调整cox回归图
调整cox回归图
提问于 2022-03-26 08:31:33
我想做一个调整的cox回归分析，像这样的生存曲线，但调整为基线测量的sex，age，trop，egfr，dm和smoke。该图形必须按照delta_mon3_baseline_to_m1的四分位数分层。
下面是得到这个单变量曲线的代码：
quantile <- df$delta_mon3_baseline_to_m1fit <- survfit(Surv(mace_months_date_vs_date_sample, mace) ~ findInterval(quantile, quantile(quantile, na.rm = TRUE)[-5]), data = df)custom_theme <- function(){
theme_survminer() %+replace%
theme(
legend.background = element_rect(fill = "white", color = "black"),
plot.title=element_text(hjust=0.9)
)
}ggsurvplot(fit,
pval = FALSE,
ggtheme = custom_theme(),
censor = FALSE,
legend = c(0.5, 0.2),
legend.title = "Mon3, P=0.02",
legend.labs = c("Quartile 1", "Quartile 2", "Quartile 3", "Quartile 4"),
xlab = "Follow-up (months)",
font.x = c(size = 15),
ylab = "Survival from MACE",
font.y = c(size = 15),
break.y.by = 0.2,
axes.offset = FALSE,
palette = c("blue", "dark red", "green", "orange"))以下是我的数据：
ID age sex mace mace_months_date_vs_date_sample trop egfr dm smoke delta_mon3_baseline_to_m1
1 121 NA 1 0 43 21876 87 0 0 -83.2
2 13 53 1 0 69 1970 87 1 1 -60.4
3 192 59 1 0 44 871 90 1 0 -52.2
4 120 71 1 1 5 7860 58 1 0 -46.1
5 54 59 1 0 71 1500 81 1 1 -45.0
6 68 58 1 0 62 NA 90 0 1 -36.2
7 41 57 0 0 73 320 79 0 1 -34.0
8 23 54 1 0 72 8450 55 0 1 -33.5
9 16 57 1 0 73 180 99 0 1 -30.6
10 45 51 0 0 73 3710 65 0 1 -28.5
11 216 69 1 0 47 1730 51 0 1 -27.1
12 61 48 1 0 76 4470 90 0 1 -26.4
13 24 47 1 0 77 2390 90 0 1 -25.5
14 136 49 1 0 61 262 90 0 1 -25.3
15 14 74 1 1 6 7120 78 0 0 -20.5
16 88 65 1 0 47 16720 58 1 0 -15.7
17 87 48 1 0 46 1247 90 0 1 -13.8
18 33 59 1 0 69 2260 79 0 1 -13.4
19 182 35 1 0 44 NA 56 0 1 -8.97
20 93 44 1 0 43 NA 62 0 1 -8.61
21 154 65 1 0 46 54592 86 0 0 -7.73
22 94 56 1 0 45 1241 61 0 1 -6.43
23 116 66 0 1 16 2413 84 1 1 -5.33
24 145 41 0 0 62 NA 63 0 0 -5.03
25 44 52 1 1 30 2600 56 1 0 -4.53
26 228 55 1 1 8 45649 63 0 0 -3.49
27 76 52 1 0 48 NA 90 0 0 0.407
28 63 67 1 1 15 NA 90 0 0 2.77
29 59 61 1 0 79 160 85 0 1 3.37
30 42 38 1 0 69 1800 69 0 1 4.49
31 219 65 1 0 62 843 64 0 0 7.30
32 47 62 1 0 69 1420 85 0 0 8.13
33 56 43 1 0 70 1360 90 1 0 9.43
34 175 61 0 0 46 NA 67 1 1 10.6
35 164 75 0 0 62 1470 45 0 1 11.1
36 181 56 1 0 45 372 90 0 0 12.7
