很多时候，在用中位数/均数、四分位数等特殊位置为截点时，生存分析往往没有差异。这个时候常常需要找到一个最佳截点使得生存分析有差异。常用的选取生存分析的最佳截断有很多种方法，如X-tile软件、R的CatPredi包、cutoff包。今天给大家介绍的是survminer 包的surv_cutpoint()函数。

载入示例数据

# 0. Load some data
library(survival)
library(survminer)
data(myeloma)
head(myeloma)
> head(myeloma)
         molecular_group chr1q21_status treatment event  time   CCND1
GSM50986      Cyclin D-1       3 copies       TT2     0 69.24  9908.4
GSM50988      Cyclin D-2       2 copies       TT2     0 66.43 16698.8
GSM50989           MMSET       2 copies       TT2     0 66.50   294.5
GSM50990           MMSET       3 copies       TT2     1 42.67   241.9
GSM50991             MAF           <NA>       TT2     0 65.00   472.6
GSM50992    Hyperdiploid       2 copies       TT2     0 65.20   664.1
         CRIM1 DEPDC1    IRF4   TP53   WHSC1
GSM50986 420.9  523.5 16156.5   10.0   261.9
GSM50988  52.0   21.1 16946.2 1056.9   363.8
GSM50989 617.9  192.9  8903.9 1762.8 10042.9
GSM50990  11.9  184.7 11894.7  946.8  4931.0
GSM50991  38.8  212.0  7563.1  361.4   165.0
GSM50992  16.9  341.6 16023.4 2096.3   569.2

计算最佳截点

# 1. Determine the optimal cutpoint of variables
res.cut <- surv_cutpoint(myeloma, #数据集
                         time = "time", #生存状态
                         event = "event", #生存时间
                         variables = c("DEPDC1", "WHSC1", "CRIM1") #需要计算的数据列名
                         )
 
summary(res.cut) #查看数据最佳截断点及统计量
> summary(res.cut)
       cutpoint statistic
DEPDC1    279.8  4.275452
WHSC1    3205.6  3.361330
CRIM1      82.3  1.968317

展示数据分布

# 2. Plot cutpoint for DEPDC1
# 以DEPDC1为例
plot(res.cut, "DEPDC1", palette = "npg")

根据截点分类数据

# 3. Categorize variables
res.cat <- surv_categorize(res.cut)
head(res.cat)
> head(res.cat)
          time event DEPDC1 WHSC1 CRIM1
GSM50986 69.24     0   high   low  high
GSM50988 66.43     0    low   low   low
GSM50989 66.50     0    low  high  high
GSM50990 42.67     1    low  high   low
GSM50991 65.00     0    low   low   low
GSM50992 65.20     0   high   low   low

绘制生存曲线

fit <- survfit(Surv(time, event) ~DEPDC1, data = res.cat)#拟合生存分析
#绘制生存曲线并显示P值
ggsurvplot(fit,
           data = res.cat,
           risk.table = TRUE,
           pval = T)

参考

surv_cutpoint: Determine the Optimal Cutpoint for Continuous Variables in survminer: Drawing Survival Curves using ‘ggplot2’ (rdrr.io)