如何从列线图提取预测的点得分并将其作为协变量包含在我的数据框中？
克里米亚
请e在下面找到我的数据样本。
问题：如何提取在诺模图中生成的分数，然后将其作为协变量包含在数据框中？我想包括每一行（即患者）的个性化分数。
我目前有
> head(e)
rfs Ki67 WHO simpson age sex rad.dose recurrence
1 25.33 0.6 1 1 43 1 0 1
2 207.93 3.3 2 2 76 1 0 0
3 80.00 1.0 2 1 79 1 0 0
4 47.77 0.6 1 3 84 1 0 1
5 193.25 0.6 1 1 62 1 0 0
6 5.78 0.5 1 4 60 1 0 1
我想介绍一个新的协变量e$score。本e$score应在每个病人的水平来计算，即每行的基础上，得分点从协变量产生e$Ki67，e$simpson，e$age和e$sex。
为此，我使用以下代码生成了列线图：
library(rms)
d <- datadist(e)
options(datadist="d")
e$simpson <- as.factor(e$simpson)
e$sex <- as.factor(e$sex)
a <- cph(Surv(rfs,recurrence)~Ki67+simpson+age+sex,data=e,surv=TRUE,x=TRUE,y=TRUE)
surv <- Survival(a)
nom <- nomogram(a, fun=list(function(x) surv(12, x),
function(x) surv(36, x),
function(x) surv(60, x)),
funlabel=c("Probability of 1 year survival",
"Probability of 3 years survival",
"Probability of 5 years survival"), lp=T)
plot(nom, xfrac=.2,
total.points.label="Sum of all points",
cex.axis = 1.05,
#force.label = TRUE,
tcl = 0.8,
lmgp = 0.1,
vnames="labels",
col.grid=gray(c(0.85,0.95)))
那么，如何从中提取预测的分数nom呢？我可以看到有一个线性预测变量，并且我认为这可能很有用，但我只是想不出如何做。
因此，与病人Ki67=1 (11 point)，simpson=2 (34 point)，age=45 (7 point)和sex=0 (0 point)拥有51的总成绩，因此应该有e$score=51。
> print(nom)
Points per unit of linear predictor: 34.35364
Linear predictor units per point : 0.029109
Ki67 Points
0.0 0
0.5 6
1.0 11
1.5 17
2.0 22
2.5 28
3.0 34
3.5 39
4.0 45
4.5 50
5.0 56
5.5 61
6.0 67
6.5 73
simpson Points
1 0
2 34
3 100
4 92
age Points
25 11
30 10
35 9
40 8
45 7
50 6
55 5
60 5
65 4
70 3
75 2
80 1
85 0
sex Points
0 0
1 34
Total Points Probability of 1 year survival
188 0.60
182 0.65
175 0.70
168 0.75
159 0.80
148 0.85
133 0.90
109 0.95
Total Points Probability of 3 years survival
182 0.3
173 0.4
163 0.5
153 0.6
140 0.7
124 0.8
98 0.9
Total Points Probability of 5 years survival
182 0.1
170 0.2
160 0.3
151 0.4
141 0.5
131 0.6
118 0.7
102 0.8
76 0.9
我的资料 e
e <- structure(list(rfs = c(25.33, 207.93, 80, 47.77, 193.25, 5.78,
6.08, 99.78, 0.69, 174.85, 30.75, 27.27, 162.27, 204.98, 122.81,
2.79, 150.08, 200.02, 20.53, 22.28, 197.65, 94.23, 195.94, 92.19,
6.93, 193.38, 14.09, 152.38, 49.15, 190.46, 50.56, 66.76, 188.58,
188.42, 78.65, 125.77, 176.59, 185.69, 185.23, 184.71, 184.31,
183.59, 181.49, 96.53, 180.63, 30.16, 65.71, 179.48, 111.47,
122.61, 177.35, 176.66, 0.13, 67.15, 175.31, 5.78, 53.45, 86.74,
174.65, 7.72, 169.53, 169.23, 41.99, 168.77, 167.69, 56.71, 163.84,
163.81, 162.69, 162.63, 162.37, 119.59, 88.02, 160.1, 159.47,
12.42, 155.56, 155.47, 155.27, 154.87, 56.18, 154.61, 9.33, 128.43,
56.51, 150.67, 40.9, 50.79, 47.93, 147.91, 83.58, 146.1, 144.69,
34.73, 142.82, 159.31, 140.58, 136.64, 135.52, 88.41), Ki67 = c(0.6,
3.3, 1, 0.6, 0.6, 0.5, 3.7, 0.8, 1.4, 1.1, 1.8, 1.6, 0.7, 0.5,
0.3, 0.2, 0.3, 0.9, 1.7, 0.5, 1.2, 4.1, 0.6, 1.4, 1.3, 1.8, 2.6,
0.7, 0.8, 1, 0.7, 0.7, 2.1, 1.3, 2.7, 1.3, 0.8, 1.1, 1.8, 1.8,
0.4, 0.9, 6.4, 1.7, 1.5, 0.6, 2.7, 0.4, 0.3, 1.5, 1.4, 1.8, 2.3,