37 234 37 1 0 55 844 78 0 0 13.0
38 146 75 1 1 21 1454 65 1 1 14.5
39 28 76 1 0 71 40 90 1 1 15.4
40 242 68 0 1 41 50000 69 0 1 15.5
41 52 49 1 0 74 1740 71 0 1 19.3
42 71 59 1 0 45 11450 90 0 0 19.4
43 67 52 1 0 46 769 90 0 1 19.7
44 187 59 1 0 45 2234 59 0 0 21.4
45 128 59 1 0 50 349 54 0 1 22.0
46 27 28 1 0 71 6440 90 0 1 23.3
47 151 63 0 0 62 250 60 0 1 23.4
48 215 62 0 0 43 3654 68 0 0 26.5
49 132 57 1 1 4 8421 86 0 0 26.7
50 21 46 1 0 75 3600 87 0 1 27.4
51 173 48 1 1 1 NA 42 0 1 29.6
52 124 65 0 1 4 5204 73 1 1 29.7
53 119 41 1 0 63 440 90 0 0 31.9
54 224 60 1 1 17 NA 85 0 0 34.9
55 29 73 0 0 77 NA 51 0 0 39.5
56 49 54 1 1 28 NA 76 1 0 64.7
57 140 77 1 1 3 40273 63 1 1 66.8
58 221 66 1 0 45 90 90 0 0 70.4
59 172 50 1 1 2 9352 86 0 1 72.1
60 115 51 0 0 50 2177 68 0 1 73.0
61 12 57 1 0 71 6020 88 0 1 84.1
62 103 81 1 1 1 10014 71 0 0 95.7
63 223 63 1 0 46 4281 90 0 0 NA
64 32 66 1 0 73 1710 90 1 0 NA
65 50 54 1 0 70 15300 90 0 1 NA
66 57 58 1 0 74 5010 68 0 1 NA
67 20 56 1 1 5 NA 75 0 1 NA
68 17 44 1 0 77 840 71 0 0 NA
69 79 68 1 0 45 776 90 0 0 NA
70 35 47 1 0 77 NA 83 0 0 NA
71 22 46 1 0 77 5330 88 0 1 NA
72 26 66 1 0 77 500 51 0 0 NA
73 25 54 1 0 73 1080 87 0 0 NA
74 31 47 0 0 72 6490 77 1 1 NA
75 15 65 1 1 43 6300 49 1 0 NA
76 43 59 0 0 74 2100 84 0 1 NA
77 36 64 1 1 5 15940 52 0 1 NA
78 30 73 0 0 68 3340 48 1 1 NA
79 39 54 1 0 74 990 77 0 0 NA
80 60 72 1 0 76 470 62 0 0 NA
81 55 47 1 0 70 NA 90 0 1 NA
82 37 81 1 0 74 NA 99 1 1 NA
83 19 66 1 0 78 9320 59 0 0 NA
84 139 39 0 0 58 NA 90 0 1 NA
85 38 55 1 0 78 3930 90 1 0 NA
86 18 56 1 0 69 6390 90 0 1 NA
87 58 36 1 0 76 NA 78 1 1 NA
88 73 61 1 0 44 11 90 0 0 NA
89 194 64 1 1 15 11135 68 0 1 NA
90 106 48 1 0 46 5256 63 0 1 NA
91 193 63 1 0 46 1753 81 0 0 NA
92 148 78 1 1 7 NA 76 1 1 NA
93 156 79 1 1 8 NA 61 1 0 NA
94 203 51 0 0 62 50 75 0 1 NA
95 100 74 1 1 16 8903 84 0 0 NA
96 81 52 1 0 47 3598 90 0 1 NA
97 190 73 0 1 19 2483 90 1 0 NA
98 206 82 0 0 61 NA 48 0 0 NA
99 233 58 1 0 46 NA 90 0 1 NA
100 189 72 0 1 1 NA 77 0 1 NA
101 105 63 1 1 4 1557 68 0 1 NA
102 220 67 1 0 48 5247 90 0 1 NA
103 85 72 0 1 61 24 64 0 1 NA
104 11 51 1 0 43 2993 90 0 1 NA
105 205 68 1 0 46 3624 90 1 1 NA
106 131 41 1 0 61 751 61 0 1 NA
107 1 66 1 0 44 NA 84 1 1 NA
