为生存图指定自定义时间点
Specifying custom time points for survival plot
我正在使用 survminer 包中的 ggsurvplot 函数创建一个 survival/cumulative 事件图。我想为我的情节指定自定义时间点，但我不知道该怎么做。 xlim 和 break.x.by 参数有点帮助，但它们创建了均匀间隔的时间点和比我想要的更多的时间点。具体来说，我只想要0、30、365天的时间点。是否可以使用 survminer 包来做到这一点？如果可以使用不同的包几乎复制 ggsurvplot 的输出，那也行 - 我真的很想获得有关风险数量和事件累积数量的信息。非常感谢！
示例数据：
data <- structure(list(status = c(1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1,
0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1,
1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1,
0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0,
0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1,
0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1,
0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1,
0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0,
0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0,
0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1,
1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1,
0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1,
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1,
0, 0, 0, 0, 0, 0), time_to_trt = c(48, 202, 83, 235, 195, 158,
81, 251, 237, 408, 794, 82, 734, 697, 208, 385, 96, 384, 126,
277, 102, 95, 651, 475, 522, 119, 51, 248, 202, 76, 606, 129,
675, 399, 26, 969, 76, 491, 91, 68, 261, 185, 164, 395, 93, 176,
389, 84, 67, 245, 299, 500, 487, 347, 325, 125, 102, 62, 403,
496, 298, 264, 249, 167, 146, 67, 311, 175, 283, 654, 599, 371,
172, 367, 151, 277, 234, 81, 54, 419, 405, 244, 152, 948, 332,
174, 451, 347, 872, 436, 738, 699, 449, 578, 41, 517, 332, 56,
214, 214, 172, 48, 343, 23, 157, 874, 668, 375, 201, 55, 633,
112, 43, 245, 23, 720, 60, 405, 799, 173, 446, 363, 333, 393,
335, 294, 46, 48, 776, 237, 435, 132, 216, 58, 266, 50, 333,
171, 816, 844, 201, 135, 24, 833, 398, 354, 269, 573, 51, 391,
48, 713, 206, 148, 538, 52, 294, 139, 647, 655, 8, 25, 486, 458,
370, 314, 224, 841, 933, 191, 601, 648, 755, 727, 717, 194, 684,
37, 577, 580, 580, 399, 462, 378, 148, 71, 273, 265, 190, 160,
46, 58, 323, 370, 181, 377, 291, 254, 535, 113, 129, 440, 202,
523, 155, 556, 284, 266, 945, 281, 914, 293, 175, 805, 78, 327,
82, 77, 606, 376, 292, 168, 110, 124, 98, 170, 83, 25, 18, 54,
26, 561, 106, 45, 528, 41, 341, 259, 102, 277, 591, 256, 165,
354, 53, 356, 391, 221, 127, 444, 69, 188, 377, 54, 874, 851,
252, 42, 762, 76, 54, 96, 315, 347, 313, 109, 74, 231, 283, 223,
237, 194, 172, 321, 217, 384, 486, 466, 111, 105, 378, 140, 129,
207, 110, 518, 60, 278, 252, 363, 213, 356, 167, 45, 438, 308,
95, 374, 363, 252, 46, 172, 139, 52, 92, 34, 158, 87, 87, 207,
95, 175), trtsdt = structure(c(17605, 17885, 18074, 18065, 18025,
NA, NA, 18156, NA, 17906, NA, 17688, NA, NA, 17883, 18135, 17932,
NA, 18191, NA, NA, NA, NA, 18281, NA, 18106, 18171, NA, NA, NA,
