Survival analysis for giloma on sex

library("survival")
library("coin")
library(survminer)
## Loading required package: ggplot2
## Loading required package: ggpubr
## Loading required package: magrittr
## Warning: Installed Rcpp (0.12.11) different from Rcpp used to build dplyr (0.12.12).
## Please reinstall dplyr to avoid random crashes or undefined behavior.
library(ggplot2)
library(ggfortify)
data(glioma)
build survival objects for kaplan meier graph
so1<-Surv(time,event) ~ sex
survo<-Surv(time=glioma$time, event=glioma$event)
survf<- survfit(formula=survo~glioma$sex,data=glioma)
summary(survf)
## Call: survfit(formula = survo ~ glioma$sex, data = glioma)
## 
##                 glioma$sex=Female 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##     5     16       1    0.938  0.0605        0.826        1.000
##     8     15       3    0.750  0.1083        0.565        0.995
##     9     12       1    0.688  0.1159        0.494        0.957
##    14     11       2    0.562  0.1240        0.365        0.867
##    25      9       1    0.500  0.1250        0.306        0.816
##    53      3       1    0.333  0.1596        0.130        0.852
## 
##                 glioma$sex=Male 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##     6     21       1    0.952  0.0465        0.866        1.000
##     8     20       1    0.905  0.0641        0.788        1.000
##    11     19       1    0.857  0.0764        0.720        1.000
##    12     18       1    0.810  0.0857        0.658        0.996
##    13     17       1    0.762  0.0929        0.600        0.968
##    14     16       1    0.714  0.0986        0.545        0.936
##    15     15       1    0.667  0.1029        0.493        0.902
##    19     14       1    0.619  0.1060        0.443        0.866
##    20     13       1    0.571  0.1080        0.395        0.828
##    25     12       1    0.524  0.1090        0.348        0.788
##    31     11       1    0.476  0.1090        0.304        0.746
##    32     10       1    0.429  0.1080        0.262        0.702
##    34      9       1    0.381  0.1060        0.221        0.657
##    36      8       1    0.333  0.1029        0.182        0.610
plot(survf,lty=1:2, main="Glioma sex",log=TRUE, ylab="Probability",xlab="Survival Time")

autoplot(survf)

####Survival analysis for giloma on histology

so2<-Surv(time,event) ~ histology
survo1<-Surv(time=glioma$time, event=glioma$event)
survf1<- survfit(formula=survo1~glioma$histology,data=glioma)
summary(survf1)
## Call: survfit(formula = survo1 ~ glioma$histology, data = glioma)
## 
##                 glioma$histology=GBM 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##     5     20       1     0.95  0.0487       0.8591        1.000
##     6     19       1     0.90  0.0671       0.7777        1.000
##     8     18       4     0.70  0.1025       0.5254        0.933
##    11     14       1     0.65  0.1067       0.4712        0.897
##    12     13       1     0.60  0.1095       0.4195        0.858
##    13     12       1     0.55  0.1112       0.3700        0.818
##    14     11       3     0.40  0.1095       0.2339        0.684
##    15      8       1     0.35  0.1067       0.1926        0.636
##    20      7       1     0.30  0.1025       0.1536        0.586
##    25      6       1     0.25  0.0968       0.1170        0.534
##    31      5       1     0.20  0.0894       0.0832        0.481
##    36      4       1     0.15  0.0798       0.0528        0.426
## 
##                 glioma$histology=Grade3 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##     9     17       1    0.941  0.0571        0.836        1.000
##    19     16       1    0.882  0.0781        0.742        1.000
##    25     15       1    0.824  0.0925        0.661        1.000
##    32     13       1    0.760  0.1048        0.580        0.996
##    34     12       1    0.697  0.1136        0.506        0.959
##    53      7       1    0.597  0.1341        0.385        0.927
plot(survf1,lty=1:2, main="Glioma Histology",log=TRUE, ylab="Probability",xlab="Survival Time")

autoplot(survf1)

Survival analysis for GBSG2 on horTH

library(TH.data)
## Loading required package: MASS
## 
## Attaching package: 'TH.data'
## The following object is masked from 'package:MASS':
## 
##     geyser
data(GBSG2)
summary(GBSG2)
##  horTh          age        menostat       tsize        tgrade   
##  no :440   Min.   :21.00   Pre :290   Min.   :  3.00   I  : 81  
##  yes:246   1st Qu.:46.00   Post:396   1st Qu.: 20.00   II :444  
##            Median :53.00              Median : 25.00   III:161  
##            Mean   :53.05              Mean   : 29.33            
##            3rd Qu.:61.00              3rd Qu.: 35.00            
##            Max.   :80.00              Max.   :120.00            
##      pnodes         progrec           estrec             time       
##  Min.   : 1.00   Min.   :   0.0   Min.   :   0.00   Min.   :   8.0  
##  1st Qu.: 1.00   1st Qu.:   7.0   1st Qu.:   8.00   1st Qu.: 567.8  
##  Median : 3.00   Median :  32.5   Median :  36.00   Median :1084.0  
##  Mean   : 5.01   Mean   : 110.0   Mean   :  96.25   Mean   :1124.5  
##  3rd Qu.: 7.00   3rd Qu.: 131.8   3rd Qu.: 114.00   3rd Qu.:1684.8  
##  Max.   :51.00   Max.   :2380.0   Max.   :1144.00   Max.   :2659.0  
##       cens       
##  Min.   :0.0000  
##  1st Qu.:0.0000  
##  Median :0.0000  
##  Mean   :0.4359  
##  3rd Qu.:1.0000  
##  Max.   :1.0000
GBSG2$event<- 1-GBSG2$cens
survo2<-Surv(time=GBSG2$time, event=GBSG2$event)
survf2<- survfit(formula=survo2~GBSG2$horTh, data=GBSG2)
s<-summary(survf2)
plot(survf2, lty=1:2, main="GBSG2 horTh survival analysis", log=TRUE,
     ylab="Probability", xlab="Survival TIME")

autoplot(survf2)