0.7, 2.4, 2.2, 2.8, 1.2, 0.6, 5.3, 0.8, 3, 4, 0.5, 1.2, 5.1,
1.5, 0.6, 1.2, 1.7, 0.7, 1.4, 0.9, 2.7, 1.1, 0.9, 0.5, 0.7, 0.9,
0.4, 1.2, 0.8, 0.7, 0.8, 0.5, 0.9, 3.3, 0.5, 1.2, 1.1, 1.4, 2.5,
2.7, 0.7, 0.8, 4.2, 0.8, 0.5, 1.7, 1.2), WHO = c(1L, 2L, 2L,
1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L,
1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L,
2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 3L, 1L,
1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L,
1L), simpson = structure(c(1L, 2L, 1L, 3L, 1L, 4L, 3L, 4L, 2L,
2L, 2L, 2L, 2L, 1L, 2L, 4L, 4L, 4L, 2L, 1L, 2L, 1L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 3L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L,
2L, 2L, 2L, 3L, 3L, 2L, 2L, 4L, 2L, 2L, 1L, 2L, 1L, 2L, 4L, 4L,
2L, 1L, 4L, 1L, 2L, 2L, 2L, 2L, 3L, 1L, 1L, 2L, 1L, 1L, 2L, 4L,
2L, 1L, 3L, 1L, 2L, 2L, 3L, 4L, 1L, 4L, 1L, 2L, 1L, 4L, 3L, 3L,
4L, 3L, 2L, 1L, 4L, 4L, 2L, 2L, 2L, 2L, 2L), .Label = c("1",
"2", "3", "4"), class = "factor"), age = c(43, 76, 79, 84, 62,
60, 71, 76, 75, 69, 53, 70, 56, 45, 77, 63, 36, 41, 72, 56, 59,
84, 72, 83, 80, 49, 50, 68, 49, 46, 50, 73, 51, 45, 42, 73, 56,
63, 30, 67, 56, 58, 72, 51, 49, 68, 65, 60, 73, 64, 52, 65, 76,
78, 74, 79, 61, 39, 30, 77, 66, 58, 49, 67, 53, 69, 41, 42, 66,
57, 52, 25, 79, 64, 48, 51, 47, 46, 44, 68, 58, 41, 64, 76, 65,
60, 56, 46, 54, 50, 66, 42, 46, 66, 74, 83, 72, 54, 51, 77),
sex = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L,
1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L,
1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L,
2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L
), .Label = c("0", "1"), class = "factor"), rad.dose = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5.4, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5.4, 0, 0, 0, 0,
0, 0, 0, 0, 0, 5.4, 0, 0, 53.24, 5.4, 0, 0, 0, 0, 0, 0, 5.4,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5.4, 0, 0,
5.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5.4, 0, 0, 5.4, 0, 0,
0, 0, 5.4, 0, 0, 0, 0, 0), recurrence = c(1L, 0L, 0L, 1L,
0L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 0L,
0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 1L,
0L, 0L, 0L, 0L, 0L, 1L)), row.names = c(NA, 100L), class = "data.frame")
苏里曼
我们可以子集nom起诉$然后使用apply
e$score <- apply(e, 1, function(x)
nom$simpson$points[nom$simpson$simpson==x['simpson']] +
nom$sex$points[nom$sex$sex==x['sex']] +
nom$age$points[nom$age$age==as.numeric(x['age'])] +
nom$Ki67$points[round(nom$Ki67$Ki67, 1)==round(as.numeric(x['Ki67']), 1)])
# the default for class 'nomogram' for print is dec=0,
# so dec=1 will print the actual value of points, e.g.
print(nom, dec=1)
Points per unit of linear predictor: 34.35364
Linear predictor units per point : 0.029109
Ki67 Points
0.2 0.0
0.3 1.1
0.4 2.2
0.5 3.4
0.6 4.5
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