R|生存分析-结果整理

参考：https://mp.weixin.qq.com/s?__biz=MzIyNDI1MzgzOQ==&mid=2650393867&idx=1&sn=53cc6a3d57faf0ba30dfc3e1da6481b2&chksm=f01cafebc76b26fd5d3fe809d7868c448918336837c84d31d02627e0cefeb758551b5e21a4ad&scene=21#wechat_redirect

#载入所需的R包

library("survival")
library("survminer")

#载入并查看数据集

data("lung")head(lung)
 inst time status age sex ph.ecog ph.karno pat.karno meal.cal wt.loss1    3  306      2  74   1       1       90       100     1175      NA2    3  455      2  68   1       0       90        90     1225      153    3 1010      1  56   1       0       90        90       NA      154    5  210      2  57   1       1       90        60     1150      115    1  883      2  60   1       0      100        90       NA       06   12 1022      1  74   1       1       50        80      513       0

#cox 回归分析

res.cox <- coxph(Surv(time, status) ~ sex, data = lung)res.coxsummary(res.cox)Call:coxph(formula = Surv(time, status) ~ sex, data = lung)  n= 228, number of events= 165       coef exp(coef) se(coef)      z Pr(>|z|)   sex -0.5310    0.5880   0.1672 -3.176  0.00149 **---Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1    exp(coef) exp(-coef) lower .95 upper .95sex     0.588      1.701    0.4237     0.816Concordance= 0.579  (se = 0.022 )Rsquare= 0.046   (max possible= 0.999 )Likelihood ratio test= 10.63  on 1 df,   p=0.001111Wald test            = 10.09  on 1 df,   p=0.001491Score (logrank) test = 10.33  on 1 df,   p=0.001312

         COX回归的结果中需要提取HR，HR的置信区间，wald.test和 p.value的信息，最简单的是在summary结果中进行复制粘贴，当然效率很低。假设当变量成百上前后，会发生什么呢？

还可以构建自定义函数，数据框的形式一次输出所有变量的COX回归结果

#查看待分析的变量

covariates <- names(lung[,4:10])covariates[1] "age"       "sex"       "ph.ecog"   "ph.karno"  "pat.karno" "meal.cal"  "wt.loss"

#构建自定义函数，以数据框形式输出结果

univ_formulas <- sapply(covariates,                        function(x) as.formula(paste('Surv(time, status)~', x)))

 #设定函数输出的信息                  

univ_models <- lapply( univ_formulas, function(x){coxph(x, data = lung)})# Extract data univ_results <- lapply(univ_models,                       function(x){                           x <- summary(x)                          p.value<-signif(x$wald["pvalue"], digits=2)                          wald.test<-signif(x$wald["test"], digits=2)                          beta<-signif(x$coef[1], digits=2);#coeficient beta                          HR <-signif(x$coef[2], digits=2);#exp(beta)                          HR.confint.lower <- signif(x$conf.int[,"lower .95"], 2)                          HR.confint.upper <- signif(x$conf.int[,"upper .95"],2)                          HR <- paste0(HR, " (",                                        HR.confint.lower, "-", HR.confint.upper, ")")                          res<-c(beta, HR, wald.test, p.value)                          names(res)<-c("beta", "HR (95% CI for HR)", "wald.test",                                         "p.value")                          return(res)                          #return(exp(cbind(coef(x),confint(x))))                         })

#输出所有变量的COX结果

res <- t(as.data.frame(univ_results, check.names = FALSE))as.data.frame(res)
              beta HR (95% CI for HR) wald.test p.valueage          0.019            1 (1-1)       4.1   0.042sex          -0.53   0.59 (0.42-0.82)        10  0.0015ph.ecog       0.48        1.6 (1.3-2)        18 2.7e-05ph.karno    -0.016      0.98 (0.97-1)       7.9   0.005pat.karno    -0.02   0.98 (0.97-0.99)        13 0.00028meal.cal  -0.00012            1 (1-1)      0.29    0.59wt.loss     0.0013         1 (0.99-1)      0.05    0.83

OK！可以write了，至于csv还是txt ，啦意随。。。