Basic usage guide for survminer
Get all the code and sample data in this article:
survReceive a reply from the public account in the background.
Survival analysis is a type of statistical analysis method that combines the outcome of an event with the time elapsed for that outcome to occur. This type of method is also known as time-to-event analysis, considering not only whether an event occurs, but also how long it has occurred. surminer The R package provides functions to facilitate survival analysis and visualization.
install and load
install.packages("survminer")
library("survminer")
ggsurvplot: plot survival curves
#调用survival包
require("survival")
#survival包自带肺癌数据集：lung，查看数据样式
head(lung)
> head(lung)
inst time status age sex ph.ecog ph.karno pat.karno meal.cal wt.loss
1 3 306 2 74 1 1 90 100 1175 NA
2 3 455 2 68 1 0 90 90 1225 15
3 3 1010 1 56 1 0 90 90 NA 15
4 5 210 2 57 1 1 90 60 1150 11
5 1 883 2 60 1 0 100 90 NA 0
6 12 1022 1 74 1 1 50 80 513 0
#lung数据集：NCCTG晚期肺癌患者的生存率。
inst # 机构代码；
time # 生存天数；
status # 生存状态，1为删失，2为死亡；
age # 年龄；
sex # 性别，1为男性，2为女性；
ph.ecog、ph.karno、pat.karno # 为病人和患者评分，这里用不到；
meal.cal # 进食时消耗的卡路里；
wt.loss # 最近6个月内的体重下降。
Plot ungrouped survival curves
#利用survival包的Surv函数对时间及生存状态进行拟合(Kaplan-Meier法)
fit1 <- survfit(Surv(time, status) ~ 1, data = lung)
#绘制生存曲线
ggsurvplot(fit, palette = "#2E9FDF") #颜色自定
Plot the survival curves of the two groups
#以sex进行分组为例
#基础图形
fit <- survfit(Surv(time, status) ~ sex, data = lung)
ggsurvplot(fit, data = lung)
#更改删失点形状及大小，默认为"+", 可选"|"
ggsurvplot(fit, data = lung, censor.shape="|", censor.size = 4)
# Use brewer color palette "Dark2"
ggsurvplot(fit, linetype = "strata",
conf.int = TRUE, pval = TRUE,
palette = "Dark2")
# Use grey palette
ggsurvplot(fit, linetype = "strata",
conf.int = TRUE, pval = TRUE,
palette = "grey")
#绘制累计风险曲线
ggsurvplot(fit, data = lung,
conf.int = TRUE, # 增加置信区间
fun = "cumhaz") # 绘制累计风险曲线
#添加总患者生存曲线
ggsurvplot(fit, # 创建的拟合对象
data = lung, # 指定变量数据来源
conf.int = TRUE, # 显示置信区间
pval = TRUE, # 添加P值
surv.median.line = "hv", # 添加中位生存时间线
add.all = TRUE) # 添加总患者生存曲线
#个性化的生存曲线
ggsurvplot(
fit,
data = lung,
size = 1, # 线条大小
palette =
c("#E7B800", "#2E9FDF"),# 分组颜色
conf.int = TRUE, # 是否添加置信区间
pval = TRUE, # 是否添加p值
risk.table = TRUE, # 是否添加风险表
risk.table.col = "strata",# 分线表颜色
legend.labs =
c("Male", "Female"), # 图例标签
risk.table.height = 0.25, # 生存曲线图下所有生存表的高度，数值0-1之间
ggtheme = theme_bw() # 主题
)
#更个性化的生存曲线
ggsurvplot(
fit,
data = lung,
risk.table = TRUE,
pval = TRUE,
conf.int = TRUE,
xlim = c(0,500), # xlim, ylim # 指定x轴和y轴的范围，如xlim = c(0,30), ylim = c(0,1)
xlab = "Time in days", # xlab, ylab 分别指x轴和y轴标签
break.time.by = 100, # 设定坐标轴刻度间距
ggtheme = theme_light(),
risk.table.y.text.col = T, # 分线表y轴文字颜色.
