如何在 R 中的 ggsurvplot 中更改审查 table 的 y 轴？
How to change the y-axis of the censoring table in ggsurvplot in R?
包 survminer 中的 ggsurvplot 函数允许用户绘制 Kaplen-Meier 曲线。它还允许我们通过配置 ncensor.plot 值在图下方创建一个 table，其中包含删失观察的数量。我的问题是：如何更改情节下方已审查 table 的 y 轴？原因是我有一项研究在例如 52 周时终止，因此删失观察的数量很高，完全扭曲了 table。请参阅下面的示例。
#Create a random dataset with gender, event, time
set.seed(100)
time <- rnorm(n = 100, mean = 52, sd = 20)
set.seed(150)
gender <- sample(x = c(0, 1), prob = c(.6, .4), size = 100, replace = TRUE)
set.seed(100)
event <- sample(x = c(0, 1), prob = c(.4, .6), size = 100, replace = TRUE)
dat <- data.frame(time, gender, event)
#Censor time after 52 weeks
dat <- dat %>% mutate(time_adjusted = if_else(time > 52, 52, time))
#KM-estimator
survObject <- Surv(time = dat$time_adjusted, event = dat$event)
km.model <- survfit(survObject ~ gender, data = dat)
summary(km.model)
Call: survfit(formula = survObject ~ gender, data = dat)
gender=0
time n.risk n.event survival std.err lower 95% CI upper 95% CI
6.56149 61 1 0.983607 0.0162585 0.952251 1.000000
14.42688 58 1 0.966648 0.0231936 0.922242 1.000000
28.42634 56 1 0.949386 0.0284876 0.895162 1.000000
28.84541 54 1 0.931805 0.0329415 0.869427 0.998658
28.84858 53 1 0.914224 0.0367130 0.845027 0.989087
33.72372 52 1 0.896643 0.0399957 0.821581 0.978562
35.49481 51 1 0.879061 0.0429020 0.798871 0.967301
35.71242 50 1 0.861480 0.0455040 0.776755 0.955446
37.73950 48 1 0.843533 0.0479650 0.754572 0.942981
41.95615 45 1 0.824787 0.0504291 0.731641 0.929793
43.23820 44 1 0.806042 0.0526518 0.709180 0.916135
44.22292 43 1 0.787297 0.0546624 0.687131 0.902065
48.84190 40 1 0.767615 0.0567288 0.664106 0.887257
49.24141 39 1 0.747932 0.0585893 0.641480 0.872050
50.42166 36 1 0.727156 0.0605334 0.617686 0.856027
50.60166 35 1 0.706380 0.0622672 0.594300 0.839599
51.00008 34 1 0.685605 0.0638078 0.571287 0.822798
52.00000 33 21 0.249311 0.0619235 0.153222 0.405659
gender=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
10.5119 39 1 0.974359 0.0253102 0.925994 1.000000
17.2280 37 1 0.948025 0.0357936 0.880404 1.000000
24.0235 36 1 0.921691 0.0434190 0.840402 1.000000
28.7516 35 1 0.895357 0.0495247 0.803367 0.997880
30.7129 33 1 0.868225 0.0549558 0.766927 0.982903
34.1409 32 1 0.841093 0.0595606 0.732095 0.966319
35.2230 31 1 0.813961 0.0635192 0.698519 0.948482
35.3501 30 1 0.786829 0.0669463 0.665973 0.929616
43.0588 26 1 0.756566 0.0708822 0.629649 0.909066
43.2310 25 1 0.726303 0.0742265 0.594466 0.887379
44.8028 24 1 0.696041 0.0770563 0.560275 0.864706
46.7601 23 1 0.665778 0.0794266 0.526965 0.841157
47.9673 21 1 0.634074 0.0817272 0.492524 0.816307
49.9674 20 1 0.602371 0.0835642 0.458966 0.790583
50.1777 18 1 0.568906 0.0853600 0.423959 0.763408
52.0000 15 8 0.265489 0.0834090 0.143425 0.491439
#KM-curves
ggsurvplot(km.model, data = dat, ggtheme = theme_bw(), conf.int = TRUE, risktable = "abs_pct", ncensor.plot = TRUE,
xlab = "Time in weeks", ylab = "Survival", xlim = c(0,52), break.x.by = 13)
图表的输出
ggsurvplot 返回的对象是一个列表，其中包含作为名为 plot 的元素的主情节和作为名为 ncensor.plot 的元素的删失 table 情节。两者都是 ggplot 对象，因此可以使用默认的 ggplot 选项进行调整。作为我下面代码中的一个例子，我使用 scale_y_continuous 来改变审查 table 图的 y 尺度的 breaks：
library(survminer)
library(survival)
library(dplyr)
p <- ggsurvplot(km.model, data = dat, ggtheme = theme_bw(), conf.int = TRUE, risktable = "abs_pct", ncensor.plot = TRUE,
xlab = "Time in weeks", ylab = "Survival", xlim = c(0,52), break.x.by = 13)
p$ncensor.plot <- p$ncensor.plot + scale_y_continuous(breaks = seq(12))
#> Scale for 'y' is already present. Adding another scale for 'y', which will
#> replace the existing scale.
