如何使用R进行单变量和多变量生存分析？

huangapple
go评论70阅读模式
英文:

How to get univariate + multivariate survival analysis with R?

问题

使用survminer、ggplot2和survival包，我想要创建两个Kaplan Meier图：

  • 一个用于单变量生存分析，使用event_followup表示时间至事件，event表示事件状态，以及具有四个类别的scoreMVPA，分别是no AF proc + inactive、no AF proc + active、AF proc + inactive、AF proc + active。
  • 一个用于多变量生存分析，其中sex编码为（1表示女性，0表示男性），age和match_RF_stroke编码为（1表示是，0表示否）作为协变量。

我想要获取这两个KM图和风险表，只显示这四个类别。我正在使用ggsurvplot，但我难以得到我的图。

谢谢！

以下是您的数据集示例：

structure(list(sex = c("Male", "Female", "Male", "Male", "Male", 
"Male", "Male", "Male", "Male", "Male", "Female", "Male", "Male", 
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male", 
"Male", "Female", "Male", "Male", "Male", "Male", "Male", "Male", 
"Female", "Male", "Male", "Male", "Male", "Female", "Female", 
"Male", "Female", "Female", "Male", "Male", "Female", "Female", 
"Female", "Male", "Male", "Male", "Male", "Male", "Male", "Female", 
"Male", "Male", "Female", "Female", "Male", "Male", "Female", 
"Female", "Female", "Male", "Female", "Female", "Male", "Male", 
"Female", "Male", "Female", "Male", "Male", "Male", "Male", "Male", 
"Male", "Female", "Male", "Male", "Male", "Male", "Male", "Male", 
"Female", "Male", "Male", "Male", "Male", "Male", "Male", "Male", 
"Female", "Female", "Female", "Male", "Male", "Male", "Female", 
"Male", "Male", "Female", "Male", "Male", "Female", "Male", "Male", 
"Male", "Male", "Male", "Female", "Female", "Male", "Male", "Female", 
"Female", "Male", "Male", "Female", "Male", "Male", "Female", 
"Female", "Female", "Male", "Male", "Male", "Male", "Male", "Male", 
"Male", "Female", "Female", "Male", "Male", "Male", "Male", "Male", 
"Male", "Female", "Male", "Male", "Female", "Male", "Female", 
"Male", "Male", "Male", "Female", "Male", "Male", "Male", "Male", 
"Female", "Female", "Male", "Female", "Male", "Male", "Male", 
"Male", "Male", "Male", "Female", "Female", "Male", "Male", "Male", 
"Male", "Female", "Male", "Female", "Male", "Male", "Female", 
"Female", "Male", "Male", "Female", "Male", "Male", "Female", 
"Female", "Male", "Male", "Male", "Male", "Male", "Male", "Female", 
"Female", "Male", "Male", "Male", "Male", "Male", "Male", "Female", 
"Male", "Male", "Female", "Male", "Female", "Male", "Male", "Male", 
"Female", "Male", "Male", "Male", "Male", "Female", "Female", 
"Male", "Female", "Male", "Male", "Male", "Male", "Male", "Male", 
"Female", "Female", "Male", "Male", "Male", "Male", "Female", 
"Male", "Female", "Male", "Male", "Female", "Female", "Male", 
"Male", "Female", "Male", "Male", "Female", "Female", "Male", 
"Male", "Male", "Male", "Male", "Male", "Female", "Female", "Male", 
"Male", "Male", "Male", "Male", "Male", "Female", "Male", "Male", 
"Female", "Male", "Male", "Female", "Female", "Male", "

<details>
<summary>英文:</summary>

Using the `survminer`, `ggplot2`, and `survival` packages, I would like to create two Kaplan Meier plots:
- One for **univariate** survival analysis with `event_followup` for the time to event, `event` for the status, and `scoreMVPA` that has four categories `no AF proc + inactive`, `no AF proc + active`, `AF proc + inactive`, `AF proc + active`.
- One for **multivariate** survival analysis with `sex` coded (1 for female and 0 for male), `age`, and `match_RF_stroke` coded (1 for yes, and 0 for no) as covariates.

I would like to get the KM plots + risk table with them that only show the four categories. I am using `ggsurvplot` but I am struggling to get my plot. 

Thanks a lot!