NA, 18169, 18225, 18058, 18171, NA, 17702, 18164, 17780, 17814,
18102, NA, NA, 18085, 17963, 18326, 17820, 17634, 17687, 17834,
17974, NA, NA, 18282, NA, NA, NA, NA, 18282, NA, NA, NA, NA,
NA, NA, NA, 18103, 17800, 17935, NA, NA, 18291, 18128, NA, 18246,
NA, NA, NA, NA, NA, NA, NA, 17744, NA, 17819, 17826, NA, NA,
NA, 18003, NA, NA, 18131, NA, 17842, NA, NA, 18187, NA, NA, NA,
NA, 18116, 17871, NA, NA, NA, NA, 18248, NA, 18018, 17603, 17617,
17927, 17626, NA, 17940, NA, 18220, 17624, 17989, 18038, 18058,
18184, NA, NA, NA, NA, NA, 17856, 18304, 18045, NA, 17562, 17910,
17779, NA, 18351, NA, NA, 18247, 17598, 17507, NA, 18351, NA,
NA, 18071, 17619, NA, NA, NA, 18001, 18227, 18274, 17980, NA,
NA, 18225, NA, 17757, 17854, NA, NA, NA, NA, 18303, 18200, NA,
17745, 18172, 18233, NA, NA, NA, 17883, NA, 17771, NA, NA, NA,
18284, NA, NA, 18137, 18156, NA, NA, NA, NA, NA, 17506, 18066,
NA, 18311, NA, NA, 18059, NA, 18122, NA, NA, NA, 18010, 17723,
NA, NA, 17682, NA, 17736, NA, 17827, 17729, NA, 17729, 18009,
17834, 17841, NA, 18198, 18205, 18109, 18114, 18198, 18219, 18319,
NA, 18317, 17576, 17625, 17681, 18199, 17879, 17914, NA, 17983,
18332, NA, 17673, 18037, NA, 18065, 18121, NA, 18121, 18088,
NA, 17652, 17638, NA, 17660, 18080, NA, NA, NA, NA, 17806, 17652,
NA, 17759, 17869, 17925, 18233, NA, NA, NA, NA, 18305, NA, NA,
NA, NA, NA, 18060, 18025, 18235, NA, NA, 18200, NA, NA, 18109,
18249, NA, 17932, NA, 18121, NA, NA, NA, NA, NA, NA, NA, NA,
18226, NA, NA, NA, 18296, 18093, NA, NA, NA, 18199, 18241, NA,
NA, NA, NA, NA, NA), label = "Index Procedure Date", class = "Date", format.sas = "DATE"),
randdt = structure(c(17557, 17683, 17991, 17830, 17830, 18214,
18291, 17905, 18135, 17498, 17578, 17606, 17638, 17675, 17675,
17750, 17836, 17988, 18065, 18095, 18270, 18277, 17721, 17806,
17850, 17987, 18120, 18124, 18170, 18296, 17766, 18040, 17550,
17659, 18145, 17403, 17626, 17673, 17689, 17746, 17841, 18187,
18208, 17690, 17870, 18150, 17431, 17550, 17620, 17589, 17675,
17872, 17885, 17935, 18047, 18247, 18270, 18310, 17879, 17876,
18074, 18108, 18123, 18205, 18226, 18305, 17792, 17625, 17652,
17718, 17773, 17920, 17956, 18005, 18095, 18095, 18138, 18291,
18318, 17953, 17967, 18128, 17592, 17424, 17487, 17652, 17921,
18025, 17500, 17567, 17634, 17673, 17682, 17794, 17801, 17855,
18040, 18131, 18158, 18158, 18200, 18324, 17773, 17848, 18215,
17498, 17704, 17997, 18047, 18317, 17385, 17491, 17574, 17682,
17603, 17652, 17880, 17967, 17421, 17451, 17543, 17675, 17725,
17791, 18037, 18078, 18326, 18324, 17596, 17619, 17869, 17913,
18156, 17504, 17644, 17729, 18039, 18180, 17556, 17528, 18046,
17463, 17483, 17539, 17953, 18018, 18103, 17498, 17568, 17981,
18324, 17659, 17795, 18079, 17736, 17928, 18078, 18233, 17578,
