#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
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 0.00names(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:
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))##Tabl3
mytable(localisation+Improvement.of.ambulation~age_code+Tabagisme+ ASIAPreOp+ ASIA_PO+ Tokuhashi_cat+AmbulationPreOP+ AmbulationPO+ RoRxPO+SystemicTherapyPO+sex_code,data=a) %>% 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)
## ---------------------------------------------------------------------------------------------------------------------------------------
## 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%)
## 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%)
## 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%)
## - 1 16 (53.3%) 6 (60.0%) 2 (28.6%) 30 (51.7%) 16 (64.0%) 7 (53.8%) 11 (47.8%) 7 (46.7%) 7 (70.0%)
## ---------------------------------------------------------------------------------------------------------------------------------------#logistic for table 3 (Ket qua OR, 95%CI cho bang 3)
a$out=a$Improvement.of.ambulationdata2=a
data2$out1[data2$out==1] <- 1
data2$out1[data2$out==0 ] <- 0
data2$out1[data2$out==2 ] <- 0data2$AmbulationPO1 <- as.numeric(data2$AmbulationPO)
data2$ASIAPreOp1 <- as.numeric(data2$ASIAPreOp)
data2$ASIA_PO1 <- as.numeric(data2$ASIA_PO)
data2$AmbulationPO2[data2$AmbulationPO1==2] <- 0
data2$AmbulationPO2[data2$AmbulationPO1<2 ] <- 1
data2$ASIAPreOp2[data2$ASIAPreOp1 == 3] <- 0
data2$ASIAPreOp2[data2$ASIAPreOp1 <3 ] <- 1
data2$ASIA_PO2[data2$ASIA_PO ==4] <- 0
data2$ASIA_PO2[data2$ASIA_PO <4 ] <- 1###logiostic model Code
library("epiDisplay")## Warning: package 'epiDisplay' was built under R version 3.6.3## Loading required package: MASS## Loading required package: nnetd1 <- ls("package:epiDisplay")data1d <- subset(data2, data2$localisation==1)
data1e <- subset(data2, data2$localisation==2)
data1f <- subset(data2, data2$localisation==3)#Table 3_2a
a1=mytable(out~sex_code+age_code+Tabagisme+AmbulationPO2+ASIA_PO2+Tokuhashi_cat,data=data1d) %>% deleteRows(1)
a1##
## Descriptive Statistics by 'out'
## ____________________________________________________
## 0 1 2 p
## (N=30) (N=10) (N=7)
## ----------------------------------------------------
## - 0 14 (46.7%) 4 (40.0%) 5 (71.4%)
## - 1 16 (53.3%) 6 (60.0%) 2 (28.6%)
## age_code 0.316
## - 0 12 (40.0%) 5 (50.0%) 5 (71.4%)
## - 1 18 (60.0%) 5 (50.0%) 2 (28.6%)
## Tabagisme 0.395
## - 0 16 (53.3%) 3 (30.0%) 4 (57.1%)
## - 1 14 (46.7%) 7 (70.0%) 3 (42.9%)
## AmbulationPO2 0.179
## - 0 5 (16.7%) 4 (40.0%) 3 (42.9%)
## - 1 25 (83.3%) 6 (60.0%) 4 (57.1%)
## ASIA_PO2 0.587
## - 0 22 (73.3%) 6 (60.0%) 4 (57.1%)
## - 1 8 (26.7%) 4 (40.0%) 3 (42.9%)
## Tokuhashi_cat 0.144
## - 1 9 (30.0%) 5 (50.0%) 3 (42.9%)
## - 2 9 (30.0%) 4 (40.0%) 4 (57.1%)
## - 3 12 (40.0%) 1 (10.0%) 0 ( 0.0%)
## ----------------------------------------------------#Table 3_2b
a2=mytable(out~sex_code+age_code+Tabagisme+AmbulationPO2+ASIA_PO2+Tokuhashi_cat,data=data1e) %>% deleteRows(1)
a2##
