r语言npsurv_R生存分析

library(stats)

library(survival)

## Information of data

data(package = "survival")

# List datasets in survival package

help(bladder1)  #

Description of data

head(bladder1)  # Show

first 6 rows

str(bladder1)  # Check

type of variables

summary(bladder1)  # Statistical summary

## Get the final data with nonzero

follow-up

bladder1$time

as.numeric(bladder1$stop -

bladder1$start)

summary(bladder1$time)

bladder1

subset(bladder1,status<=1 & time>0)

## Step1 Create Kaplan-Meier curve and

estimate median survial/event time

## The "log-log" confidence interval is

preferred.

## Create overval Kaplan-Meier

curve

km.as.one

status) ~ 1, data = bladder1,

conf.type =

"log-log")

## Create Kaplan-Meier curve stratified

by treatment

km.by.trt

status) ~ treatment, data =

bladder1,

conf.type =

"log-log")

## Show simple statistics of Kaplan-Meier

curve

km.as.one

km.by.trt

## See survival estimates at given time

(lots of outputs)

summary(km.as.one)

summary(km.by.trt)

## Plot Kaplan-Meier curve without any

specification

plot(km.as.one)

plot(km.by.trt)

## Plot Kaplan-Meier curve Without

confidence interval and mark of event

plot(km.as.one, conf = F, mark.time =

F)

plot(km.by.trt, conf = F, mark.time =

F)

## step2 Create a simple cox regression

and estimate HR:

model1

treatment + number +size,

data=bladder1)

## Model output

summary(model1)  # Output

summary information

confint(model1)  # Output

95% CI for the coefficients

exp(coef(model1))  # Output HR (exponentiated

coefficients)

exp(confint(model1))  # 95% CI

for exponentiated coefficients

predict(model1, type="risk")

#

predicted values

residuals(model1, type="deviance") #

residuals

## Step3 Check for violation of

proportional hazard (constant HR over time)

model1.zph

cox.zph(model1)

model1.zph

## Note: p value of treatmentthiotepa

<0.05

## GLOBAL p value is more impo