi_cat3    0.2072      4.826    0.1072    0.4004
## 
## Concordance= 0.666  (se = 0.025 )
## Likelihood ratio test= 33.98  on 2 df,   p=4e-08
## Wald test            = 34.49  on 2 df,   p=3e-08
## Score (logrank) test = 38.74  on 2 df,   p=4e-09
#model 8: Pre. ambulatory status

cox8b = coxph(Surv(data1b$SurviePosOpMOIS, data1b$Deces==1) ~ data1b$AmbulationPreOP)
summary(cox8b)
## Call:
## coxph(formula = Surv(data1b$SurviePosOpMOIS, data1b$Deces == 
##     1) ~ data1b$AmbulationPreOP)
## 
##   n= 96, number of events= 89 
## 
##                             coef exp(coef) se(coef)      z Pr(>|z|)  
## data1b$AmbulationPreOP1 -0.67884   0.50721  0.27352 -2.482   0.0131 *
## data1b$AmbulationPreOP2 -0.06122   0.94062  0.31122 -0.197   0.8441  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##                         exp(coef) exp(-coef) lower .95 upper .95
## data1b$AmbulationPreOP1    0.5072      1.972    0.2967     0.867
## data1b$AmbulationPreOP2    0.9406      1.063    0.5111     1.731
## 
## Concordance= 0.586  (se = 0.03 )
## Likelihood ratio test= 8.42  on 2 df,   p=0.01
## Wald test            = 8.58  on 2 df,   p=0.01
## Score (logrank) test = 8.85  on 2 df,   p=0.01
#model 9: Post. ambulatory status
cox9b = coxph(Surv(data1b$SurviePosOpMOIS, data1b$Deces==1) ~ data1b$AmbulationPO)
summary(cox9b)
## Call:
## coxph(formula = Surv(data1b$SurviePosOpMOIS, data1b$Deces == 
##     1) ~ data1b$AmbulationPO)
## 
##   n= 96, number of events= 89 
## 
##                         coef exp(coef) se(coef)      z Pr(>|z|)    
## data1b$AmbulationPO1 -2.0268    0.1318   0.4891 -4.144 3.42e-05 ***
## data1b$AmbulationPO2 -1.3677    0.2547   0.4843 -2.824  0.00474 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##                      exp(coef) exp(-coef) lower .95 upper .95
## data1b$AmbulationPO1    0.1318      7.590   0.05051    0.3437
## data1b$AmbulationPO2    0.2547      3.926   0.09858    0.6580
## 
## Concordance= 0.613  (se = 0.03 )
## Likelihood ratio test= 16.88  on 2 df,   p=2e-04
## Wald test            = 20.87  on 2 df,   p=3e-05
## Score (logrank) test = 25.07  on 2 df,   p=4e-06
#model 10: Post. Radiotherapy

cox10b = coxph(Surv(data1b$SurviePosOpMOIS, data1b$Deces==1) ~ data1b$RoRxPO)
summary(cox10b)
## Call:
## coxph(formula = Surv(data1b$SurviePosOpMOIS, data1b$Deces == 
##     1) ~ data1b$RoRxPO)
## 
##   n= 95, number of events= 88 
##    (1 observation deleted due to missingness)
## 
##                   coef exp(coef) se(coef)      z Pr(>|z|)  
## data1b$RoRxPO1 -0.4922    0.6113   0.2433 -2.023   0.0431 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##                exp(coef) exp(-coef) lower .95 upper .95
## data1b$RoRxPO1    0.6113      1.636    0.3794    0.9848
## 
## Concordance= 0.564  (se = 0.026 )
## Likelihood ratio test= 3.79  on 1 df,   p=0.05
## Wald test            = 4.09  on 1 df,   p=0.04
## Score (logrank) test = 4.17  on 1 df,   p=0.04
#model 11: Post. Chemotherapy

cox11b = coxph(Surv(data1b$SurviePosOpMOIS, data1b$Deces==1) ~ data1b$SystemicTherapyPO)
summary(cox11b)
## Call:
## coxph(formula = Surv(data1b$SurviePosOpMOIS, data1b$Deces == 
##     1) ~ data1b$SystemicTherapyPO)
## 
##   n= 96, number of events= 89 
## 
##                              coef exp(coef) se(coef)      z Pr(>|z|)    
## data1b$SystemicTherapyPO1 -0.9443    0.3890   0.2194 -4.304 1.68e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##                           exp(coef) exp(-coef) lower .95 upper .95
## data1b$SystemicTherapyPO1     0.389      2.571     0.253    0.5979
## 
## Concordance= 0.668  (se = 0.022 )
## Likelihood ratio test= 17.95  on 1 df,   p=2e-05
## Wald test            = 18.52  on 1 df,   p=2e-05
## Score (logrank) test = 19.72  on 1 df,   p=9e-06