108 225 69 1 0 48 NA 80 1 1 NA
109 183 60 1 1 1 24160 69 0 1 NA
110 213 66 1 1 5 NA 58 0 0 NA
111 7 71 1 0 43 NA 65 0 1 NA
112 147 45 1 0 43 5687 86 1 1 NA
113 134 81 0 0 46 1911 86 0 0 NA
114 86 52 1 0 58 NA 46 0 1 NA
115 69 67 1 1 3 NA 56 0 0 NA
116 212 58 1 0 55 1855 90 0 0 NA
117 199 63 0 1 27 9951 46 0 1 NA
118 75 47 1 1 1 13374 87 0 0 NA
119 137 63 0 0 47 2107 84 1 1 NA
120 191 67 1 1 19 4927 68 0 0 NA
121 245 62 1 1 11 83 67 0 1 NA
122 111 72 1 1 1 2380 72 0 0 NA
123 153 75 0 1 1 663 35 0 0 NA
124 112 46 1 1 1 NA 86 0 1 NA
125 89 73 1 1 2 NA 74 0 0 NA
126 243 57 1 0 48 NA 78 1 1 NA
127 109 75 1 0 62 NA 67 0 1 NA
128 165 68 0 0 61 NA 90 0 1 NA
129 95 68 1 1 23 NA 90 0 0 NA
130 231 73 1 1 1 NA 90 0 1 NA
131 5 51 1 0 55 1627 76 0 0 NA
132 168 59 1 1 19 NA 90 1 1 NA
133 159 59 1 0 48 1211 86 0 0 NA
134 6 60 1 1 1 5654 63 0 1 NA
135 179 54 1 0 43 NA 63 0 0 NA
136 77 62 1 0 47 NA 53 0 0 NA
137 155 64 1 1 1 10000 90 0 0 NA
138 171 48 1 0 46 NA 90 0 0 NA
139 174 73 1 1 11 NA 75 1 1 NA
140 84 79 1 0 46 NA 64 0 0 NA
141 102 58 1 0 48 2956 69 0 0 NA
142 207 46 0 0 46 67927 68 0 1 NA
143 230 75 1 1 32 NA 52 1 0 NA
144 138 63 0 0 50 63 49 0 1 NA
145 188 68 1 0 61 NA 90 0 1 NA
146 241 60 1 0 62 4790 65 1 1 NA
147 72 54 1 0 48 NA 86 0 1 NA
148 235 78 0 1 46 NA 42 1 0 NA
149 211 54 1 0 55 3569 63 0 1 NA
150 127 46 0 0 62 961 90 0 1 NA
151 237 49 0 0 55 6581 90 0 1 NA
152 70 67 1 0 43 253 73 1 0 NA
153 210 79 0 0 61 2888 75 0 0 NA
154 110 54 0 0 47 33017 66 0 0 NA
155 133 47 1 0 59 1675 64 0 0 NA
156 2 51 1 0 50 438 90 0 1 NA
157 218 66 1 1 10 15543 35 0 0 NA
158 180 61 1 0 55 6212 90 0 1 NA
159 229 64 0 0 63 6694 82 1 1 NA
160 65 79 0 0 55 NA 90 1 1 NA
161 130 73 1 0 48 1945 84 0 0 NA
162 96 51 0 1 31 3004 61 0 1 NA
163 226 52 1 1 32 3789 90 0 1 NA
164 152 52 1 0 48 NA 86 0 0 NA
165 197 76 0 0 55 2844 51 0 0 NA
166 178 75 0 0 62 950 52 0 1 NA
167 141 56 1 0 59 123 69 1 1 NA
168 195 76 1 1 53 6630 54 1 0 NA
169 92 54 1 0 48 3220 90 0 1 NA
170 162 82 0 0 48 2040 75 1 0 NA
171 201 62 1 0 62 NA 85 0 1 NA
172 217 57 0 0 59 6672 72 0 1 NA
173 222 53 1 1 4 1480 90 0 1 NA
174 208 42 1 1 4 6979 80 0 1 NA
175 104 63 0 1 25 NA 54 0 1 NA
176 160 37 1 0 59 1411 58 0 1 NA
177 66 66 1 0 48 5711 90 0 0 NA
