问题
I am trying to run Cox proportional hazard model on a data of 4 groups. Here's the data:
I am using this code:
time_Allo_NHL<- c(28,32,49,84,357,933,1078,1183,1560,2114,2144)
censor_Allo_NHL<- c(rep(1,5), rep(0,6))
time_Auto_NHL<- c(42,53,57,63,81,140,176,210,252,476,524,1037)
censor_Auto_NHL<- c(rep(1,7), rep(0,1), rep(1,1), rep(0,1), rep(1,1), rep(0,1))
time_Allo_HOD<- c(2,4,72,77,79)
censor_Allo_HOD<- c(rep(1,5))
time_Auto_HOD<- c(30,36,41,52,62,108,132,180,307,406,446,484,748,1290,1345)
censor_Auto_HOD<- c(rep(1,7), rep(0,8))
myData <- data.frame(time=c(time_Allo_NHL, time_Auto_NHL, time_Allo_HOD, time_Auto_HOD),
censor=c(censor_Allo_NHL, censor_Auto_NHL, censor_Allo_HOD, censor_Auto_HOD),
group= rep(1:4,), each= )
str(myData)
The problem is each group has different number of observations. What I should modify in the code :
myData <- data.frame(time=c(time_Allo_NHL, time_Auto_NHL, time_Allo_HOD, time_Auto_HOD),
censor=c(censor_Allo_NHL, censor_Auto_NHL, censor_Allo_HOD,
censor_Auto_HOD), group= rep(1:4,), each= )
Instead of writing each=# so I can run the code properly in order to complete doing the Cox proportional hazard model?
Then I have attempted to run a Cox proportional hazard model using the following code:
library(survival)
for(i in 1:43){
if (myData$group[i]==2)
myData$Z1[i]<-1
else myData$Z1[i]<-0
}
for(i in 1:43){
if (myData$group[i]==3)
myData$Z2[i]<-1
else myData$Z2[i]<-0
}
for(i in 1:43){
if (myData$group[i]==4)
myData$Z3[i]<-1
else myData$Z3[i]<-0
}
myData
Coxfit<-coxph(Surv(time,censor)~Z1+Z2+Z3, data = myData)
summary(Coxfit)
This is all I got. There's no valuse!!
Next, I want to test for an interaction between type of transplant and disease type using main effects and interaction terms.
The code I'm going to use:
n<-length(myData$time)
n
for (i in 1:n){
if (myData$(here?)[i]==2)
myData$W1[i] <-1
else myData$W1[i]<-0
}
for (i in 1:n){
if (myData$(here?)[i]==2)
myData$W2[i] <-1
else myData$W2[i]<-0
}
myData
Coxfit.W<-coxph(Surv(time,censor)~W1+W2+W1*W2, data = myData)
summary(Coxfit.W)
I'm not sure what it should be written in here (myData$(here?) from the above code.
回答1:
This looks like the bone marrow transplant study at Ohio State University.
As you mentioned, each group has different numbers of observations per group. I would consider binding the rows from each subgroup together in the end.
First, would create a data frame for each group. I would add a column indicating which group they belonged to. So, for example, in df_Allo_NHL would have all of the observations have Allo NHL for group:
df_Allo_NHL <- data.frame(group = "Allo NHL",
time = c(28,32,49,84,357,933,1078,1183,1560,2114,2144),
censor = c(rep(1,5), rep(0,6)))
Or just adding to the 2 vectors you have already:
df_Allo_NHL <- data.frame(group = "Allo NHL", time = time_Allo_NHL, censor = censor_Allo_NHL)
Then once you have your 4 data frames, you can combine them. One way to do this is by using Reduce and putting all your data frames in a list. The final result should be ready for cox proportional hazards analysis, in long form, and you will have group available to include. (Edit: Z1 and Z2 added from table for model.)