Here is a sample of my dataset:

structure(list(sex = c("Male", "Female", "Male", "Male", "Male",
"Male", "Male", "Male", "Male", "Male", "Female", "Male", "Male",
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male",
"Male", "Female", "Male", "Male", "Male", "Male", "Male", "Male",
"Female", "Male", "Male", "Male", "Male", "Female", "Female",
"Male", "Female", "Female", "Male", "Male", "Female", "Female",
"Female", "Male", "Male", "Male", "Male", "Male", "Male", "Female",
"Male", "Male", "Female", "Female", "Male", "Male", "Female",
"Female", "Female", "Male", "Female", "Female", "Male", "Male",
"Female", "Male", "Female", "Male", "Male", "Male", "Male", "Male",
"Male", "Female", "Male", "Male", "Male", "Male", "Male", "Male",
"Female", "Male", "Male", "Male", "Male", "Male", "Male", "Male",
"Female", "Female", "Female", "Male", "Male", "Male", "Female",
"Male", "Male", "Female", "Male", "Male", "Female", "Male", "Male",
"Male", "Male", "Male", "Female", "Female", "Male", "Male", "Female",
"Female", "Male", "Male", "Female", "Male", "Male", "Female",
"Female", "Female", "Male", "Male", "Male", "Male", "Male", "Male",
"Male", "Female", "Male", "Male", "Female", "Male", "Female",
"Male", "Male", "Male", "Female", "Male", "Male", "Male", "Male",
"Female", "Female", "Male", "Female", "Male", "Male", "Male",
"Male", "Male", "Male", "Female", "Female", "Male", "Male", "Male",
"Male", "Female", "Male", "Female", "Male", "Male", "Female",
"Female", "Male", "Male", "Female", "Male", "Male", "Female",
"Female", "Male", "Male", "Male", "Male", "Male", "Male", "Female",
"Female", "Male", "Male", "Male", "Male", "Male", "Male", "Female",
"Male", "Male", "Female", "Male", "Male", "Female", "Female",
"Male", "Male", "Female", "Female", "Female", "Female"), age = c(41,
58, 63, 65, 62, 66, 68, 48, 68, 59, 63, 67, 64, 66, 52, 52, 69,
64, 61, 67, 65, 67, 61, 62, 58, 54, 53, 66, 67, 67, 64, 65, 64,
69, 63, 51, 67, 65, 68, 64, 65, 58, 62, 65, 67, 63, 60, 52, 69,
69, 54, 53, 55, 68, 61, 46, 67, 67, 57, 55, 61, 65, 61, 66, 48,
47, 69, 55, 58, 69, 64, 53, 65, 64, 62, 68, 64, 65, 60, 58, 54,
43, 51, 69, 60, 69, 70, 64, 59, 65, 61, 69, 67, 63, 69, 58, 55,
61, 63, 64, 65, 69, 62, 69, 61, 65, 60, 53, 41, 56, 61, 65, 65,
68, 65, 51, 69, 52, 61, 54, 44, 63, 55, 62, 62, 68, 54, 63, 41,
69, 67, 50, 65, 60, 59, 67, 66, 61, 67, 47, 63, 66, 65, 64, 57,
52, 65, 65, 60, 44, 48, 67, 43, 63, 46, 59, 63, 66, 67, 62, 62,
59, 70, 64, 65, 63, 62, 62, 42, 60, 62, 61, 64, 61, 60, 68, 64,
56, 66, 61, 69, 62, 58, 59, 60, 57, 69, 47, 69, 51, 43, 63, 63,
64, 68, 64, 60, 70, 66, 62), smoking_status = c(0, 0, 0, 0, 0,
1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0), match_MACE = c(0, 0, 1, 1, 0, 0, 0, 0, 1,
1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0,
0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0,
1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0,
0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0,
1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1,
0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0,
0, 0), time_diff_MACE_months = c(72, 72, 28.4, 59.4, 72, 72,
72, 72, 68.9, 11.4, 72, 72, 11.7, 7.8, 1.5, 72, 1.7, 16.8, 72,
72, 72, 26.1, 72, 72, 62.3, 72, 72, 72, 38.9, 72, 72, 49.8, 72,
72, 13, 26.4, 72, 72, 72, 72, 72, 18.8, 24, 49.9, 25.1, 72, 16.4,
72, 27, 72, 72, 72, 72, 72, 72, 72, 3.6, 72, 72, 72, 72, 40.8,
72, 1.6, 72, 72, 69, 3.4, 72, 28.1, 72, 72, 52.8, 72, 39.6, 72,
59.9, 72, 72, 72, 72, 72, 72, 6.8, 72, 37.9, 72, 9.3, 18.7, 5.9,
72, 37.1, 72, 72, 5.9, 72, 72, 20.4, 45.6, 68.5, 72, 72, 25.4,
72, 72, 18.5, 72, 72, 72, 10.1, 24, 72, 72, 72, 72, 72, 11.3,
72, 72, 72, 72, 41.6, 72, 72, 72, 66.6, 24.1, 72, 72, 8, 72,
1.2, 72, 72, 72, 2.1, 30.6, 72, 10, 72, 55.5, 20.9, 67, 11.7,
4, 72, 18, 14, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 7.7, 72,
72, 72, 72, 64.3, 56.8, 2.6, 72, 3.9, 72, 72, 72, 72, 72, 72,
72, 37.9, 22.6, 72, 72, 30.4, 72, 72, 72, 72, 34.5, 72, 8, 72,
24.3, 72, 13.5, 9.3, 12, 72, 3, 1.3, 72, 72, 72, 72), match_RF_stroke = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0), match_RF_HF = c(0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0), match_RF_AMI = c(0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0), match_RF_T2D = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), match_RF_HTA = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0), match_RF_MI = c(0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0), scoreMVPA = structure(c(1L, 1L, 2L, 1L, 3L,
2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L,
1L, 2L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L,
2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 4L, 2L, 1L, 1L, 1L, 2L,
2L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 3L, 2L, 2L, 1L, 1L, 1L, 2L,
1L, 2L, 2L, 4L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 4L, 2L, 2L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 3L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L,
1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L,
1L, 2L, 2L, 2L, 3L, 4L, 2L, 1L, 2L, 3L, 1L, 2L, 2L, 2L, 2L, 1L,
2L, 2L, 2L, 2L, 1L, 3L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L,
2L, 4L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 3L, 1L, 1L,
2L, 2L, 4L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L,
1L, 1L, 3L), levels = c("no AF proc + inactive", "no AF proc + active",
"AF proc + inactive", "AF proc + active"), class = "factor")), row.names = c(NA,
-200L), class = c("tbl_df", "tbl", "data.frame"))