risk.table.y.text = FALSE # 风险表y轴展示条形图例不展示文字标签
)
#Uber定制生存曲线
ggsurv <- ggsurvplot(
fit,
data = lung,
risk.table = TRUE,
pval = TRUE,
conf.int = TRUE,
palette = c("#E7B800", "#2E9FDF"),
xlim = c(0,500),
xlab = "Time in days",
break.time.by = 100,
ggtheme = theme_light(),
risk.table.y.text.col = T,
risk.table.height = 0.25,
risk.table.y.text = FALSE,
ncensor.plot = TRUE, # 绘制删失事件图
ncensor.plot.height = 0.25,
conf.int.style = "step", # 设置置信区间的类型，有"ribbon"(默认),"step"两种
surv.median.line = "hv", # 添加中位生存时间线,可用值有”none”、”hv”、”h”、”v”；其中v绘制垂直线，h绘制水平线。
legend.labs =
c("Male", "Female")
)
ggsurv
#引入customize_labels 函数进行更高级的定制
customize_labels <- function (p, font.title = NULL,
font.subtitle = NULL, font.caption = NULL,
font.x = NULL, font.y = NULL, font.xtickslab = NULL, font.ytickslab = NULL)
{
original.p <- p
if(is.ggplot(original.p)) list.plots <- list(original.p)
else if(is.list(original.p)) list.plots <- original.p
else stop("Can't handle an object of class ", class (original.p))
.set_font <- function(font){
font <- ggpubr:::.parse_font(font)
ggtext::element_markdown (size = font$size, face = font$face, colour = font$color)
}
for(i in 1:length(list.plots)){
p <- list.plots[[i]]
if(is.ggplot(p)){
if (!is.null(font.title)) p <- p + theme(plot.title = .set_font(font.title))
if (!is.null(font.subtitle)) p <- p + theme(plot.subtitle = .set_font(font.subtitle))
if (!is.null(font.caption)) p <- p + theme(plot.caption = .set_font(font.caption))
if (!is.null(font.x)) p <- p + theme(axis.title.x = .set_font(font.x))
if (!is.null(font.y)) p <- p + theme(axis.title.y = .set_font(font.y))
if (!is.null(font.xtickslab)) p <- p + theme(axis.text.x = .set_font(font.xtickslab))
if (!is.null(font.ytickslab)) p <- p + theme(axis.text.y = .set_font(font.ytickslab))
list.plots[[i]] <- p
}
}
if(is.ggplot(original.p)) list.plots[[1]]
else list.plots
}
#Uber platinum customized survival curves
# 更改标签
#生存曲线的标签
ggsurv$plot <- ggsurv$plot + labs(
title = "Survival curves", #主标题
subtitle = "Based on Kaplan-Meier estimates", #副标题
caption = "created with survminer" #说明
)
ggsurv$plot
#风险表标签
ggsurv$table <- ggsurv$table + labs(
title = "Note the risk set sizes",
subtitle = "and remember about censoring.",
caption = "source code: website.com"
)
ggsurv$table
#删失图标签
ggsurv$ncensor.plot <- ggsurv$ncensor.plot + labs(
title = "Number of censorings",
subtitle = "over the time.",
caption = "source code: website.com"
)
ggsurv$ncensor.plot
# 更改字体大小、类型和颜色
ggsurv <- customize_labels(
ggsurv,
font.title = c(16, "bold", "darkblue"),#用长度为3的向量分别指定大小、类型、颜色
font.subtitle = c(15, "bold.italic", "purple"),
font.caption = c(14, "plain", "orange"),
font.x = c(14, "bold.italic", "red"),
font.y = c(14, "bold.italic", "darkred"),
font.xtickslab = c(12, "plain", "darkgreen")
)
ggsurv
Plot multiple groups of survival curves
#绘制多组生存曲线
#使用内置数据集colon
#查看数据格式
head(colon)
> head(colon)
id study rx sex age obstruct perfor adhere
1 1 1 Lev+5FU 1 43 0 0 0
2 1 1 Lev+5FU 1 43 0 0 0
3 2 1 Lev+5FU 1 63 0 0 0
4 2 1 Lev+5FU 1 63 0 0 0
5 3 1 Obs 0 71 0 0 1
6 3 1 Obs 0 71 0 0 1
nodes status differ extent surg node4 time etype
1 5 1 2 3 0 1 1521 2
2 5 1 2 3 0 1 968 1
3 1 0 2 3 0 0 3087 2
4 1 0 2 3 0 0 3087 1
5 7 1 2 2 0 1 963 2
6 7 1 2 2 0 1 542 1
colon数据集：B/C期结肠癌辅助化疗治疗数据
d # 患者编号
study # 所有患者都是1
rx # 治疗方式，有三种：观察、Levamisole、Levamisole+5-FU
sex # 性别，男性为 1，女性为 0
age # 年龄
obstruct # 肿瘤是否阻塞结肠，有为1，无为0
perfor # 结肠是否穿孔，有为1，无为0
adhere # 肿瘤是否粘附附近器官，有为1，无为0
nodes # 检出淋巴结的数目
time # 至发生终点事件或删失的时间
status # 生存状态，1为发生终点事件，0为删失
differ # 肿瘤分化程度 (1=well, 2=moderate, 3=poor)
extent # 局部转移程度（1=粘膜下层，2=肌肉，3=浆膜，4=相邻结构）
surg # 从手术到登记的时间 (0=short, 1=long)
node4 # 超过4个阳性淋巴结，有为1，无为0
etype # 事件类型: 1=复发,2=死亡
#rx分组
fit2 <- survfit( Surv(time, status) ~ rx + adhere,
data = colon )
ggsurvplot(fit2, pval = TRUE,
break.time.by = 800,
risk.table = TRUE,
risk.table.height = 0.5
)
reference
https://rpkgs.datanovia.com/survminer/
http://www.sthda.com/english/wiki/survminer-r-package-survival-data-analysis-and-visualization
Past articles:
ggcorrplot | Simple correlation heatmap plotting