1.c: Lumbar spine surgery. (1c)

age_sur2 <- survfit(Surv(SurviePosOpMOIS, Deces) ~ age_code, data=data1c)
sex_code2 <- survfit(Surv(SurviePosOpMOIS, Deces) ~ sex_code, data=data1c)
Tabagisme2 <- survfit(Surv(SurviePosOpMOIS, Deces) ~ Tabagisme, data=data1c)

AmbulationPreOP2 <- survfit(Surv(SurviePosOpMOIS, Deces) ~ AmbulationPreOP, data=data1c)
ASIAPreOp2 <- survfit(Surv(SurviePosOpMOIS, Deces) ~ ASIAPreOp, data=data1c)
AmbulationPO2 <- survfit(Surv(SurviePosOpMOIS, Deces) ~ AmbulationPO, data=data1c)
ASIA_PO2 <- survfit(Surv(SurviePosOpMOIS, Deces) ~ ASIA_PO, data=data1c)
Tokuhashi_cat2 <- survfit(Surv(SurviePosOpMOIS, Deces) ~ Tokuhashi_cat, data=data1c)
RoRxPO2 <- survfit(Surv(SurviePosOpMOIS, Deces) ~ RoRxPO, data=data1c)
SystemicTherapyPO2 <- survfit(Surv(SurviePosOpMOIS, Deces) ~ SystemicTherapyPO, data=data1c)

table 2 (ressult): cacs ket qua bang 2b

print(age_sur2)
## Call: survfit(formula = Surv(SurviePosOpMOIS, Deces) ~ age_code, data = data1c)
## 
##             n events median 0.95LCL 0.95UCL
## age_code=0 21     20   6.00    4.00    30.6
## age_code=1 27     25   7.47    5.43    13.9
print(sex_code2)
## Call: survfit(formula = Surv(SurviePosOpMOIS, Deces) ~ sex_code, data = data1c)
## 
##             n events median 0.95LCL 0.95UCL
## sex_code=0 23     21    6.0     3.5    25.0
## sex_code=1 25     24    6.8     5.7    13.9
print(Tabagisme2)
## Call: survfit(formula = Surv(SurviePosOpMOIS, Deces) ~ Tabagisme, data = data1c)
## 
##              n events median 0.95LCL 0.95UCL
## Tabagisme=0 35     32  13.10     5.7    24.9
## Tabagisme=1 13     13   5.43     2.9      NA
print(AmbulationPreOP2)
## Call: survfit(formula = Surv(SurviePosOpMOIS, Deces) ~ AmbulationPreOP, 
##     data = data1c)
## 
##                    n events median 0.95LCL 0.95UCL
## AmbulationPreOP=0 14     13   5.62     2.9    16.2
## AmbulationPreOP=1 22     21  14.91     6.8    31.9
## AmbulationPreOP=2 12     11   4.48     4.1      NA
print(ASIAPreOp2)
## Call: survfit(formula = Surv(SurviePosOpMOIS, Deces) ~ ASIAPreOp, data = data1c)
## 
##              n events median 0.95LCL 0.95UCL
## ASIAPreOp=1  8      8   3.98    2.90      NA
## ASIAPreOp=2 24     22   6.63    4.43    13.7
## ASIAPreOp=3 16     15  16.16    5.70    32.0
print(AmbulationPO2)
## Call: survfit(formula = Surv(SurviePosOpMOIS, Deces) ~ AmbulationPO, 
##     data = data1c)
## 
##                 n events median 0.95LCL 0.95UCL
## AmbulationPO=0  3      3    1.6    0.77      NA
## AmbulationPO=1 18     17   16.2    6.10    32.0
## AmbulationPO=2 27     25    5.8    4.13    13.6
print(ASIA_PO2)
## Call: survfit(formula = Surv(SurviePosOpMOIS, Deces) ~ ASIA_PO, data = data1c)
## 
##            n events median 0.95LCL 0.95UCL
## ASIA_PO=2  1      1   1.70      NA      NA
## ASIA_PO=3 19     19   4.53    3.43    9.47
## ASIA_PO=4 28     25  15.03    6.47   31.87
print(Tokuhashi_cat2)
## Call: survfit(formula = Surv(SurviePosOpMOIS, Deces) ~ Tokuhashi_cat, 
##     data = data1c)
## 
##                  n events median 0.95LCL 0.95UCL
## Tokuhashi_cat=1 17     17   4.13    3.43    7.67
## Tokuhashi_cat=2 16     16   8.79    3.50   22.00
## Tokuhashi_cat=3 15     12  24.87    6.80      NA
print(RoRxPO2)
## Call: survfit(formula = Surv(SurviePosOpMOIS, Deces) ~ RoRxPO, data = data1c)
## 
##           n events median 0.95LCL 0.95UCL
## RoRxPO=0  9      9   2.57    1.37      NA
## RoRxPO=1 39     36   9.47    5.80    16.2
print(SystemicTherapyPO2)
## Call: survfit(formula = Surv(SurviePosOpMOIS, Deces) ~ SystemicTherapyPO, 
##     data = data1c)
## 
##                      n events median 0.95LCL 0.95UCL
## SystemicTherapyPO=0 25     23   4.43    3.43    13.1
## SystemicTherapyPO=1 23     22  13.70    6.10    25.0