17717, 17749, 17829, 17886, 17914, 18002, 18058, 18079, 17359,
17439, 17554, 17571, 17585, 17617, 17645, 17655, 17689, 17688,
17734, 17795, 17792, 17792, 17885, 17910, 17994, 17989, 18085,
18099, 18107, 18182, 18212, 18326, 17448, 17743, 18002, 18130,
17995, 18081, 17805, 17837, 18009, 18243, 17932, 18170, 17487,
17568, 17816, 18088, 17416, 17427, 17455, 17458, 17534, 17554,
17567, 17651, 17682, 17752, 17764, 17766, 17822, 17913, 17941,
18004, 18074, 18121, 18149, 18289, 18292, 17558, 17571, 17655,
17638, 17773, 17869, 17844, 17942, 17991, 18113, 17571, 17760,
17781, 17809, 17956, 18018, 18068, 17732, 17981, 17431, 17511,
17928, 17591, 17892, 17995, 18318, 17498, 17521, 17554, 17610,
17610, 17683, 17815, 17829, 17918, 18025, 18059, 18263, 18298,
18074, 18089, 18149, 18135, 18178, 18200, 17739, 17808, 17851,
17886, 17906, 18089, 18267, 17994, 17969, 18120, 18165, 17822,
17854, 18061, 18094, 18120, 18009, 18159, 18016, 18205, 18327,
17934, 17918, 18277, 17998, 18009, 18044, 18047, 18200, 18233,
18320, 18107, 18207, 18214, 18285, 18285, 18165, 18277, 18197
), label = "Randomization Date", class = "Date", format.sas = "DATE")), class = c("tbl_df",
"tbl", "data.frame"), row.names = c(NA, -312L))
适合：
fit <- survfit(Surv(time_to_trt, status) ~ 1, data = data)
"Bad" 尝试使用 ggsurvplot:
a <- ggsurvplot(fit,
data = data,
conf.int = FALSE,
risk.table = TRUE,
cumevents = TRUE,
fun = "event",
ggtheme = theme_minimal(),
risk.table.y.text = FALSE,
risk.table.y.text.col = TRUE,
legend = "none",
legend.title = "")
输出：
这是一种解决方法。它有点像科学怪人，但我认为它很好地展示了 ggsurvplot 函数幕后发生的事情。如果这不是您想要的，请告诉我。我可能会建议您使用此答案中的原则直接使用 ggplot 函数创建您的绘图，但我会把它留给您
ggsurvplot returns 3 ggplot 个对象作为列表的一部分：散点图和 2 个表。您可以提取这些对象，直接编辑它们，然后将它们放回列表中。首先，我使用您指定的 xlim 和 break.x.by = 30
生成绘图
a <- ggsurvplot(fit,
data = data,
conf.int = FALSE,
risk.table = TRUE,
cumevents = TRUE,
fun = "event",
ggtheme = theme_minimal(),
risk.table.y.text = FALSE,
risk.table.y.text.col = TRUE,
legend = "none",
legend.title = "",
xlim = c(0, 360),
break.x.by = 30)
a
然后我手动编辑每个 ggplot 对象以设置 x 轴中断。这两个表是用geom_text生成的。如果你想删除与刻度不对应的标签，对我来说最简单的方法是清除标签，然后用正确的 x 值手动重新创建它们。
# extract ggplot object from ggsurvplot
p <- a$plot
p <- p + scale_x_continuous(breaks = c(0, 30, 360))
# extract table object from ggsurvplot
tab <- a$table
tab$layers = NULL # clear labels
tab <- tab +
geom_text(aes(x = time, y = rev(strata), label = llabels), data = tab$data[tab$data$time %in% c(0, 30, 360),]) +
scale_x_continuous(breaks = c(0, 30, 360))
# extract cumevents object from ggsurvplot
tab2 <- a$cumevents
tab2$layers = NULL # clear labels
tab2 <- tab2 +
geom_text(aes(x = time, y = rev(strata), label = cum.n.event), data = tab$data[tab$data$time %in% c(0, 30, 360),]) +
scale_x_continuous(breaks = c(0, 30, 360))
# Add plots back
a$plot <- p