## Descriptive Statistics by 'out'
## _____________________________________________________
## 0 1 2 p
## (N=58) (N=25) (N=13)
## -----------------------------------------------------
## - 0 28 (48.3%) 9 (36.0%) 6 (46.2%)
## - 1 30 (51.7%) 16 (64.0%) 7 (53.8%)
## age_code 0.864
## - 0 22 (37.9%) 8 (32.0%) 5 (38.5%)
## - 1 36 (62.1%) 17 (68.0%) 8 (61.5%)
## Tabagisme 0.571
## - 0 36 (62.1%) 15 (60.0%) 6 (46.2%)
## - 1 22 (37.9%) 10 (40.0%) 7 (53.8%)
## AmbulationPO2 0.000
## - 0 14 (24.1%) 14 (56.0%) 12 (92.3%)
## - 1 44 (75.9%) 11 (44.0%) 1 ( 7.7%)
## ASIA_PO2 0.039
## - 0 38 (65.5%) 10 (40.0%) 10 (76.9%)
## - 1 20 (34.5%) 15 (60.0%) 3 (23.1%)
## Tokuhashi_cat 0.068
## - 1 20 (34.5%) 15 (60.0%) 7 (53.8%)
## - 2 24 (41.4%) 8 (32.0%) 6 (46.2%)
## - 3 14 (24.1%) 2 ( 8.0%) 0 ( 0.0%)
## -----------------------------------------------------#Table 3_2c
a3=mytable(out~sex_code+age_code+Tabagisme+AmbulationPO2+ASIA_PO2+Tokuhashi_cat,data=data1f) %>% deleteRows(1)
a3##
## Descriptive Statistics by 'out'
## ____________________________________________________
## 0 1 2 p
## (N=23) (N=15) (N=10)
## ----------------------------------------------------
## - 0 12 (52.2%) 8 (53.3%) 3 (30.0%)
## - 1 11 (47.8%) 7 (46.7%) 7 (70.0%)
## age_code 0.947
## - 0 10 (43.5%) 7 (46.7%) 4 (40.0%)
## - 1 13 (56.5%) 8 (53.3%) 6 (60.0%)
## Tabagisme 0.002
## - 0 20 (87.0%) 6 (40.0%) 9 (90.0%)
## - 1 3 (13.0%) 9 (60.0%) 1 (10.0%)
## AmbulationPO2 0.058
## - 0 9 (39.1%) 10 (66.7%) 8 (80.0%)
## - 1 14 (60.9%) 5 (33.3%) 2 (20.0%)
## ASIA_PO2 0.314
## - 0 16 (69.6%) 7 (46.7%) 5 (50.0%)
## - 1 7 (30.4%) 8 (53.3%) 5 (50.0%)
## Tokuhashi_cat 0.208
## - 1 8 (34.8%) 6 (40.0%) 3 (30.0%)
## - 2 5 (21.7%) 5 (33.3%) 6 (60.0%)
## - 3 10 (43.5%) 4 (26.7%) 1 (10.0%)
## ----------------------------------------------------#Table 3_2a
a1=mytable(out1~sex_code+age_code+Tabagisme+AmbulationPO2+ASIA_PO2+Tokuhashi_cat,data=data1d) %>% deleteRows(1)
a1##
## Descriptive Statistics by 'out1'
## _________________________________________
## 0 1 p
## (N=37) (N=10)
## -----------------------------------------
## - 0 19 (51.4%) 4 (40.0%)
## - 1 18 (48.6%) 6 (60.0%)
## age_code 1.000
## - 0 17 (45.9%) 5 (50.0%)
## - 1 20 (54.1%) 5 (50.0%)
## Tabagisme 0.320
## - 0 20 (54.1%) 3 (30.0%)
## - 1 17 (45.9%) 7 (70.0%)
## AmbulationPO2 0.439
## - 0 8 (21.6%) 4 (40.0%)
## - 1 29 (78.4%) 6 (60.0%)
## ASIA_PO2 0.814
## - 0 26 (70.3%) 6 (60.0%)
## - 1 11 (29.7%) 4 (40.0%)
## Tokuhashi_cat 0.340
## - 1 12 (32.4%) 5 (50.0%)
## - 2 13 (35.1%) 4 (40.0%)
## - 3 12 (32.4%) 1 (10.0%)
## -----------------------------------------#Table 3_2b
a2=mytable(out1~sex_code+age_code+Tabagisme+AmbulationPO2+ASIA_PO2+Tokuhashi_cat,data=data1e) %>% deleteRows(1)
a2##
## Descriptive Statistics by 'out1'
## __________________________________________
## 0 1 p
## (N=71) (N=25)
## ------------------------------------------
## - 0 34 (47.9%) 9 (36.0%)
## - 1 37 (52.1%) 16 (64.0%)
## age_code 0.767
## - 0 27 (38.0%) 8 (32.0%)
## - 1 44 (62.0%) 17 (68.0%)
## Tabagisme 1.000
## - 0 42 (59.2%) 15 (60.0%)
## - 1 29 (40.8%) 10 (40.0%)
## AmbulationPO2 0.146
## - 0 26 (36.6%) 14 (56.0%)
## - 1 45 (63.4%) 11 (44.0%)
## ASIA_PO2 0.029
## - 0 48 (67.6%) 10 (40.0%)
## - 1 23 (32.4%) 15 (60.0%)
## Tokuhashi_cat 0.132
## - 1 27 (38.0%) 15 (60.0%)
## - 2 30 (42.3%) 8 (32.0%)
## - 3 14 (19.7%) 2 ( 8.0%)
## ------------------------------------------#Table 3_2c
a3=mytable(out1~sex_code+age_code+Tabagisme+AmbulationPO2+ASIA_PO2+Tokuhashi_cat,data=data1f) %>% deleteRows(1)
