R_Survival data2020
Binh Thang Tran
03/23/2020
****if deces: 0=death; 1 alive)*************
#Prepare: packages, data
library("foreign")
library("survival")
require("moonBook")## Loading required package: moonBookrequire("ztable")## Loading required package: ztable## Welcome to package ztable ver 0.2.0require("magrittr")## Loading required package: magrittroptions(ztable.type="html")#read data and group for variable
Read Data
data=read.csv("E:/GCSP/Jobs/A Tri/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 0.00names(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:
data1 <- subset(data, select=c(localisation, sex_code, age_code, Tabagisme, ASIAPreOp, ASIA_PO, Tokuhashi_cat,AmbulationPreOP, AmbulationPO, RoRxPO,SystemicTherapyPO, Improvement.of.ambulation, SurviePosOpMOIS, Deces))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 1View(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 10.0summary(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)
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, unionlibrary(ggfortify)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 13.7autoplot(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 40.3print(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 10.4print(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 5.7print(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 NAprint(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 35.8print(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 NAprint(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 16.9print(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 NAprint(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 35.83print(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=0.0031.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 12.4print(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 10.1print(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 12.9print(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 12.0print(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 19.7print(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 10.1print(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 17.27print(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 NAprint(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 17.27print(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, istrat, 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-061.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 13.9print(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 13.9print(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 NAprint(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 NAprint(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 32.0print(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 13.6print(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 31.87print(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 NAprint(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 16.2print(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##Tabl3
mytable(localisation+Improvement.of.ambulation~sex_code+age_code+Tabagisme+ ASIAPreOp+ ASIA_PO+ Tokuhashi_cat+AmbulationPreOP+ AmbulationPO+ RoRxPO+SystemicTherapyPO,data=data1) %>% deleteRows(3)##