#Univariate cox-model (gia tri P cho moi bang tren)

library(survival)
#model 1: sex
cox1c = coxph(Surv(data1c$SurviePosOpMOIS, data1c$Deces==1) ~ data1c$sex_code)
summary(cox1c)
## Call:
## coxph(formula = Surv(data1c$SurviePosOpMOIS, data1c$Deces == 
##     1) ~ data1c$sex_code)
## 
##   n= 48, number of events= 45 
## 
##                     coef exp(coef) se(coef)     z Pr(>|z|)
## data1c$sex_code1 0.09499   1.09965  0.30576 0.311    0.756
## 
##                  exp(coef) exp(-coef) lower .95 upper .95
## data1c$sex_code1       1.1     0.9094    0.6039     2.002
## 
## Concordance= 0.494  (se = 0.045 )
## Likelihood ratio test= 0.1  on 1 df,   p=0.8
## Wald test            = 0.1  on 1 df,   p=0.8
## Score (logrank) test = 0.1  on 1 df,   p=0.8
#model 2: age group
cox2c = coxph(Surv(data1c$SurviePosOpMOIS, data1c$Deces==1) ~ data1c$age_code)
summary(cox2c)
## Call:
## coxph(formula = Surv(data1c$SurviePosOpMOIS, data1c$Deces == 
##     1) ~ data1c$age_code)
## 
##   n= 48, number of events= 45 
## 
##                     coef exp(coef) se(coef)     z Pr(>|z|)
## data1c$age_code1 0.02104   1.02126  0.30575 0.069    0.945
## 
##                  exp(coef) exp(-coef) lower .95 upper .95
## data1c$age_code1     1.021     0.9792    0.5609     1.859
## 
## Concordance= 0.501  (se = 0.044 )
## Likelihood ratio test= 0  on 1 df,   p=0.9
## Wald test            = 0  on 1 df,   p=0.9
## Score (logrank) test = 0  on 1 df,   p=0.9
#model 3: tobacco use 
cox3c = coxph(Surv(data1c$SurviePosOpMOIS, data1c$Deces==1) ~ data1c$Tabagisme)
summary(cox3c)
## Call:
## coxph(formula = Surv(data1c$SurviePosOpMOIS, data1c$Deces == 
##     1) ~ data1c$Tabagisme)
## 
##   n= 48, number of events= 45 
## 
##                     coef exp(coef) se(coef)     z Pr(>|z|)   
## data1c$Tabagisme1 1.0310    2.8037   0.3667 2.812  0.00493 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##                   exp(coef) exp(-coef) lower .95 upper .95
## data1c$Tabagisme1     2.804     0.3567     1.367     5.752
## 
## Concordance= 0.583  (se = 0.033 )
## Likelihood ratio test= 7.21  on 1 df,   p=0.007
## Wald test            = 7.91  on 1 df,   p=0.005
## Score (logrank) test = 8.58  on 1 df,   p=0.003
#model 5: Pre. ASIA score
cox5c = coxph(Surv(data1c$SurviePosOpMOIS, data1c$Deces==1) ~ data1c$ASIAPreOp)
summary(cox5c)
## Call:
## coxph(formula = Surv(data1c$SurviePosOpMOIS, data1c$Deces == 
##     1) ~ data1c$ASIAPreOp)
## 
##   n= 48, number of events= 45 
## 
##                     coef exp(coef) se(coef)     z Pr(>|z|)  
## data1c$ASIAPreOp1 1.0379    2.8232   0.4605 2.254   0.0242 *
## data1c$ASIAPreOp2 0.1886    1.2076   0.3403 0.554   0.5794  
## data1c$ASIAPreOp3     NA        NA   0.0000    NA       NA  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##                   exp(coef) exp(-coef) lower .95 upper .95
## data1c$ASIAPreOp1     2.823     0.3542    1.1450     6.961
## data1c$ASIAPreOp2     1.208     0.8281    0.6198     2.353
## data1c$ASIAPreOp3        NA         NA        NA        NA
## 
## Concordance= 0.588  (se = 0.042 )
## Likelihood ratio test= 4.61  on 2 df,   p=0.1
## Wald test            = 5.35  on 2 df,   p=0.07
## Score (logrank) test = 5.72  on 2 df,   p=0.06
#model 6: Post. ASIA score
cox6c = coxph(Surv(data1c$SurviePosOpMOIS, data1c$Deces==1) ~ data1c$ASIA_PO)
summary(cox6c)
## Call:
## coxph(formula = Surv(data1c$SurviePosOpMOIS, data1c$Deces == 
##     1) ~ data1c$ASIA_PO)
## 
##   n= 48, number of events= 45 
## 
##                    coef exp(coef) se(coef)     z Pr(>|z|)    
## data1c$ASIA_PO1      NA        NA   0.0000    NA       NA    
## data1c$ASIA_PO2  3.0972   22.1358   1.1444 2.706  0.00680 ** 
## data1c$ASIA_PO3  1.2739    3.5749   0.3556 3.583  0.00034 ***
## data1c$ASIA_PO4      NA        NA   0.0000    NA       NA    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##                 exp(coef) exp(-coef) lower .95 upper .95
## data1c$ASIA_PO1        NA         NA        NA        NA
## data1c$ASIA_PO2    22.136    0.04518     2.350   208.545
## data1c$ASIA_PO3     3.575    0.27972     1.781     7.177
## data1c$ASIA_PO4        NA         NA        NA        NA
## 
## Concordance= 0.642  (se = 0.034 )
## Likelihood ratio test= 15.49  on 2 df,   p=4e-04
## Wald test            = 16.8  on 2 df,   p=2e-04
## Score (logrank) test = 21.33  on 2 df,   p=2e-05
#model 7: Revised Tokuhashi score