a$table <- tab
a$cumevents <- tab2
a
我正在使用 survminer 包中的 ggsurvplot 函数创建一个 survival/cumulative 事件图。我想为我的情节指定自定义时间点，但我不知道该怎么做。 xlim 和 break.x.by 参数有点帮助，但它们创建了均匀间隔的时间点和比我想要的更多的时间点。具体来说，我只想要0、30、365天的时间点。是否可以使用 survminer 包来做到这一点？如果可以使用不同的包几乎复制 ggsurvplot 的输出，那也行 - 我真的很想获得有关风险数量和事件累积数量的信息。非常感谢！
示例数据：
data <- structure(list(status = c(1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1,
0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1,
1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1,
0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0,
0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1,
0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1,
0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1,
0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0,
0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0,
0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1,
1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1,
0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1,
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1,
0, 0, 0, 0, 0, 0), time_to_trt = c(48, 202, 83, 235, 195, 158,
81, 251, 237, 408, 794, 82, 734, 697, 208, 385, 96, 384, 126,
277, 102, 95, 651, 475, 522, 119, 51, 248, 202, 76, 606, 129,
675, 399, 26, 969, 76, 491, 91, 68, 261, 185, 164, 395, 93, 176,
389, 84, 67, 245, 299, 500, 487, 347, 325, 125, 102, 62, 403,
496, 298, 264, 249, 167, 146, 67, 311, 175, 283, 654, 599, 371,
172, 367, 151, 277, 234, 81, 54, 419, 405, 244, 152, 948, 332,
174, 451, 347, 872, 436, 738, 699, 449, 578, 41, 517, 332, 56,
214, 214, 172, 48, 343, 23, 157, 874, 668, 375, 201, 55, 633,
112, 43, 245, 23, 720, 60, 405, 799, 173, 446, 363, 333, 393,
335, 294, 46, 48, 776, 237, 435, 132, 216, 58, 266, 50, 333,
171, 816, 844, 201, 135, 24, 833, 398, 354, 269, 573, 51, 391,
48, 713, 206, 148, 538, 52, 294, 139, 647, 655, 8, 25, 486, 458,
370, 314, 224, 841, 933, 191, 601, 648, 755, 727, 717, 194, 684,
37, 577, 580, 580, 399, 462, 378, 148, 71, 273, 265, 190, 160,
46, 58, 323, 370, 181, 377, 291, 254, 535, 113, 129, 440, 202,
523, 155, 556, 284, 266, 945, 281, 914, 293, 175, 805, 78, 327,
82, 77, 606, 376, 292, 168, 110, 124, 98, 170, 83, 25, 18, 54,
26, 561, 106, 45, 528, 41, 341, 259, 102, 277, 591, 256, 165,
354, 53, 356, 391, 221, 127, 444, 69, 188, 377, 54, 874, 851,
252, 42, 762, 76, 54, 96, 315, 347, 313, 109, 74, 231, 283, 223,
237, 194, 172, 321, 217, 384, 486, 466, 111, 105, 378, 140, 129,
207, 110, 518, 60, 278, 252, 363, 213, 356, 167, 45, 438, 308,
95, 374, 363, 252, 46, 172, 139, 52, 92, 34, 158, 87, 87, 207,
95, 175), trtsdt = structure(c(17605, 17885, 18074, 18065, 18025,
NA, NA, 18156, NA, 17906, NA, 17688, NA, NA, 17883, 18135, 17932,
NA, 18191, NA, NA, NA, NA, 18281, NA, 18106, 18171, NA, NA, NA,