[1]: https://i.stack.imgur.com/lFuZz.jpg
</details>
# 答案1
**得分**: 1
这是解决方案。我已经为您完善了UV代码，并留下了MV供您进行优化。我已将您的数据存储为x。
使用survival库
使用survminer库
将数据存储为data1
UV使用以下代码：
使用Surv函数计算，时间变量为time_diff_MACE_months，事件变量为match_MACE，按scoreMVPA分组，使用data1中的数据
MV使用以下代码：
使用coxph函数进行Cox比例风险回归分析，时间变量为time_diff_MACE_months，事件变量为match_MACE，考虑age、sex和scoreMVPA分层，使用data1中的数据
生成plot3：
使用ggsurvplot函数绘制生存曲线图，UV数据，显示p值，时间间隔为10，不显示置信区间，显示风险表格，y轴风险表格文本颜色为真，x轴范围为0到75，图例标签分别为“no AF proc + inactive”、“no AF proc + active”、“AF proc + inactive”、“AF proc + active”，x轴标签为“Months”，y轴标签为“% who do not incur MACE”，调色板设置，图形主题为“lancet”，p值显示坐标为(0,0.1)，图例标题为空字符串
生成plot4：
使用ggadjustedcurves函数绘制调整后的曲线图，MV数据，使用data1中的数据
UV图
[![plot3][2]][2]
MV图
[![plot4][1]][1]
<details>
<summary>英文:</summary>
This is the solution to your problem. I&#39;ve fleshed out the UV code for you and left the MV for your own optimisation. I have stored your data as x.
library(survival)
library(survminer)
data1 &lt;- x %&gt;% data.frame()
UV &lt;- survfit(Surv(time = time_diff_MACE_months, event = match_MACE) ~ scoreMVPA, data = data1)
MV &lt;- coxph(Surv(time = time_diff_MACE_months, event = match_MACE) ~ age + sex + strata(scoreMVPA), data = data1)
plot3 &lt;- ggsurvplot(UV,
pval = TRUE,
break.time.by = 10,
conf.int = FALSE,
risk.table = T, 
risk.table.y.text.col = TRUE,
xlim=c(0,75), 
legend.labs = c(&quot;no AF proc + inactive&quot;, &quot;no AF proc + active&quot;, &quot;AF proc + inactive&quot;, &quot;AF proc + active&quot;), 
xlab = &quot;Months&quot;,
ylab = &quot;% who do not incur MACE&quot;, 
palette = c(&quot;#fba30c&quot;, &quot;#0077b8&quot;, &quot;#06D6A0&quot;, &quot;#002A32&quot;), 
theme = &quot;lancet&quot;, 
pval.coord = c(0,0.1), 
legend.title = &quot;&quot;)
plot4 &lt;- ggadjustedcurves(MV, data = data1)
UV plot
[![plot3][2]][2]
MV plot
[![plot4][1]][1]
[1]: https://i.stack.imgur.com/88Mdm.png
[2]: https://i.stack.imgur.com/r4G6z.png
</details>

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  • 本文由 发表于 2023年6月26日 05:07:48
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