cox7c = coxph(Surv(data1c$SurviePosOpMOIS, data1c$Deces==1) ~ data1c$Tokuhashi_cat)
summary(cox7c)
## Call:
## coxph(formula = Surv(data1c$SurviePosOpMOIS, data1c$Deces == 
##     1) ~ data1c$Tokuhashi_cat)
## 
##   n= 48, number of events= 45 
## 
##                          coef exp(coef) se(coef)      z Pr(>|z|)    
## data1c$Tokuhashi_cat2 -0.6042    0.5465   0.3567 -1.694 0.090348 .  
## data1c$Tokuhashi_cat3 -1.4660    0.2308   0.4076 -3.596 0.000323 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##                       exp(coef) exp(-coef) lower .95 upper .95
## data1c$Tokuhashi_cat2    0.5465      1.830    0.2716    1.0997
## data1c$Tokuhashi_cat3    0.2308      4.332    0.1038    0.5132
## 
## Concordance= 0.665  (se = 0.04 )
## Likelihood ratio test= 13.66  on 2 df,   p=0.001
## Wald test            = 12.94  on 2 df,   p=0.002
## Score (logrank) test = 14.25  on 2 df,   p=8e-04
#model 8: Pre. ambulatory status

cox8c = coxph(Surv(data1c$SurviePosOpMOIS, data1c$Deces==1) ~ data1c$AmbulationPreOP)
summary(cox8c)
## Call:
## coxph(formula = Surv(data1c$SurviePosOpMOIS, data1c$Deces == 
##     1) ~ data1c$AmbulationPreOP)
## 
##   n= 48, number of events= 45 
## 
##                            coef exp(coef) se(coef)      z Pr(>|z|)  
## data1c$AmbulationPreOP1 -0.6302    0.5325   0.3633 -1.735   0.0828 .
## data1c$AmbulationPreOP2 -0.1459    0.8642   0.4113 -0.355   0.7227  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##                         exp(coef) exp(-coef) lower .95 upper .95
## data1c$AmbulationPreOP1    0.5325      1.878    0.2613     1.085
## data1c$AmbulationPreOP2    0.8642      1.157    0.3860     1.935
## 
## Concordance= 0.597  (se = 0.046 )
## Likelihood ratio test= 3.41  on 2 df,   p=0.2
## Wald test            = 3.43  on 2 df,   p=0.2
## Score (logrank) test = 3.51  on 2 df,   p=0.2
#model 9: Post. ambulatory status
cox9c = coxph(Surv(data1c$SurviePosOpMOIS, data1c$Deces==1) ~ data1c$AmbulationPO)
summary(cox9c)
## Call:
## coxph(formula = Surv(data1c$SurviePosOpMOIS, data1c$Deces == 
##     1) ~ data1c$AmbulationPO)
## 
##   n= 48, number of events= 45 
## 
##                          coef exp(coef) se(coef)      z Pr(>|z|)    
## data1c$AmbulationPO1 -3.87216   0.02081  0.95290 -4.064 4.83e-05 ***
## data1c$AmbulationPO2 -3.39389   0.03358  0.93078 -3.646 0.000266 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##                      exp(coef) exp(-coef) lower .95 upper .95
## data1c$AmbulationPO1   0.02081      48.05  0.003215    0.1347
## data1c$AmbulationPO2   0.03358      29.78  0.005417    0.2081
## 
## Concordance= 0.63  (se = 0.043 )
## Likelihood ratio test= 14.71  on 2 df,   p=6e-04
## Wald test            = 16.79  on 2 df,   p=2e-04
## Score (logrank) test = 39.8  on 2 df,   p=2e-09
#model 10: Post. Radiotherapy

