R Kaplan Meier曲线按性别和危险因素划分的-divide曲线

R Kaplan-Meier曲线是一种非参数统计方法，用于估计生存函数，即在不同时间点上生存的概率。当数据中包含分类变量（如性别）或其他危险因素时，我们可以根据这些变量来划分曲线，以观察不同组之间的生存差异。

以下是如何在R中使用survival和survminer包来绘制按性别和其他危险因素划分的Kaplan-Meier曲线：

1. 安装和加载必要的包

install.packages("survival")
install.packages("survminer")
library(survival)
library(survminer)

2. 准备数据

假设你有一个数据框df，其中包含生存时间time、事件指示status、性别gender和其他危险因素（例如risk_factor）。

# 示例数据
df <- data.frame(
  time = c(10, 20, 30, 40, 50, 60, 70, 80, 90, 100),
  status = c(1, 1, 0, 1, 0, 1, 0, 1, 0, 1),
  gender = c("Male", "Female", "Male", "Female", "Male", "Female", "Male", "Female", "Male", "Female"),
  risk_factor = c("High", "Low", "High", "Low", "High", "Low", "High", "Low", "High", "Low")
)

3. 拟合Kaplan-Meier模型

# 拟合基本的Kaplan-Meier模型
km_model <- survfit(Surv(time, status) ~ 1, data = df)

4. 绘制按性别划分的Kaplan-Meier曲线

# 绘制按性别划分的Kaplan-Meier曲线
ggsurvplot(km_model, data = df, risk.table = TRUE, pval = TRUE, break.time.by = 20,
            ggtheme = theme_minimal(), legend.title = "Gender",
            facet.by = "gender", palette = "jco")

5. 绘制按危险因素划分的Kaplan-Meier曲线

# 绘制按危险因素划分的Kaplan-Meier曲线
ggsurvplot(km_model, data = df, risk.table = TRUE, pval = TRUE, break.time.by = 20,
            ggtheme = theme_minimal(), legend.title = "Risk Factor",
            facet.by = "risk_factor", palette = "jco")

6. 绘制按性别和危险因素划分的Kaplan-Meier曲线

# 绘制按性别和危险因素划分的Kaplan-Meier曲线
ggsurvplot(km_model, data = df, risk.table = TRUE, pval = TRUE, break.time.by = 20,
            ggtheme = theme_minimal(), legend.title = "Gender and Risk Factor",
            facet.by = c("gender", "risk_factor"), palette = "jco")