R包survminer画生存曲线的实用技能，你get了吗？

install.packages(“survminer”);library(survminer);require("survival")

fit <- survfit(Surv(time, status) ~ sex, data = lung)#也可以用surv_fit
fit #Fig 1

# Basic survival curves
ggsurvplot(fit, data = lung) #Fig 2

ggsurvplot(fit, data = lung,
surv.median.line = "hv", #用于在中位生存值绘制水平/垂直线，c(“none”，“hv”，“h”，“v”)。
legend.title = "Sex",legend.labs = c("Male", "Female"), #改变图例
pval = "The hot p-value is: 0.003",conf.int = TRUE, #加p-value和置信区间
risk.table = TRUE, #加入风险表
tables.height = 0.2, #生存图下的表的高度，[0~1]。
palette = c("#E7B800", "#2E9FDF"),#曲线颜色 
ggtheme = theme_bw(), #改变ggplot2背景theme_linedraw，theme_light，theme_gray等。
font.main = c(16, "bold", "darkblue"),font.x = c(14, "bold.italic", "red"),font.y = c(14, "bold.italic", "darkred"),font.tickslab = c(12, "plain", "darkgreen")# 改变字体大小类型颜色等
) #Fig 3

fit <- survfit( Surv(time, status) ~ sex + rx + adhere,data = colon )
ggsurvplot(fit, data = colon,
fun = "cumhaz", #定义生存曲线变换的任意函数，"event"累积事件f(y) = 1-y, "cumhaz"累积风险函数f(y) = -log(y),"pct"生存概率(百分比)。
conf.int = TRUE, risk.table = TRUE, risk.table.col="strata",
ggtheme = theme_bw())
#fun = "event"--Fig 4.1
#fun = "cumhaz"--Fig 4.2
#fun = "pct"--Fig 4.3

splots <- list()#创建一个列表并设置成不同的背景色
splots[[1]] <- ggsurvplot(fit, data = lung, risk.table = TRUE, ggtheme = theme_bw())
splots[[2]] <- ggsurvplot(fit, data = lung, risk.table = TRUE, ggtheme = theme_grey())

arrange_ggsurvplots(splots, 
print = TRUE,#为TRUE时展示排列好的图形
ncol = 2, nrow = 1,#行和列的数量
risk.table.height = 0.4#这里设置risk.table为TRUE，风险表的高度默认0.25，有多个表时适当增加高度。
) #Fig 5

fit1 <- surv_fit(Surv(time, status) ~ sex, data = colon)
fit2 <- surv_fit(Surv(time, status) ~ adhere, data = colon)
fit.list <- list(sex = fit1, adhere = fit2)

surv_median(fit1)
surv_median(fit.list)
surv_median(fit.list, combine = TRUE) #Fig 6

surv_pvalue(fit1, data = colon, method = "survdiff", 
get_coord = TRUE) #Fig 7
#pval:p值
#method: 计算p值的方法
#pval.txt: 用于注释图形的文本
#pval.x, pval.y: 注释图形的x&y的坐标
#method.x, method.y: 计算p值的方法的坐标x & y coordinates of pvalue method

res.sum <- surv_summary(fit1, data = colon);head(res.sum) #Fig 8
#time: 在曲线上有一阶跃的时间点。
#n.risk: 当前时间的受试者人数。
#n.event: 当前时间的事件数目。
#n.censor: 删失事件数目。
#surv: 生存评估。
#std.err: 标准误差。
#upper/lower: 置信区间的上下限。
#strata: 生存曲线的类别

head(surv_summary(fit2, colon)) #Fig 9

ggsurvplot_df(surv_summary(fit2, colon), conf.int = TRUE,
legend.title = "Adhere", legend.labs = c("0", "1"))#Fig 10

surv <- Surv(lung$time, lung$status)
ggsurvevents(surv, 
normalized = TRUE,#展示相对事件数目
type = "radius") #Fig 11.1

#"cumulative"累积事件数,"radius"给定半径内的事件数
ggsurvevents(surv, normalized = TRUE, type = "cumulative")#Fig 11.2
ggsurvevents(surv, normalized = TRUE, type = "fraction")#Fig 11.3

data(mgus)
res.cox <- coxph(Surv(futime, death) ~ mspike + log(mspike) + I(mspike^2) +age + I(log(age)^2) + I(sqrt(age)), data = mgus)
res.cox #Fig 12

ggcoxfunctional(res.cox, data = mgus,
point.col = "blue", point.size = 0.5, point.shape = 8, point.alpha = 0.5,#点的颜色大小形状和可见度
title = "Title", caption = "Caption"#主标题，纵标题
)#Fig 13

fit <- coxph(Surv(futime, fustat) ~ age + ecog.ps + rx, data=ovarian)
cox.zph.fit <- cox.zph(fit)

ggcoxzph(cox.zph.fit, var = c("ecog.ps", "rx", "age"), 
font.main =c(12,"darkred"), caption = "Caption", 
submain="Submain",font.y=c(14, "bold.italic", "darkred"), 
font.x = c(14, "bold", "red")) #Fig 14

require("cmprsk")
#生成随机数
set.seed(2);ss <- rexp(100)
gg <- factor(sample(1:3,100,replace=TRUE),1:3,c('BRCA','LUNG','OV'))
cc <- factor(sample(0:2,100,replace=TRUE),0:2,c('no event', 'death', 'progression'))
strt <- sample(1:2,100,replace=TRUE)

# fit：cmprsk::cuminc类的对象——用cmprsk::cuminc函数创建，或者用survfit函数创建
print(fit <- cmprsk::cuminc(ss,cc,gg,strt)) #Fig 15

ggcompetingrisks(fit)#Fig 16

ggcompetingrisks(fit, multiple_panels = FALSE, conf.int = TRUE)#不分组 Fig 17

fit <- survfit(Surv(time, status, type="mstate") ~ group, data=df)
ggcompetingrisks(fit)#多组 Fig 18

model <- coxph( Surv(time, status) ~ sex + rx + adhere, data = colon )
ggforest(model) #Fig 19

colon <- within(colon, {sex <- factor(sex, labels = c("female", "male"))
differ <- factor(differ, labels = c("well", "moderate", "poor"))
extent <- factor(extent, labels = c("submuc.", "muscle", "serosa", "contig."))
})
head(colon)#Fig 20

bigmodel<-coxph(Surv(time, status) ~ sex + rx + adhere + differ + extent + node4,data = colon )
ggforest(bigmodel,
fontsize=1.5,#图中注释的相对大小，默认0.7。
noDigits=4#图中估计值和p值的显示位数
)#Fig 21

fit <- coxph( Surv(stop, event) ~ size + strata(rx), data = bladder )
curve <- surv_adjustedcurves(fit, data = bladder, method = "average", variable = "rx")
ggadjustedcurves(fit, data = bladder, 
method = "average",#描述预期生存曲线应如何计算。single(总体平均)，average(亚组平均)，’marginal’, ’conditional’(再平衡后的亚组平均)。
variable = "rx",
palette=c("red","blue")#曲线颜色
size=0.5#曲线宽度
)# Fig 22