a3##
## Descriptive Statistics by 'out1'
## __________________________________________
## 0 1 p
## (N=33) (N=15)
## ------------------------------------------
## - 0 15 (45.5%) 8 (53.3%)
## - 1 18 (54.5%) 7 (46.7%)
## age_code 1.000
## - 0 14 (42.4%) 7 (46.7%)
## - 1 19 (57.6%) 8 (53.3%)
## Tabagisme 0.002
## - 0 29 (87.9%) 6 (40.0%)
## - 1 4 (12.1%) 9 (60.0%)
## AmbulationPO2 0.505
## - 0 17 (51.5%) 10 (66.7%)
## - 1 16 (48.5%) 5 (33.3%)
## ASIA_PO2 0.430
## - 0 21 (63.6%) 7 (46.7%)
## - 1 12 (36.4%) 8 (53.3%)
## Tokuhashi_cat 0.871
## - 1 11 (33.3%) 6 (40.0%)
## - 2 11 (33.3%) 5 (33.3%)
## - 3 11 (33.3%) 4 (26.7%)
## ------------------------------------------res1d1 = glm(out1 ~ sex_code , family=binomial,
data=data1d)
logistic.display(res1d1)##
## Logistic regression predicting out1
##
## OR(95%CI) P(Wald's test) P(LR-test)
## sex_code: 1 vs 0 1.58 (0.38,6.55) 0.526 0.523
##
## Log-likelihood = -24.1229
## No. of observations = 47
## AIC value = 52.2458res1d2 = glm(out1 ~ age_code , family=binomial,
data=data1d)
logistic.display(res1d2)##
## Logistic regression predicting out1
##
## OR(95%CI) P(Wald's test) P(LR-test)
## age_code: 1 vs 0 0.85 (0.21,3.44) 0.82 0.82
##
## Log-likelihood = -24.3012
## No. of observations = 47
## AIC value = 52.6024res1d3 = glm(out1 ~ Tabagisme , family=binomial,
data=data1d)
logistic.display(res1d3)##
## Logistic regression predicting out1
##
## OR(95%CI) P(Wald's test) P(LR-test)
## Tabagisme: 1 vs 0 2.75 (0.61,12.29) 0.187 0.172
##
## Log-likelihood = -23.3932
## No. of observations = 47
## AIC value = 50.7864res1d4 = glm(out1 ~ AmbulationPO1 , family=binomial,
data=data1d)
logistic.display(res1d4)##
## Logistic regression predicting out1
##
## OR(95%CI) P(Wald's test) P(LR-test)
## AmbulationPO1 (cont. var.) 2.69 (0.74,9.73) 0.132 0.12
##
## Log-likelihood = -23.116
## No. of observations = 47
## AIC value = 50.2319res1d5 = glm(out1 ~ ASIA_PO1 , family=binomial,
data=data1d)
logistic.display(res1d5)##
## Logistic regression predicting out1
##
## OR(95%CI) P(Wald's test) P(LR-test)
## ASIA_PO1 (cont. var.) 0.96 (0.36,2.53) 0.931 0.932
##
## Log-likelihood = -24.3234
## No. of observations = 47
## AIC value = 52.6469res1d6 = glm(out1 ~ Tokuhashi_cat , family=binomial,
data=data1d)
logistic.display(res1d6)##
## Logistic regression predicting out1
##
## OR(95%CI) P(Wald's test) P(LR-test)
## Tokuhashi_cat (cont. var.) 0.51 (0.19,1.33) 0.168 0.15
##
## Log-likelihood = -23.2905
## No. of observations = 47
## AIC value = 50.581res1e1 = glm(out1 ~ sex_code , family=binomial,
data=data1e)
logistic.display(res1e1)##
## Logistic regression predicting out1
##
## OR(95%CI) P(Wald's test) P(LR-test)
## sex_code: 1 vs 0 1.63 (0.64,4.18) 0.306 0.301
##
## Log-likelihood = -54.5204
## No. of observations = 96
## AIC value = 113.0408res1e2 = glm(out1 ~ age_code , family=binomial,
data=data1e)
logistic.display(res1e2)##
## Logistic regression predicting out1
##
## OR(95%CI) P(Wald's test) P(LR-test)
## age_code: 1 vs 0 1.3 (0.5,3.43) 0.591 0.588
##
## Log-likelihood = -54.9084
## No. of observations = 96
## AIC value = 113.8168res1e = glm(out1 ~ age_code+sex_code+Tabagisme+AmbulationPO1+ASIA_PO1+Tokuhashi_cat , family=binomial, data=data1e) logistic.display(res1e)
res1e3 = glm(out1 ~ Tabagisme , family=binomial,