## Descriptive Statistics stratified by 'localisation' and 'Improvement.of.ambulation'
## _______________________________________________________________________________________________________________________________________
## 1 2 3
## -------------------------------------- -------------------------------------- --------------------------------------
## 0 1 2 p 0 1 2 p 0 1 2 p
## (N=30) (N=10) (N=7) (N=58) (N=25) (N=13) (N=23) (N=15) (N=10)
## ---------------------------------------------------------------------------------------------------------------------------------------
## sex_code 0.407 0.584 0.443
## - 0 14 (46.7%) 4 (40.0%) 5 (71.4%) 28 (48.3%) 9 (36.0%) 6 (46.2%) 12 (52.2%) 8 (53.3%) 3 (30.0%)
## age_code 0.316 0.864 0.947
## - 0 12 (40.0%) 5 (50.0%) 5 (71.4%) 22 (37.9%) 8 (32.0%) 5 (38.5%) 10 (43.5%) 7 (46.7%) 4 (40.0%)
## - 1 18 (60.0%) 5 (50.0%) 2 (28.6%) 36 (62.1%) 17 (68.0%) 8 (61.5%) 13 (56.5%) 8 (53.3%) 6 (60.0%)
## Tabagisme 0.395 0.571 0.002
## - 0 16 (53.3%) 3 (30.0%) 4 (57.1%) 36 (62.1%) 15 (60.0%) 6 (46.2%) 20 (87.0%) 6 (40.0%) 9 (90.0%)
## - 1 14 (46.7%) 7 (70.0%) 3 (42.9%) 22 (37.9%) 10 (40.0%) 7 (53.8%) 3 (13.0%) 9 (60.0%) 1 (10.0%)
## ASIAPreOp 0.000
## - 0 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 1 ( 1.7%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%)
## - 1 0 ( 0.0%) 2 (20.0%) 0 ( 0.0%) 2 ( 3.4%) 11 (44.0%) 0 ( 0.0%) 3 (13.0%) 4 (26.7%) 1 (10.0%)
## - 2 12 (40.0%) 5 (50.0%) 5 (71.4%) 26 (44.8%) 9 (36.0%) 5 (38.5%) 11 (47.8%) 8 (53.3%) 5 (50.0%)
## - 3 18 (60.0%) 3 (30.0%) 2 (28.6%) 29 (50.0%) 5 (20.0%) 8 (61.5%) 9 (39.1%) 3 (20.0%) 4 (40.0%)
## ASIA_PO 0.281
## - 0 0 ( 0.0%) 0 ( 0.0%) 1 (14.3%) 1 ( 1.7%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%)
## - 1 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 1 ( 1.7%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%)
## - 2 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 1 ( 1.7%) 0 ( 0.0%) 0 ( 0.0%) 1 ( 4.3%) 0 ( 0.0%) 0 ( 0.0%)
## - 3 8 (26.7%) 4 (40.0%) 2 (28.6%) 17 (29.3%) 15 (60.0%) 3 (23.1%) 6 (26.1%) 8 (53.3%) 5 (50.0%)
## - 4 22 (73.3%) 6 (60.0%) 4 (57.1%) 38 (65.5%) 10 (40.0%) 10 (76.9%) 16 (69.6%) 7 (46.7%) 5 (50.0%)
## Tokuhashi_cat 0.144 0.068 0.208
## - 1 9 (30.0%) 5 (50.0%) 3 (42.9%) 20 (34.5%) 15 (60.0%) 7 (53.8%) 8 (34.8%) 6 (40.0%) 3 (30.0%)
## - 2 9 (30.0%) 4 (40.0%) 4 (57.1%) 24 (41.4%) 8 (32.0%) 6 (46.2%) 5 (21.7%) 5 (33.3%) 6 (60.0%)
## - 3 12 (40.0%) 1 (10.0%) 0 ( 0.0%) 14 (24.1%) 2 ( 8.0%) 0 ( 0.0%) 10 (43.5%) 4 (26.7%) 1 (10.0%)
## AmbulationPreOP 0.000 0.000 0.000
## - 0 1 ( 3.3%) 6 (60.0%) 0 ( 0.0%) 4 ( 6.9%) 17 (68.0%) 0 ( 0.0%) 1 ( 4.3%) 13 (86.7%) 0 ( 0.0%)
## - 1 24 (80.0%) 0 ( 0.0%) 5 (71.4%) 40 (69.0%) 0 ( 0.0%) 12 (92.3%) 13 (56.5%) 0 ( 0.0%) 9 (90.0%)
## - 2 5 (16.7%) 4 (40.0%) 2 (28.6%) 14 (24.1%) 8 (32.0%) 1 ( 7.7%) 9 (39.1%) 2 (13.3%) 1 (10.0%)
## AmbulationPO 0.000 0.000 0.013
## - 0 1 ( 3.3%) 0 ( 0.0%) 4 (57.1%) 4 ( 6.9%) 0 ( 0.0%) 1 ( 7.7%) 1 ( 4.3%) 0 ( 0.0%) 2 (20.0%)
## - 1 24 (80.0%) 6 (60.0%) 0 ( 0.0%) 40 (69.0%) 11 (44.0%) 0 ( 0.0%) 13 (56.5%) 5 (33.3%) 0 ( 0.0%)
## - 2 5 (16.7%) 4 (40.0%) 3 (42.9%) 14 (24.1%) 14 (56.0%) 12 (92.3%) 9 (39.1%) 10 (66.7%) 8 (80.0%)
## RoRxPO 0.000 0.135 0.512
## - 0 5 (16.7%) 4 (40.0%) 7 (100.0%) 15 (26.3%) 4 (16.0%) 6 (46.2%) 3 (13.0%) 3 (20.0%) 3 (30.0%)
## - 1 25 (83.3%) 6 (60.0%) 0 ( 0.0%) 42 (73.7%) 21 (84.0%) 7 (53.8%) 20 (87.0%) 12 (80.0%) 7 (70.0%)
## SystemicTherapyPO 0.664 0.220 0.443
## - 0 12 (40.0%) 5 (50.0%) 4 (57.1%) 25 (43.1%) 13 (52.0%) 9 (69.2%) 11 (47.8%) 7 (46.7%) 7 (70.0%)
## - 1 18 (60.0%) 5 (50.0%) 3 (42.9%) 33 (56.9%) 12 (48.0%) 4 (30.8%) 12 (52.2%) 8 (53.3%) 3 (30.0%)
## 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