R Notebook

Our Required Library

# Load required packages
library(survival)
library(ggplot2)
library(ggpubr)
library(survminer)
library(dplyr)
library(foreign)

Call Main DataSet

# Import the dataset and have a look at it
medical<- read.spss("AKI HD Outcome MMCH.sav")
medical<- as.data.frame(medical)
glimpse(medical)

create new dataset

class(time_status_gender)

[1] "data.frame"

Record the gender variable

time_status_gender$sex[time_status_gender$sex==1]<- "Male"
time_status_gender$sex[time_status_gender$sex==2]<- "Female"

Creating model for Survival probability

summary(fit1)

Call: survfit(formula = Surv(time_status_gender$time, time_status_gender$death) ~ 
    time_status_gender$sex, type = "kaplan-meier")

                time_status_gender$sex=Female 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    3     58       1    0.983  0.0171        0.950        1.000
    4     57       2    0.948  0.0291        0.893        1.000
    7     50       1    0.929  0.0341        0.865        0.999
    9     46       1    0.909  0.0389        0.836        0.989
   16     43       1    0.888  0.0434        0.807        0.977
   32     39       1    0.865  0.0479        0.776        0.964
   50     37       1    0.842  0.0520        0.746        0.950
  180     33      15    0.459  0.0783        0.329        0.641

                time_status_gender$sex=Male 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    4     66       1    0.985  0.0150        0.956        1.000
    6     65       1    0.970  0.0211        0.929        1.000
    7     64       1    0.955  0.0256        0.906        1.000
   10     62       1    0.939  0.0295        0.883        0.999
   17     60       1    0.923  0.0329        0.861        0.990
  180     53      21    0.558  0.0651        0.443        0.701

Visualizing the Kaplane Meire Survial Curve

Normal plot