（烧伤）数据的 Cox 比例风险模型

Question

On a study is described which evaluates a protocol change in disinfectant practices in a large midwestern university medical center. Of primary interest in the study is a comparison of two methods of body cleansing. The first method, used exclusively from January 1983 to June 1984, consisted of a routine bathing care method (initial surface decontamination with 10% povidone-iodine followed with regular bathing with Dial soap). From June 1984 to the end of the study period in December1985, body cleansing was initially performedusing 4% chlorhexidine gluconate. Eighty-four patients were in the group who received the new bathing solution, chlorhexidine, and 70 patients served as the control group who received routine bathing care, povidoneiodine. Included in the data set is a covariate that measures the total surface area burned. The data is (burn). I want to test for: 1- any difference in survival functions for the two groups. 2- any difference in survival functions for the two groups adjusting for total area burned.

library(KMsurv)
data()
data(burn)
burn

library(survival)

I know that the function that would be used is coxph() , but I'm not sure which groups that I should test for (from the above information). Are they T1 and D2? so that for 1, Coxfit1<-coxph(Surv(T1,D2)~group, data = burn) ? and for 2, Coxfit2<-coxph(Surv(T1,D2)~Z4, data = burn) ?

What is this code doing?

for(i in 1:154){
  if (burn$??[i]==2)
    burn$Z1[i]<-1
  else burn$Z1[i]<-0
}

for(i in 1:154){
  if (burn$??[i]==3)
    burn$Z2[i]<-1
  else burn$Z2[i]<-0
}

Answer 1

For question 1, you want to test the survival distributions* between the levels of the Z1 variable. There is no variable called group in the dataset. Z1=0 means routine bathing and Z1=1 means body cleansing. You may want to convert all Z variables to factors before continuing further (except Z4).

library(survival)
library(KMsurv)
library (dplyr)

burn$Z1 <- factor(burn$Z1, label=c("Routine bathing", "Body cleansing"))

* The word survival needs some clarification. Presumably it is time until first straphylocous aureaus infection (D3) or on study time if no event occurred. The time is in variable T3.

The command to perform the test is:

coxph(Surv(T3,D3) ~ Z1, data=burn)
                    coef exp(coef) se(coef)      z      p
Z1Body cleansing -0.5614    0.5704   0.2934 -1.914 0.0557

For question 2, Z4 contains the percentage of total surface area burned, the variable to adjust for.

coxph(Surv(T3,D3)~Z1+Z4, data=burn)

                      coef exp(coef)  se(coef)      z     p
Z1Body cleansing -0.524764  0.591695  0.295769 -1.774 0.076
Z4                0.007248  1.007275  0.007145  1.015 0.310

So there appears to be no difference in time until first infection between those who were given routine bathing vs body cleansing.

Answer 2

??burn tells you what the variables mean; Z1 and Z4 seem to be what you are after:

This data frame contains the following columns:

Obs Observation number

Z1 Treatment: 0-routine bathing 1-Body cleansing

Z2 Gender (0=male 1=female)

Z3 Race: 0=nonwhite 1=white

Z4 Percentage of total surface area burned

Z5 Burn site indicator: head 1=yes, 0=no

Z6 Burn site indicator: buttock 1=yes, 0=no

Z7 Burn site indicator: trunk 1=yes, 0=no

Z8 Burn site indicator: upper leg 1=yes, 0=no

Z9 Burn site indicator: lower leg 1=yes, 0=no

Z10 Burn site indicator: respiratory tract 1=yes, 0=no

Z11 Type of burn: 1=chemical, 2=scald, 3=electric, 4=flame

T1 Time to excision or on study time

D1 Excision indicator: 1=yes 0=no

T2 Time to prophylactic antibiotic treatment or on study time

D2 Prophylactic antibiotic treatment: 1=yes 0=no

T3 Time to straphylocous aureaus infection or on study time

D3 Straphylocous aureaus infection: 1=yes 0=no

Source Klein and Moeschberger (1997) Survival Analysis Techniques for Censored and truncated data, Springer. Ichida et al. Stat. Med. 12 (1993): 301-310.

Edit: In your case, there is a significant difference between routine bathing and body cleansing (Z1), but Percentage of total surface area burned (Z4) is not significant in a univariate analysis.

library(KMsurv)
library(survival)
library(survminer)
#> Loading required package: ggplot2
#> Loading required package: ggpubr
#> Loading required package: magrittr
data(burn)

## Univariate Cox regression analysis to see whether Z1 and Z4 are significant:
res.cox <- coxph(Surv(T1, D1) ~ Z1, data = burn)
summary(res.cox)
#> Call:
#> coxph(formula = Surv(T1, D1) ~ Z1, data = burn)
#> 
#>   n= 154, number of events= 99 
#> 
#>      coef exp(coef) se(coef)     z Pr(>|z|)   
#> Z1 0.5504    1.7339   0.2072 2.656   0.0079 **
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#>    exp(coef) exp(-coef) lower .95 upper .95
#> Z1     1.734     0.5767     1.155     2.602
#> 
#> Concordance= 0.599  (se = 0.027 )
#> Likelihood ratio test= 7.24  on 1 df,   p=0.007
#> Wald test            = 7.06  on 1 df,   p=0.008
#> Score (logrank) test = 7.23  on 1 df,   p=0.007
ggsurvplot(surv_fit(Surv(T1, D1) ~ Z1, data = burn), data = burn,
           conf.int = TRUE, pval = TRUE)

res.cox <- coxph(Surv(T1, D1) ~ Z4, data = burn)
summary(res.cox)
#> Call:
#> coxph(formula = Surv(T1, D1) ~ Z4, data = burn)
#> 
#>   n= 154, number of events= 99 
#> 
#>         coef exp(coef)  se(coef)      z Pr(>|z|)
#> Z4 -0.005108  0.994905  0.005408 -0.945    0.345
#> 
#>    exp(coef) exp(-coef) lower .95 upper .95
#> Z4    0.9949      1.005    0.9844     1.006
#> 
#> Concordance= 0.529  (se = 0.034 )
#> Likelihood ratio test= 0.94  on 1 df,   p=0.3
#> Wald test            = 0.89  on 1 df,   p=0.3
#> Score (logrank) test = 0.89  on 1 df,   p=0.3

## Multivariate Cox regression analysis to see whether Z1 and Z4 remain significant
## here, univariate Z4 was n.s., so not that relevant...
res.cox <- coxph(Surv(T1, D1) ~ Z1 + Z4, data = burn)
summary(res.cox)
#> Call:
#> coxph(formula = Surv(T1, D1) ~ Z1 + Z4, data = burn)
#> 
#>   n= 154, number of events= 99 
#> 
#>         coef exp(coef)  se(coef)      z Pr(>|z|)  
#> Z1  0.534232  1.706138  0.208651  2.560   0.0105 *
#> Z4 -0.003458  0.996548  0.005435 -0.636   0.5246  
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#>    exp(coef) exp(-coef) lower .95 upper .95
#> Z1    1.7061     0.5861     1.133     2.568
#> Z4    0.9965     1.0035     0.986     1.007
#> 
#> Concordance= 0.606  (se = 0.033 )
#> Likelihood ratio test= 7.66  on 2 df,   p=0neither.02
#> Wald test            = 7.44  on 2 df,   p=0.02
#> Score (logrank) test = 7.61  on 2 df,   p=0.02