cox10c = coxph(Surv(data1c$SurviePosOpMOIS, data1c$Deces==1) ~ data1c$RoRxPO)
summary(cox10c)
## Call:
## coxph(formula = Surv(data1c$SurviePosOpMOIS, data1c$Deces == 
##     1) ~ data1c$RoRxPO)
## 
##   n= 48, number of events= 45 
## 
##                   coef exp(coef) se(coef)      z Pr(>|z|)  
## data1c$RoRxPO1 -0.9337    0.3931   0.3821 -2.444   0.0145 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##                exp(coef) exp(-coef) lower .95 upper .95
## data1c$RoRxPO1    0.3931      2.544    0.1859    0.8312
## 
## Concordance= 0.583  (se = 0.035 )
## Likelihood ratio test= 5  on 1 df,   p=0.03
## Wald test            = 5.97  on 1 df,   p=0.01
## Score (logrank) test = 6.41  on 1 df,   p=0.01
#model 11: Post. Chemotherapy

cox11c = coxph(Surv(data1c$SurviePosOpMOIS, data1c$Deces==1) ~ data1c$SystemicTherapyPO)
summary(cox11c)
## Call:
## coxph(formula = Surv(data1c$SurviePosOpMOIS, data1c$Deces == 
##     1) ~ data1c$SystemicTherapyPO)
## 
##   n= 48, number of events= 45 
## 
##                              coef exp(coef) se(coef)      z Pr(>|z|)
## data1c$SystemicTherapyPO1 -0.3306    0.7185   0.3041 -1.087    0.277
## 
##                           exp(coef) exp(-coef) lower .95 upper .95
## data1c$SystemicTherapyPO1    0.7185      1.392    0.3959     1.304
## 
## Concordance= 0.592  (se = 0.04 )
## Likelihood ratio test= 1.18  on 1 df,   p=0.3
## Wald test            = 1.18  on 1 df,   p=0.3
## Score (logrank) test = 1.19  on 1 df,   p=0.3
---
title: "R_Survival data2020"
author: "Binh Thang Tran"
date: "03/23/2020"
output:
  html_document:
    code_download: yes
  pdf_document: default
  word_document: default
---

****if deces: 0=death; 1 alive)*************

#Prepare: packages, data
```{r}
library("foreign")
library("survival")

require("moonBook")
require("ztable")
require("magrittr")
options(ztable.type="html")

```

#read data and group for variable

## Read Data

```{r}
data=read.csv("D:/data/data csv.csv")
head(data)
names(data) #name of variables
```

##explaination


###subset dataset -Independent vars:


```{r}
a <- subset(data, select=c(localisation, sex_code, age_code, Tabagisme, ASIAPreOp, ASIA_PO, Tokuhashi_cat,AmbulationPreOP, AmbulationPO, RoRxPO,SystemicTherapyPO, Improvement.of.ambulation, SurviePosOpMOIS, Deces))

data1=a
```