NA, 18169, 18225, 18058, 18171, NA, 17702, 18164, 17780, 17814,
18102, NA, NA, 18085, 17963, 18326, 17820, 17634, 17687, 17834,
17974, NA, NA, 18282, NA, NA, NA, NA, 18282, NA, NA, NA, NA,
NA, NA, NA, 18103, 17800, 17935, NA, NA, 18291, 18128, NA, 18246,
NA, NA, NA, NA, NA, NA, NA, 17744, NA, 17819, 17826, NA, NA,
NA, 18003, NA, NA, 18131, NA, 17842, NA, NA, 18187, NA, NA, NA,
NA, 18116, 17871, NA, NA, NA, NA, 18248, NA, 18018, 17603, 17617,
17927, 17626, NA, 17940, NA, 18220, 17624, 17989, 18038, 18058,
18184, NA, NA, NA, NA, NA, 17856, 18304, 18045, NA, 17562, 17910,
17779, NA, 18351, NA, NA, 18247, 17598, 17507, NA, 18351, NA,
NA, 18071, 17619, NA, NA, NA, 18001, 18227, 18274, 17980, NA,
NA, 18225, NA, 17757, 17854, NA, NA, NA, NA, 18303, 18200, NA,
17745, 18172, 18233, NA, NA, NA, 17883, NA, 17771, NA, NA, NA,
18284, NA, NA, 18137, 18156, NA, NA, NA, NA, NA, 17506, 18066,
NA, 18311, NA, NA, 18059, NA, 18122, NA, NA, NA, 18010, 17723,
NA, NA, 17682, NA, 17736, NA, 17827, 17729, NA, 17729, 18009,
17834, 17841, NA, 18198, 18205, 18109, 18114, 18198, 18219, 18319,
NA, 18317, 17576, 17625, 17681, 18199, 17879, 17914, NA, 17983,
18332, NA, 17673, 18037, NA, 18065, 18121, NA, 18121, 18088,
NA, 17652, 17638, NA, 17660, 18080, NA, NA, NA, NA, 17806, 17652,
NA, 17759, 17869, 17925, 18233, NA, NA, NA, NA, 18305, NA, NA,
NA, NA, NA, 18060, 18025, 18235, NA, NA, 18200, NA, NA, 18109,
18249, NA, 17932, NA, 18121, NA, NA, NA, NA, NA, NA, NA, NA,
18226, NA, NA, NA, 18296, 18093, NA, NA, NA, 18199, 18241, NA,
NA, NA, NA, NA, NA), label = "Index Procedure Date", class = "Date", format.sas = "DATE"),
randdt = structure(c(17557, 17683, 17991, 17830, 17830, 18214,
18291, 17905, 18135, 17498, 17578, 17606, 17638, 17675, 17675,
17750, 17836, 17988, 18065, 18095, 18270, 18277, 17721, 17806,
17850, 17987, 18120, 18124, 18170, 18296, 17766, 18040, 17550,
17659, 18145, 17403, 17626, 17673, 17689, 17746, 17841, 18187,
18208, 17690, 17870, 18150, 17431, 17550, 17620, 17589, 17675,
17872, 17885, 17935, 18047, 18247, 18270, 18310, 17879, 17876,
18074, 18108, 18123, 18205, 18226, 18305, 17792, 17625, 17652,
17718, 17773, 17920, 17956, 18005, 18095, 18095, 18138, 18291,
18318, 17953, 17967, 18128, 17592, 17424, 17487, 17652, 17921,
18025, 17500, 17567, 17634, 17673, 17682, 17794, 17801, 17855,
18040, 18131, 18158, 18158, 18200, 18324, 17773, 17848, 18215,
17498, 17704, 17997, 18047, 18317, 17385, 17491, 17574, 17682,
17603, 17652, 17880, 17967, 17421, 17451, 17543, 17675, 17725,
17791, 18037, 18078, 18326, 18324, 17596, 17619, 17869, 17913,
18156, 17504, 17644, 17729, 18039, 18180, 17556, 17528, 18046,
17463, 17483, 17539, 17953, 18018, 18103, 17498, 17568, 17981,
18324, 17659, 17795, 18079, 17736, 17928, 18078, 18233, 17578,