p
EDIT 可以使用 limits 参数设置 x 轴的限制，其中我还根据 ggsurvplot 默认设置的设置设置了中断，除了 52我将其替换为 51:
p$ncensor.plot <- p$ncensor.plot +
scale_y_continuous(breaks = seq(12)) +
scale_x_continuous(breaks = c(0, 13, 26, 39, 51), limits = c(NA, 51))
p
包 survminer 中的 ggsurvplot 函数允许用户绘制 Kaplen-Meier 曲线。它还允许我们通过配置 ncensor.plot 值在图下方创建一个 table，其中包含删失观察的数量。我的问题是：如何更改情节下方已审查 table 的 y 轴？原因是我有一项研究在例如 52 周时终止，因此删失观察的数量很高，完全扭曲了 table。请参阅下面的示例。
#Create a random dataset with gender, event, time
set.seed(100)
time <- rnorm(n = 100, mean = 52, sd = 20)
set.seed(150)
gender <- sample(x = c(0, 1), prob = c(.6, .4), size = 100, replace = TRUE)
set.seed(100)
event <- sample(x = c(0, 1), prob = c(.4, .6), size = 100, replace = TRUE)
dat <- data.frame(time, gender, event)
#Censor time after 52 weeks
dat <- dat %>% mutate(time_adjusted = if_else(time > 52, 52, time))
#KM-estimator
survObject <- Surv(time = dat$time_adjusted, event = dat$event)
km.model <- survfit(survObject ~ gender, data = dat)
summary(km.model)
Call: survfit(formula = survObject ~ gender, data = dat)
gender=0
time n.risk n.event survival std.err lower 95% CI upper 95% CI
6.56149 61 1 0.983607 0.0162585 0.952251 1.000000
14.42688 58 1 0.966648 0.0231936 0.922242 1.000000
28.42634 56 1 0.949386 0.0284876 0.895162 1.000000
28.84541 54 1 0.931805 0.0329415 0.869427 0.998658
28.84858 53 1 0.914224 0.0367130 0.845027 0.989087
33.72372 52 1 0.896643 0.0399957 0.821581 0.978562
35.49481 51 1 0.879061 0.0429020 0.798871 0.967301
35.71242 50 1 0.861480 0.0455040 0.776755 0.955446
37.73950 48 1 0.843533 0.0479650 0.754572 0.942981
41.95615 45 1 0.824787 0.0504291 0.731641 0.929793
43.23820 44 1 0.806042 0.0526518 0.709180 0.916135
44.22292 43 1 0.787297 0.0546624 0.687131 0.902065
48.84190 40 1 0.767615 0.0567288 0.664106 0.887257
49.24141 39 1 0.747932 0.0585893 0.641480 0.872050
50.42166 36 1 0.727156 0.0605334 0.617686 0.856027
50.60166 35 1 0.706380 0.0622672 0.594300 0.839599
51.00008 34 1 0.685605 0.0638078 0.571287 0.822798
52.00000 33 21 0.249311 0.0619235 0.153222 0.405659
gender=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
10.5119 39 1 0.974359 0.0253102 0.925994 1.000000
17.2280 37 1 0.948025 0.0357936 0.880404 1.000000
24.0235 36 1 0.921691 0.0434190 0.840402 1.000000
28.7516 35 1 0.895357 0.0495247 0.803367 0.997880
30.7129 33 1 0.868225 0.0549558 0.766927 0.982903
34.1409 32 1 0.841093 0.0595606 0.732095 0.966319
35.2230 31 1 0.813961 0.0635192 0.698519 0.948482
35.3501 30 1 0.786829 0.0669463 0.665973 0.929616
43.0588 26 1 0.756566 0.0708822 0.629649 0.909066
43.2310 25 1 0.726303 0.0742265 0.594466 0.887379
44.8028 24 1 0.696041 0.0770563 0.560275 0.864706
46.7601 23 1 0.665778 0.0794266 0.526965 0.841157
47.9673 21 1 0.634074 0.0817272 0.492524 0.816307
49.9674 20 1 0.602371 0.0835642 0.458966 0.790583
50.1777 18 1 0.568906 0.0853600 0.423959 0.763408
52.0000 15 8 0.265489 0.0834090 0.143425 0.491439
#KM-curves
ggsurvplot(km.model, data = dat, ggtheme = theme_bw(), conf.int = TRUE, risktable = "abs_pct", ncensor.plot = TRUE,
xlab = "Time in weeks", ylab = "Survival", xlim = c(0,52), break.x.by = 13)
图表的输出
ggsurvplot 返回的对象是一个列表，其中包含作为名为 plot 的元素的主情节和作为名为 ncensor.plot 的元素的删失 table 情节。两者都是 ggplot 对象，因此可以使用默认的 ggplot 选项进行调整。作为我下面代码中的一个例子，我使用 scale_y_continuous 来改变审查 table 图的 y 尺度的 breaks：
library(survminer)
library(survival)
library(dplyr)
p <- ggsurvplot(km.model, data = dat, ggtheme = theme_bw(), conf.int = TRUE, risktable = "abs_pct", ncensor.plot = TRUE,
xlab = "Time in weeks", ylab = "Survival", xlim = c(0,52), break.x.by = 13)
p$ncensor.plot <- p$ncensor.plot + scale_y_continuous(breaks = seq(12))
#> Scale for 'y' is already present. Adding another scale for 'y', which will
#> replace the existing scale.
p
EDIT 可以使用 limits 参数设置 x 轴的限制，其中我还根据 ggsurvplot 默认设置的设置设置了中断，除了 52我将其替换为 51:
p$ncensor.plot <- p$ncensor.plot +
scale_y_continuous(breaks = seq(12)) +
scale_x_continuous(breaks = c(0, 13, 26, 39, 51), limits = c(NA, 51))
p