Independent variables

```{r}

data1$sex_code = factor(data1$sex_code) # Sex: male - female (sexe)
data1$age_code=factor(data1$age_code) 
data1$Tabagisme = factor(data1$Tabagisme) #Tobacco use: Yes - No (tabagisme)
data1$ASIAPreOp = factor(data1$ASIAPreOp) #Pre. ASIA score: A, C, D, E   (ASIAPr3Op)
data1$ASIA_PO = factor(data1$ASIA_PO) #Post. ASIA score: A, B C, D, E  (ASIA_PO)
data1$Tokuhashi_cat = factor(data1$Tokuhashi_cat) #Revised Tokuhashi score: 0-8; 9-11   (Tokuhashi_cat)
data1$AmbulationPreOP = factor(data1$AmbulationPreOP) #Pre. ambulatory status: No, With help; Independent   (ambulation_preop)
data1$AmbulationPO = factor(data1$AmbulationPO) #Post. ambulatory status: No, With help; Independent  (ambulationPO)
data1$RoRxPO = factor(data1$RoRxPO)  #Post. Radiotherapy: No- Yes   (RoRx_PO)
data1$SystemicTherapyPO = factor(data1$SystemicTherapyPO) #
```
####View new dataset again

```{r}
head(data1)
View(data1)
```


#participant chracteristics 

```{r}
mytable(localisation~sex_code+age_code+Tabagisme+ ASIAPreOp+ ASIA_PO+ Tokuhashi_cat+AmbulationPreOP+ AmbulationPO+ RoRxPO+SystemicTherapyPO+ Improvement.of.ambulation,data=data1)
```


###Outcome:
SurviePosOpMOIS: time-to-event
deces: 0: censorted (alive); 1 event (die)

```{r}
library("survival")
```


```{r}
baseline = Surv(data1$SurviePosOpMOIS, data1$Deces==1)
km = survfit(baseline ~ 1)
km
summary(km)
```

`


## Including Plots

```{r}
library(ranger)
library(ggplot2)
library(dplyr)
library(ggfortify)
```

Kaplan – Meier plot:

```{r}
plot(km, xlab="Time to death", ylab="Prob of survival")

km_trt_fit <- survfit(Surv(SurviePosOpMOIS, Deces) ~ localisation, data=data1)

```

```{r}
print(km_trt_fit)
```


```{r}
autoplot(km_trt_fit)
```




#table 1: general chracteristics of participants

```{r}
#install package "moonBook", ztable
library(moonBook)
require(ztable)
require(magrittr)
options(ztable.type="html")
```

```{r}
#table 1: general chracteristics of participants

mytable(data1)

```



##Table 2



```{r}
attach(data1)

data1a <- subset(data1, data1$localisation==1)
data1b <- subset(data1, data1$localisation==2)
data1c <- subset(data1, data1$localisation==3)
```



### Cervical spine surgery No. (1a)

```{r}
age_sur <- survfit(Surv(SurviePosOpMOIS, Deces) ~ age_code, data=data1a)

sex_code <- survfit(Surv(SurviePosOpMOIS, Deces) ~ sex_code, data=data1a)
Tabagisme <- survfit(Surv(SurviePosOpMOIS, Deces) ~ Tabagisme, data=data1a)

AmbulationPreOP <- survfit(Surv(SurviePosOpMOIS, Deces) ~ AmbulationPreOP, data=data1a)
ASIAPreOp <- survfit(Surv(SurviePosOpMOIS, Deces) ~ ASIAPreOp, data=data1a)
AmbulationPO <- survfit(Surv(SurviePosOpMOIS, Deces) ~ AmbulationPO, data=data1a)
ASIA_PO <- survfit(Surv(SurviePosOpMOIS, Deces) ~ ASIA_PO, data=data1a)
Tokuhashi_cat <- survfit(Surv(SurviePosOpMOIS, Deces) ~ Tokuhashi_cat, data=data1a)
RoRxPO <- survfit(Surv(SurviePosOpMOIS, Deces) ~ RoRxPO, data=data1a)
SystemicTherapyPO <- survfit(Surv(SurviePosOpMOIS, Deces) ~ SystemicTherapyPO, data=data1a)


```


#### table 2 (ressult): cacs ket qua bang 2
```{r}
print(age_sur)
print(sex_code)
print(Tabagisme)
print(AmbulationPreOP)
print(ASIAPreOp)
print(AmbulationPO)
print(ASIA_PO)
print(Tokuhashi_cat)
print(RoRxPO)
print(SystemicTherapyPO)
```





