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Contents

1
2
3
4 譟危S(t), h(t)
5 :譟危
6 :
7
8 interval
9 AUC(area under the curve)
10 ctree
11 survfit 蟆郁骸襯 data.frame 朱
12 谿瑚襭



1 #

譟 覿 譯 蟯
  • 譟 (survival function) 豢
  • 譟 (hazard function) レ 譯朱 螻給(corvarate) 豸 覲襯 谿場伎 蠏 郁 襯 蟲螻 .

譴 襭
  • 一: t蟾讌襷 襭 襯 .
    • 郁規 譬襭煙 伎襦 t蟾讌襷 襭襯 蟯豸″. 覈 襭螳 一襭
    • 覩碁Μ 企 蟇 覦襯 . 覈 襭螳 一襭伎襷, 語 蟯豸′ 譬襭讌 覈襴.
    • 一襭
      • 譴(administrative censoring): 郁規螳 譬襭 蟾讌 螳 殊企讌 .
      • 豢 ろ(loss to follow up)
      • 蟆曙 覈(competing risk): るジ 譬襯 蟇 覦朱 誤 蟯 蟇伎 譴 蟆曙


2 #

3 #

  • 伎讌 譯曙讌 覈襯企 襭.
  • 讌襷 伎 轟蟾讌 一危磯 .
  • 5 伎 誤朱 覯瑚 覦 譟危語 企れ 蟆曙

4 譟危S(t), h(t) #

  • 譟危 - t 伎 襯
  • - t 伎讌襷 螻 譯曙 襯

5 :譟危 #

normally
  • 0=alive
  • 1=dead
  • Other choices TRUE/FALSE (TRUE = death) or 1/2 (2=death)

For interval censored data
  • 0=right censored
  • 1=event at time
  • 2=left censored
  • 3=interval censored

time 殊. status=1企 譯曙, status=0企 譟伎企. gender=1企 , gender=0企 .

library("RODBC")
conn <- odbcConnect("192.168.201.36",uid="id", pwd="pw")
x <-  sqlQuery(conn, "select * from ods.dbo.v_h")

#焔覲襦 谿願 螳?
out <- survfit(Surv(time, status==1) ~ gender, data=x)
plot(out, lty=1:2, col=c("red", "blue"))
survdiff(Surv(time, status==1) ~ gender, data=x)

s01.png

survdiff(Surv(time, status==1) ~ gender, data=x) 蟆郁骸 れ螻 螳. survdiff 譟危襯 視.
> survdiff(Surv(time, status==1) ~ gender, data=x)
Call:
survdiff(formula = Surv(time, status == 1) ~ gender, data = x)

            N Observed Expected (O-E)^2/E (O-E)^2/V
gender=0  881      804      852      2.70      6.14
gender=1 1142     1054     1006      2.28      6.14

 Chisq= 6.1  on 1 degrees of freedom, p= 0.0132 
p-value螳 0.0132襦 譴 0.05 蠏覓願 蠍郁, 襴所 豈. 讀, 焔覲襦 谿願 誤 . summary(out) 蟆郁骸襯 覲伎.
> summary(out)
Call: survfit(formula = Surv(time, status == 1) ~ gender, data = x)

                gender=0 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    1    881     281   0.6810 0.01570       0.6510       0.7125
    2    525      34   0.6369 0.01641       0.6056       0.6699
    3    491      25   0.6045 0.01680       0.5725       0.6384
    4    466       9   0.5928 0.01692       0.5606       0.6269
    5    457      13   0.5760 0.01708       0.5435       0.6104
    6    444      10   0.5630 0.01718       0.5303       0.5977
    7    434       9   0.5513 0.01726       0.5185       0.5862
    8    425       8   0.5409 0.01732       0.5080       0.5760
    9    417       5   0.5345 0.01735       0.5015       0.5696
   10    412       2   0.5319 0.01736       0.4989       0.5670
   11    410       8   0.5215 0.01741       0.4885       0.5567
   12    402       5   0.5150 0.01743       0.4819       0.5503
   13    397       4   0.5098 0.01745       0.4767       0.5452
   14    393       5   0.5033 0.01747       0.4702       0.5387
   15    388       8   0.4929 0.01749       0.4598       0.5284
   16    380       3   0.4891 0.01749       0.4559       0.5246
   17    377       4   0.4839 0.01750       0.4508       0.5194
   18    373       4   0.4787 0.01750       0.4456       0.5142
   19    369       1   0.4774 0.01750       0.4443       0.5129
   20    368       2   0.4748 0.01750       0.4417       0.5104
   21    366       2   0.4722 0.01750       0.4391       0.5078
   22    364       2   0.4696 0.01750       0.4365       0.5052
   23    362       3   0.4657 0.01750       0.4326       0.5013
   24    359       3   0.4618 0.01750       0.4288       0.4974
.
.
.