time_Allo_NHL<- c(28,32,49,84,357,933,1078,1183,1560,2114,2144)
censor_Allo_NHL<- c(rep(1,5), rep(0,6))
df_Allo_NHL <- data.frame(group = "Allo NHL",
time = time_Allo_NHL,
censor = censor_Allo_NHL,
Z1 = c(90,30,40,60,70,90,100,90,80,80,90),
Z2 = c(24,7,8,10,42,9,16,16,20,27,5))
time_Auto_NHL<- c(42,53,57,63,81,140,176,210,252,476,524,1037)
censor_Auto_NHL<- c(rep(1,7), rep(0,1), rep(1,1), rep(0,1), rep(1,1), rep(0,1))
df_Auto_NHL <- data.frame(group = "Auto NHL",
time = time_Auto_NHL,
censor = censor_Auto_NHL,
Z1 = c(80,90,30,60,50,100,80,90,90,90,90,90),
Z2 = c(19,17,9,13,12,11,38,16,21,24,39,84))
time_Allo_HOD<- c(2,4,72,77,79)
censor_Allo_HOD<- c(rep(1,5))
df_Allo_HOD <- data.frame(group = "Allo HOD",
time = time_Allo_HOD,
censor = censor_Allo_HOD,
Z1 = c(20,50,80,60,70),
Z2 = c(34,28,59,102,71))
time_Auto_HOD<- c(30,36,41,52,62,108,132,180,307,406,446,484,748,1290,1345)
censor_Auto_HOD<- c(rep(1,7), rep(0,8))
df_Auto_HOD <- data.frame(group = "Auto HOD",
time = time_Auto_HOD,
censor = censor_Auto_HOD,
Z1 = c(90,80,70,60,90,70,60,100,100,100,100,90,90,90,80),
Z2 = c(73,61,34,18,40,65,17,61,24,48,52,84,171,20,98))
myData <- Reduce(rbind, list(df_Allo_NHL, df_Auto_NHL, df_Allo_HOD, df_Auto_HOD))
Edit
If you go ahead and also add Z1 (Karnofsky Score) and Z2 (waiting time from diagnosis to transplant), you can do the CPH survival model like this below. group is already a factor and the first level Allo NHL would by default be there reference category.
library(survival)
Coxfit<-coxph(Surv(time,censor)~group+Z1+Z2, data = myData)
summary(Coxfit)
Output
Call:
coxph(formula = Surv(time, censor) ~ group + Z1 + Z2, data = myData)
n= 43, number of events= 26
coef exp(coef) se(coef) z Pr(>|z|)
groupAuto NHL 0.77357 2.16748 0.58631 1.319 0.18704
groupAllo HOD 2.73673 15.43639 0.94081 2.909 0.00363 **
groupAuto HOD 1.06293 2.89485 0.63494 1.674 0.09412 .
Z1 -0.05052 0.95074 0.01222 -4.135 3.55e-05 ***
Z2 -0.01660 0.98354 0.01002 -1.656 0.09769 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
groupAuto NHL 2.1675 0.46136 0.6869 6.8395
groupAllo HOD 15.4364 0.06478 2.4419 97.5818
groupAuto HOD 2.8948 0.34544 0.8340 10.0481
Z1 0.9507 1.05181 0.9282 0.9738
Z2 0.9835 1.01674 0.9644 1.0030
Concordance= 0.783 (se = 0.059 )
Likelihood ratio test= 32.48 on 5 df, p=5e-06
Wald test = 28.48 on 5 df, p=3e-05
Score (logrank) test = 39.45 on 5 df, p=2e-07
Data
group time censor Z1 Z2
1 Allo NHL 28 1 90 24
2 Allo NHL 32 1 30 7
3 Allo NHL 49 1 40 8
4 Allo NHL 84 1 60 10
5 Allo NHL 357 1 70 42
6 Allo NHL 933 0 90 9
7 Allo NHL 1078 0 100 16
8 Allo NHL 1183 0 90 16
9 Allo NHL 1560 0 80 20
10 Allo NHL 2114 0 80 27
11 Allo NHL 2144 0 90 5
12 Auto NHL 42 1 80 19
13 Auto NHL 53 1 90 17
14 Auto NHL 57 1 30 9
15 Auto NHL 63 1 60 13
16 Auto NHL 81 1 50 12
17 Auto NHL 140 1 100 11
18 Auto NHL 176 1 80 38
19 Auto NHL 210 0 90 16
20 Auto NHL 252 1 90 21
21 Auto NHL 476 0 90 24
22 Auto NHL 524 1 90 39
23 Auto NHL 1037 0 90 84
24 Allo HOD 2 1 20 34
25 Allo HOD 4 1 50 28
26 Allo HOD 72 1 80 59
27 Allo HOD 77 1 60 102
28 Allo HOD 79 1 70 71
29 Auto HOD 30 1 90 73
30 Auto HOD 36 1 80 61
31 Auto HOD 41 1 70 34
32 Auto HOD 52 1 60 18
33 Auto HOD 62 1 90 40
34 Auto HOD 108 1 70 65
35 Auto HOD 132 1 60 17
36 Auto HOD 180 0 100 61
37 Auto HOD 307 0 100 24
38 Auto HOD 406 0 100 48
39 Auto HOD 446 0 100 52
40 Auto HOD 484 0 90 84
41 Auto HOD 748 0 90 171
42 Auto HOD 1290 0 90 20
43 Auto HOD 1345 0 80 98
来源：https://stackoverflow.com/questions/60919837/cox-proportional-hazard-model