#Univariate cox-model (gia tri P cho moi bang tren)

```{r}
library(survival)
#model 1: sex
cox1 = coxph(Surv(data1a$SurviePosOpMOIS, data1a$Deces==1) ~ data1a$sex_code)
summary(cox1)

#model 2: age group
cox2 = coxph(Surv(data1a$SurviePosOpMOIS, data1a$Deces==1) ~ data1a$age_code)
summary(cox2)

#model 3: tobacco use 
cox3 = coxph(Surv(data1a$SurviePosOpMOIS, data1a$Deces==1) ~ data1a$Tabagisme)
summary(cox3)


#model 5: Pre. ASIA score
cox5 = coxph(Surv(data1a$SurviePosOpMOIS, data1a$Deces==1) ~ data1a$ASIAPreOp)
summary(cox5)

#model 6: Post. ASIA score
cox6 = coxph(Surv(data1a$SurviePosOpMOIS, data1a$Deces==1) ~ data1a$ASIA_PO)
summary(cox6)

#model 7: Revised Tokuhashi score

cox7 = coxph(Surv(data1a$SurviePosOpMOIS, data1a$Deces==1) ~ data1a$Tokuhashi_cat)
summary(cox7)

#model 8: Pre. ambulatory status

cox8 = coxph(Surv(data1a$SurviePosOpMOIS, data1a$Deces==1) ~ data1a$AmbulationPreOP)
summary(cox8)

#model 9: Post. ambulatory status
cox9 = coxph(Surv(data1a$SurviePosOpMOIS, data1a$Deces==1) ~ data1a$AmbulationPO)
summary(cox9)

#model 10: Post. Radiotherapy

cox10 = coxph(Surv(data1a$SurviePosOpMOIS, data1a$Deces==1) ~ data1a$RoRxPO)
summary(cox10)

#model 11: Post. Chemotherapy

cox11 = coxph(Surv(data1a$SurviePosOpMOIS, data1a$Deces==1) ~ data1a$SystemicTherapyPO)
summary(cox11)

```






### 1.b:  Thoracic spine surgery. (1b)

```{r}
age_sur1 <- survfit(Surv(SurviePosOpMOIS, Deces) ~ age_code, data=data1b)
sex_code1 <- survfit(Surv(SurviePosOpMOIS, Deces) ~ sex_code, data=data1b)
Tabagisme1 <- survfit(Surv(SurviePosOpMOIS, Deces) ~ Tabagisme, data=data1b)

AmbulationPreOP1 <- survfit(Surv(SurviePosOpMOIS, Deces) ~ AmbulationPreOP, data=data1b)
ASIAPreOp1 <- survfit(Surv(SurviePosOpMOIS, Deces) ~ ASIAPreOp, data=data1b)
AmbulationPO1 <- survfit(Surv(SurviePosOpMOIS, Deces) ~ AmbulationPO, data=data1b)
ASIA_PO1 <- survfit(Surv(SurviePosOpMOIS, Deces) ~ ASIA_PO, data=data1b)
Tokuhashi_cat1 <- survfit(Surv(SurviePosOpMOIS, Deces) ~ Tokuhashi_cat, data=data1b)
RoRxPO1 <- survfit(Surv(SurviePosOpMOIS, Deces) ~ RoRxPO, data=data1b)
SystemicTherapyPO1 <- survfit(Surv(SurviePosOpMOIS, Deces) ~ SystemicTherapyPO, data=data1b)


```



#### table 2 (ressult): cacs ket qua bang 2b
```{r}
print(age_sur1)
print(sex_code1)
print(Tabagisme1)
print(AmbulationPreOP1)
print(ASIAPreOp1)
print(AmbulationPO1)
print(ASIA_PO1)
print(Tokuhashi_cat1)
print(RoRxPO1)
print(SystemicTherapyPO1)
```





#Univariate cox-model (gia tri P cho moi bang tren)

```{r}
library(survival)
#model 1: sex
cox1b = coxph(Surv(data1b$SurviePosOpMOIS, data1b$Deces==1) ~ data1b$sex_code)
summary(cox1b)

#model 2: age group
cox2b = coxph(Surv(data1b$SurviePosOpMOIS, data1b$Deces==1) ~ data1b$age_code)
summary(cox2b)

#model 3: tobacco use 
cox3b = coxph(Surv(data1b$SurviePosOpMOIS, data1b$Deces==1) ~ data1b$Tabagisme)
summary(cox3b)


#model 5: Pre. ASIA score
cox5b = coxph(Surv(data1b$SurviePosOpMOIS, data1b$Deces==1) ~ data1b$ASIAPreOp)
summary(cox5b)

#model 6: Post. ASIA score
cox6b = coxph(Surv(data1b$SurviePosOpMOIS, data1b$Deces==1) ~ data1b$ASIA_PO)
summary(cox6b)

#model 7: Revised Tokuhashi score

cox7b = coxph(Surv(data1b$SurviePosOpMOIS, data1b$Deces==1) ~ data1b$Tokuhashi_cat)
summary(cox7b)

#model 8: Pre. ambulatory status

cox8b = coxph(Surv(data1b$SurviePosOpMOIS, data1b$Deces==1) ~ data1b$AmbulationPreOP)
summary(cox8b)

#model 9: Post. ambulatory status
cox9b = coxph(Surv(data1b$SurviePosOpMOIS, data1b$Deces==1) ~ data1b$AmbulationPO)
summary(cox9b)

#model 10: Post. Radiotherapy

cox10b = coxph(Surv(data1b$SurviePosOpMOIS, data1b$Deces==1) ~ data1b$RoRxPO)
summary(cox10b)

#model 11: Post. Chemotherapy

cox11b = coxph(Surv(data1b$SurviePosOpMOIS, data1b$Deces==1) ~ data1b$SystemicTherapyPO)
summary(cox11b)