               gender=1 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    1   1142     437   0.6173 0.01438       0.5898       0.6462
    2    618      57   0.5604 0.01490       0.5319       0.5904
    3    561      24   0.5364 0.01505       0.5077       0.5667
    4    537      20   0.5164 0.01514       0.4876       0.5470
    5    517       7   0.5095 0.01516       0.4806       0.5400
    6    510      10   0.4995 0.01519       0.4706       0.5301
    7    500      11   0.4885 0.01521       0.4596       0.5192
    8    489       6   0.4825 0.01522       0.4536       0.5133
    9    483      11   0.4715 0.01523       0.4426       0.5023
   10    472       9   0.4625 0.01523       0.4336       0.4933
   11    463       4   0.4585 0.01523       0.4296       0.4894
   12    459      11   0.4475 0.01522       0.4187       0.4784
   13    448       4   0.4435 0.01522       0.4147       0.4744
   14    444       7   0.4365 0.01520       0.4077       0.4674
   15    437       6   0.4305 0.01519       0.4018       0.4614
   16    431       7   0.4235 0.01517       0.3948       0.4544
   17    424       5   0.4186 0.01516       0.3899       0.4493
   18    419       9   0.4096 0.01512       0.3810       0.4403
   19    410       2   0.4076 0.01512       0.3790       0.4383
   20    408       5   0.4026 0.01510       0.3740       0.4333
   21    403       5   0.3976 0.01507       0.3691       0.4282
   22    398       4   0.3936 0.01505       0.3652       0.4242
.
.
.
螳 覲襦 譯曙 襯 . 5殊姶蟾讌 伎 襯 0.5760願, 0.5095.

6 : #

cox regression 企.
> summary(coxph(Surv(time, status==1) ~ gender, data=x))

Call:
coxph(formula = Surv(time, status == 1) ~ gender, data = x)

  n= 607, number of events= 605 

          coef exp(coef) se(coef)    z Pr(>|z|)
gender 0.10346   1.10900  0.08214 1.26    0.208

       exp(coef) exp(-coef) lower .95 upper .95
gender     1.109     0.9017    0.9441     1.303

Concordance= 0.527  (se = 0.033 )
Rsquare= 0.003   (max possible= 1 )
Likelihood ratio test= 1.59  on 1 df,   p=0.2069
Wald test            = 1.59  on 1 df,   p=0.2078
Score (logrank) test = 1.59  on 1 df,   p=0.2076
觜 螳襷(hazard)螳 1.10900覦 . p-value螳 0.208襦 讌 .

7 #

out2 <- survfit(Surv(suvival_time, status==1) ~ mm, data=m)
summary(out2)
col <- colors()[c(26,31,33,51,76,153)] 
plot(out2, lty=1:2, col=col)
legend("topright", levels(m$mm), col=col, text.col=col)

col <- colors()[c(26,31,33,36,50,62,70,128,142,258,367,477,275)]
plot(fit2, col=col, lwd=1)
legend("topright", levels(sur$螳), col=col, text.col=col)

8 interval #

library(survival)

x <- df[df$genre == "殊",]
out <- survfit(Surv(begin_dt, end_dt, event, type="interval") ~ nation, data=df)
col <- colors()[c(26,31,33,36,50,62,70,128,142,258,367,477,275)]
plot(out, col=col)
legend("topright", levels(df$nation), col=col, text.col=col)


s <- summary(out)
data.frame(nation=gsub("nation=", "", s$strata), time=s$time, n.risk=s$n.risk, n.event=s$n.event, survival=s$surv)

