在下面查看我的数据y的示例
我有对应于生存时间和事件的协变量y$resp.time和y$resp。我想估计由y$total.mbq的影响引起的危险比。
library(rms)
model <- cph(Surv(resp.time,response)~total.mbq,data=w)
summary(model)
这给出了
Effects Response : Surv(resp.time, response)
Factor Low High Diff. Effect S.E. Lower 0.95 Upper 0.95
total.mbq 7500 14850 7350 -1.20540 0.3163 -1.82530 -0.58545
Hazard Ratio 7500 14850 7350 0.29958 NA 0.16117 0.55685
y $ total.mbq是一个连续的协变量，范围为1688至29770。据我了解的模型输出，y$total.mbq的1单位增加产生的危险比= 0.3。
但是，我认为如果我报告每y$total.mbq
可以做到吗？
y <- structure(list(resp.time = c(18, 2, 13, 17, 22, 2, 6, 5, 12,
8, 3, 2, 1, 21, 2, 43, 4, 2, 4, 5, 0.1, 137, 4, 87, 17, 24, 72,
19, 14, 83, 57, 14, 15.6, 8.9, 15, 16.4, 7.7, 75.5, 3, 54.8,
22.2, 12, 14.3, 6, 12, 6, 3, 12, 3, 6, 3, 3, 12, 9.7, 3, 3, 12,
3, 6, 3, 6, 4, 50, 21, 30, 5, 11, 12), response = c(0L, 1L, 0L,
0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 1L,
0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L,
0L), total.mbq = c(29354L, 7445L, 22309L, 29699L, 29711L, 14765L,
22257L, 29715L, 29772L, 13320L, 20905L, 12950L, 3400L, 14800L,
7400L, 21890L, 19400L, 14800L, 14700L, 22200L, 1688L, 4500L,
8438L, 13500L, 14800L, 12580L, 12950L, 13320L, 11840L, 13320L,
11250L, 13320L, 7500L, 5000L, 12500L, 12500L, 10000L, 12500L,
7500L, 15000L, 10000L, 5000L, 7500L, 5000L, 15000L, 7500L, 7500L,
7500L, 5000L, 10000L, 10000L, 10000L, 12500L, 5000L, 5000L, 10000L,
12500L, 5000L, 10000L, 10000L, 22200L, 14800L, 29000L, 14000L,
4800L, 21600L, 28800L, 11400L)), class = "data.frame", row.names = c(1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L,
16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L,
29L, 30L, 33L, 35L, 70L, 71L, 72L, 73L, 74L, 75L, 76L, 77L, 78L,
79L, 80L, 81L, 82L, 84L, 85L, 86L, 87L, 88L, 89L, 90L, 91L, 92L,
93L, 94L, 95L, 96L, 97L, 98L, 99L, 100L, 101L, 102L, 103L, 104L,
105L, 106L))