```





### 1.c:  Lumbar spine surgery. (1c)

```{r}
age_sur2 <- survfit(Surv(SurviePosOpMOIS, Deces) ~ age_code, data=data1c)
sex_code2 <- survfit(Surv(SurviePosOpMOIS, Deces) ~ sex_code, data=data1c)
Tabagisme2 <- survfit(Surv(SurviePosOpMOIS, Deces) ~ Tabagisme, data=data1c)

AmbulationPreOP2 <- survfit(Surv(SurviePosOpMOIS, Deces) ~ AmbulationPreOP, data=data1c)
ASIAPreOp2 <- survfit(Surv(SurviePosOpMOIS, Deces) ~ ASIAPreOp, data=data1c)
AmbulationPO2 <- survfit(Surv(SurviePosOpMOIS, Deces) ~ AmbulationPO, data=data1c)
ASIA_PO2 <- survfit(Surv(SurviePosOpMOIS, Deces) ~ ASIA_PO, data=data1c)
Tokuhashi_cat2 <- survfit(Surv(SurviePosOpMOIS, Deces) ~ Tokuhashi_cat, data=data1c)
RoRxPO2 <- survfit(Surv(SurviePosOpMOIS, Deces) ~ RoRxPO, data=data1c)
SystemicTherapyPO2 <- survfit(Surv(SurviePosOpMOIS, Deces) ~ SystemicTherapyPO, data=data1c)


```



#### table 2 (ressult): cacs ket qua bang 2b
```{r}
print(age_sur2)
print(sex_code2)
print(Tabagisme2)
print(AmbulationPreOP2)
print(ASIAPreOp2)
print(AmbulationPO2)
print(ASIA_PO2)
print(Tokuhashi_cat2)
print(RoRxPO2)
print(SystemicTherapyPO2)
```





#Univariate cox-model (gia tri P cho moi bang tren)

```{r}
library(survival)
#model 1: sex
cox1c = coxph(Surv(data1c$SurviePosOpMOIS, data1c$Deces==1) ~ data1c$sex_code)
summary(cox1c)

#model 2: age group
cox2c = coxph(Surv(data1c$SurviePosOpMOIS, data1c$Deces==1) ~ data1c$age_code)
summary(cox2c)

#model 3: tobacco use 
cox3c = coxph(Surv(data1c$SurviePosOpMOIS, data1c$Deces==1) ~ data1c$Tabagisme)
summary(cox3c)


#model 5: Pre. ASIA score
cox5c = coxph(Surv(data1c$SurviePosOpMOIS, data1c$Deces==1) ~ data1c$ASIAPreOp)
summary(cox5c)

#model 6: Post. ASIA score
cox6c = coxph(Surv(data1c$SurviePosOpMOIS, data1c$Deces==1) ~ data1c$ASIA_PO)
summary(cox6c)

#model 7: Revised Tokuhashi score

cox7c = coxph(Surv(data1c$SurviePosOpMOIS, data1c$Deces==1) ~ data1c$Tokuhashi_cat)
summary(cox7c)

#model 8: Pre. ambulatory status

cox8c = coxph(Surv(data1c$SurviePosOpMOIS, data1c$Deces==1) ~ data1c$AmbulationPreOP)
summary(cox8c)

#model 9: Post. ambulatory status
cox9c = coxph(Surv(data1c$SurviePosOpMOIS, data1c$Deces==1) ~ data1c$AmbulationPO)
summary(cox9c)

#model 10: Post. Radiotherapy

cox10c = coxph(Surv(data1c$SurviePosOpMOIS, data1c$Deces==1) ~ data1c$RoRxPO)
summary(cox10c)

#model 11: Post. Chemotherapy

cox11c = coxph(Surv(data1c$SurviePosOpMOIS, data1c$Deces==1) ~ data1c$SystemicTherapyPO)
summary(cox11c)

```