9 AUC(area under the curve) #

#AUC
install.packages("pracma")
require(pracma)
out <- survfit(Surv(time=diff, event=event, type="right") ~ nation, data=x)
rs <- data.frame(time=summary(out)$time, surv=summary(out)$surv, nation=gsub("nation=", "",summary(out)$strata))
trapz(rs[rs$nation=="譴蟲",]$time, rs[rs$nation=="譴蟲",]$surv)

10 ctree #

library(party)
library(survival)

data("GBSG2", package = "TH.data")
fit <- ctree(Surv(time, cens) ~ ., data = GBSG2)
plot(fit)

11 survfit 蟆郁骸襯 data.frame 朱 #

https://github.com/kmiddleton/rexamples/blob/master/qplot_survival.R
createSurvivalFrame <- function(f.survfit){
  # initialise frame variable
  f.frame <- NULL
  
  # check if more then one strata
  if(length(names(f.survfit$strata)) == 0){
    # create data.frame with data from survfit
    f.frame <- data.frame(time=f.survfit$time, 
                          n.risk=f.survfit$n.risk, 
                          n.event=f.survfit$n.event, 
                          n.censor = f.survfit$n.censor, 
                          surv=f.survfit$surv, 
                          upper=f.survfit$upper, 
                          lower=f.survfit$lower)
    # create first two rows (start at 1)
    f.start <- data.frame(time=c(0, f.frame$time[1]), 
                          n.risk=c(f.survfit$n, f.survfit$n), 
                          n.event=c(0,0), 
                          n.censor=c(0,0), 
                          surv=c(1,1), 
                          upper=c(1,1), 
                          lower=c(1,1)) 
    # add first row to dataset
    f.frame <- rbind(f.start, f.frame)
    
    # remove temporary data
    rm(f.start)
  } 
  else {
    # create vector for strata identification
    f.strata <- NULL
    for(f.i in 1:length(f.survfit$strata)){
      # add vector for one strata according to number of rows of strata
      f.strata <- c(f.strata, rep(names(f.survfit$strata)[f.i], f.survfit$strata[f.i]))
    }
    
    # create data.frame with data from survfit (create column for strata)
    f.frame <- data.frame(time=f.survfit$time, n.risk=f.survfit$n.risk, n.event=f.survfit$n.event, n.censor = f.survfit$n.censor, surv=f.survfit$surv, upper=f.survfit$upper, lower=f.survfit$lower, strata=factor(f.strata))
    
    # remove temporary data
    rm(f.strata)
    
    # create first two rows (start at 1) for each strata
    for(f.i in 1:length(f.survfit$strata)){
      
      # take only subset for this strata from data
      f.subset <- subset(f.frame, strata==names(f.survfit$strata)[f.i])
      
      # create first two rows (time: 0, time of first event)
      f.start <- data.frame(time=c(0, f.subset$time[1]), n.risk=rep(f.survfit[f.i]$n, 2), n.event=c(0,0), n.censor=c(0,0), surv=c(1,1), upper=c(1,1), lower=c(1,1), strata=rep(names(f.survfit$strata)[f.i],2))    
      
      # add first two rows to dataset
      f.frame <- rbind(f.start, f.frame)
      
      # remove temporary data
      rm(f.start, f.subset)
      
    }
    
    # reorder data
    f.frame <- f.frame[order(f.frame$strata, f.frame$time), ]
    
    # rename row.names
    rownames(f.frame) <- NULL   
  }
  # return frame
  return(f.frame)
}

12 谿瑚襭 #


覘 谿城り 覲企 れ 危碁 る 蟆曙郁 襷給. 譬 襭 螻旧 譯殊 螳. -- レ危 2014-11-11 10:16:39

蠏碁郁~ -- 伎 2014-11-12 16:14:52
蠍 蠍郁鍵..
企: : るジ讓曙 襦螻豺 企Ν 譯殊語. 襦螻豺
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