17717, 17749, 17829, 17886, 17914, 18002, 18058, 18079, 17359,
17439, 17554, 17571, 17585, 17617, 17645, 17655, 17689, 17688,
17734, 17795, 17792, 17792, 17885, 17910, 17994, 17989, 18085,
18099, 18107, 18182, 18212, 18326, 17448, 17743, 18002, 18130,
17995, 18081, 17805, 17837, 18009, 18243, 17932, 18170, 17487,
17568, 17816, 18088, 17416, 17427, 17455, 17458, 17534, 17554,
17567, 17651, 17682, 17752, 17764, 17766, 17822, 17913, 17941,
18004, 18074, 18121, 18149, 18289, 18292, 17558, 17571, 17655,
17638, 17773, 17869, 17844, 17942, 17991, 18113, 17571, 17760,
17781, 17809, 17956, 18018, 18068, 17732, 17981, 17431, 17511,
17928, 17591, 17892, 17995, 18318, 17498, 17521, 17554, 17610,
17610, 17683, 17815, 17829, 17918, 18025, 18059, 18263, 18298,
18074, 18089, 18149, 18135, 18178, 18200, 17739, 17808, 17851,
17886, 17906, 18089, 18267, 17994, 17969, 18120, 18165, 17822,
17854, 18061, 18094, 18120, 18009, 18159, 18016, 18205, 18327,
17934, 17918, 18277, 17998, 18009, 18044, 18047, 18200, 18233,
18320, 18107, 18207, 18214, 18285, 18285, 18165, 18277, 18197
), label = "Randomization Date", class = "Date", format.sas = "DATE")), class = c("tbl_df",
"tbl", "data.frame"), row.names = c(NA, -312L))
适合：
fit <- survfit(Surv(time_to_trt, status) ~ 1, data = data)
"Bad" 尝试使用 ggsurvplot:
a <- ggsurvplot(fit,
data = data,
conf.int = FALSE,
risk.table = TRUE,
cumevents = TRUE,
fun = "event",
ggtheme = theme_minimal(),
risk.table.y.text = FALSE,
risk.table.y.text.col = TRUE,
legend = "none",
legend.title = "")
输出：
这是一种解决方法。它有点像科学怪人，但我认为它很好地展示了 ggsurvplot 函数幕后发生的事情。如果这不是您想要的，请告诉我。我可能会建议您使用此答案中的原则直接使用 ggplot 函数创建您的绘图，但我会把它留给您
ggsurvplot returns 3 ggplot 个对象作为列表的一部分：散点图和 2 个表。您可以提取这些对象，直接编辑它们，然后将它们放回列表中。首先，我使用您指定的 xlim 和 break.x.by = 30
a <- ggsurvplot(fit,
data = data,
conf.int = FALSE,
risk.table = TRUE,
cumevents = TRUE,
fun = "event",
ggtheme = theme_minimal(),
risk.table.y.text = FALSE,
risk.table.y.text.col = TRUE,
legend = "none",
legend.title = "",
xlim = c(0, 360),
break.x.by = 30)
a
然后我手动编辑每个 ggplot 对象以设置 x 轴中断。这两个表是用geom_text生成的。如果你想删除与刻度不对应的标签，对我来说最简单的方法是清除标签，然后用正确的 x 值手动重新创建它们。
# extract ggplot object from ggsurvplot
p <- a$plot
p <- p + scale_x_continuous(breaks = c(0, 30, 360))
# extract table object from ggsurvplot
tab <- a$table
tab$layers = NULL # clear labels
tab <- tab +
geom_text(aes(x = time, y = rev(strata), label = llabels), data = tab$data[tab$data$time %in% c(0, 30, 360),]) +
scale_x_continuous(breaks = c(0, 30, 360))
# extract cumevents object from ggsurvplot
tab2 <- a$cumevents
tab2$layers = NULL # clear labels
tab2 <- tab2 +
geom_text(aes(x = time, y = rev(strata), label = cum.n.event), data = tab$data[tab$data$time %in% c(0, 30, 360),]) +
scale_x_continuous(breaks = c(0, 30, 360))
# Add plots back
a$plot <- p
a$table <- tab
a$cumevents <- tab2
a