data=data1e)
logistic.display(res1e3)##
## Logistic regression predicting out1
##
## OR(95%CI) P(Wald's test) P(LR-test)
## Tabagisme: 1 vs 0 0.97 (0.38,2.45) 0.941 0.941
##
## Log-likelihood = -55.0525
## No. of observations = 96
## AIC value = 114.105res1e4 = glm(out1 ~ AmbulationPO1 , family=binomial,
data=data1e)
logistic.display(res1e4)##
## Logistic regression predicting out1
##
## OR(95%CI) P(Wald's test) P(LR-test)
## AmbulationPO1 (cont. var.) 2.31 (0.99,5.42) 0.053 0.046
##
## Log-likelihood = -53.0606
## No. of observations = 96
## AIC value = 110.1211res1e5 = glm(out1 ~ ASIA_PO1 , family=binomial,
data=data1e)
logistic.display(res1e5)##
## Logistic regression predicting out1
##
## OR(95%CI) P(Wald's test) P(LR-test)
## ASIA_PO1 (cont. var.) 0.68 (0.36,1.29) 0.237 0.239
##
## Log-likelihood = -54.3607
## No. of observations = 96
## AIC value = 112.7213res1e6 = glm(out1 ~ Tokuhashi_cat , family=binomial,
data=data1e)
logistic.display(res1e6)##
## Logistic regression predicting out1
##
## OR(95%CI) P(Wald's test) P(LR-test)
## Tokuhashi_cat (cont. var.) 0.5 (0.25,1) 0.052 0.041
##
## Log-likelihood = -52.9635
## No. of observations = 96
## AIC value = 109.927res1f1 = glm(out1 ~ sex_code , family=binomial,
data=data1f)
logistic.display(res1f1)##
## Logistic regression predicting out1
##
## OR(95%CI) P(Wald's test) P(LR-test)
## sex_code: 1 vs 0 0.73 (0.21,2.48) 0.613 0.613
##
## Log-likelihood = -29.6839
## No. of observations = 48
## AIC value = 63.3678res1f2 = glm(out1 ~ age_code , family=binomial,
data=data1f)
logistic.display(res1f2)##
## Logistic regression predicting out1
##
## OR(95%CI) P(Wald's test) P(LR-test)
## age_code: 1 vs 0 0.84 (0.25,2.87) 0.784 0.784
##
## Log-likelihood = -29.7745
## No. of observations = 48
## AIC value = 63.549res1f = glm(out1 ~ age_code+sex_code+Tabagisme+AmbulationPO1+ASIA_PO1+Tokuhashi_cat , family=binomial, data=data1f) logistic.display(res1f)
res1f3 = glm(out1 ~ Tabagisme , family=binomial,
data=data1f)
logistic.display(res1f3)##
## Logistic regression predicting out1
##
## OR(95%CI) P(Wald's test) P(LR-test)
## Tabagisme: 1 vs 0 10.87 (2.5,47.28) 0.001 < 0.001
##
## Log-likelihood = -24.0592
## No. of observations = 48
## AIC value = 52.1184res1f4 = glm(out1 ~ AmbulationPO1 , family=binomial,
data=data1f)
logistic.display(res1f4)##
## Logistic regression predicting out1
##
## OR(95%CI) P(Wald's test) P(LR-test)
## AmbulationPO1 (cont. var.) 2.04 (0.67,6.27) 0.212 0.19
##
## Log-likelihood = -28.9538
## No. of observations = 48
## AIC value = 61.9077res1f5 = glm(out1 ~ ASIA_PO1 , family=binomial,
data=data1f)
logistic.display(res1f5)##
## Logistic regression predicting out1
##
## OR(95%CI) P(Wald's test) P(LR-test)
## ASIA_PO1 (cont. var.) 0.62 (0.2,1.91) 0.407 0.407
##
## Log-likelihood = -29.469
## No. of observations = 48
## AIC value = 62.938res1f6 = glm(out1 ~ Tokuhashi_cat , family=binomial,
data=data1f)
logistic.display(res1f6)##
## Logistic regression predicting out1
##
## OR(95%CI) P(Wald's test) P(LR-test)
## Tokuhashi_cat (cont. var.) 0.82 (0.38,1.74) 0.6 0.599
##
## Log-likelihood = -29.6738
## No. of observations = 48
## AIC value = 63.3476