178 74 46 1 0 60 NA 89 0 1 NA
179 185 76 1 1 43 2340 72 0 0 NA
180 177 39 1 0 58 NA 90 0 1 NA
181 123 51 1 0 49 57 84 0 1 NA
182 184 80 1 1 1 NA 33 1 1 NA
183 204 69 1 1 8 33 74 0 1 NA
184 227 76 1 1 1 5110 36 1 1 NA
185 125 48 1 0 63 NA 56 0 1 NA
186 83 65 1 0 51 2797 61 0 0 NA
187 8 59 1 0 55 1035 63 0 1 NA
188 143 76 1 0 47 2840 77 0 1 NA
189 9 57 0 0 49 251 84 0 1 NA
190 3 66 0 0 55 7671 85 0 0 NA
191 117 69 0 0 49 6155 90 0 1 NA
192 10 68 1 0 55 4299 90 1 1 NA
193 198 72 1 1 13 NA 51 1 0 NA
194 244 62 0 0 60 846 90 0 1 NA
195 108 56 1 1 2 2339 90 0 1 NA
196 34 51 1 1 13 400 68 1 0 NA
197 214 60 1 1 3 86115 90 0 1 NA
198 4 71 1 1 25 27 52 0 0 NA
199 97 68 1 1 7 87355 90 1 0 NA
200 200 77 1 0 62 NA 48 1 0 NA
201 113 28 1 0 62 8669 90 0 1 NA
202 80 62 1 0 60 NA 82 0 0 NA
203 166 51 1 1 10 NA 86 0 1 NA
204 98 61 1 0 61 NA 67 0 0 NA
205 238 56 1 0 45 1258 90 0 1 NA
206 239 72 1 1 3 3000 76 1 1 NA
207 114 79 0 1 11 NA 14 1 1 NA
208 167 62 1 0 51 137 63 0 0 NA
209 64 68 0 0 47 3866 59 1 1 NA
210 157 68 0 1 1 NA 82 0 0 NA
211 90 49 1 0 46 1312 90 0 0 NA
212 149 75 0 0 63 NA 39 1 1 NA
213 129 64 1 0 60 NA 77 0 1 NA
214 170 48 0 0 43 NA 90 0 0 NA
215 91 51 1 0 62 NA 78 2 1 NA
216 135 68 1 0 58 NA 74 1 1 NA
217 122 68 1 0 61 2103 54 0 1 NA
218 240 70 0 1 1 1586 36 0 0 NA
219 99 73 1 1 2 601 58 0 1 NA
220 236 54 1 1 46 1472 69 0 1 NA
221 144 47 1 1 1 1692 NA 0 1 NA
222 53 79 1 1 13 NA 53 1 0 NA
223 176 40 0 0 54 2102 90 0 1 NA
224 107 52 1 0 48 6452 90 0 1 NA
225 232 58 1 0 54 NA 90 0 0 NA
226 163 69 1 0 45 NA 88 0 0 NA
227 142 61 1 0 45 1244 90 0 0 NA
228 118 44 1 1 31 2051 90 0 1 NA
229 126 57 1 0 48 1007 90 0 0 NA
230 158 55 1 0 42 NA 90 0 0 NA
231 186 43 1 0 49 NA 90 0 1 NA
232 82 61 1 0 43 NA 63 0 0 NA
233 78 44 1 0 61 1726 87 0 0 NA
234 48 77 1 1 1 3400 48 0 0 NA
235 62 35 1 1 31 2143 90 0 0 NA
236 209 74 0 1 1 NA 55 1 0 NA
237 196 72 0 0 63 236 70 NA NA NA
238 46 60 1 1 11 3930 65 1 0 NA
239 150 44 1 0 47 31026 90 1 0 NA
240 161 53 1 1 39 NA NA 0 1 NA
241 169 61 1 1 7 NA 90 0 0 NA
242 101 NA 1 0 42 NA 90 0 0 NA
243 202 80 1 1 1 NA 72 0 0 NA
244 51 73 0 1 1 5280 66 0 0 NA
245 40 72 1 1 1 1230 55 0 1 NA
使用此代码：
df$quantile <- findInterval(quantile, quantile(quantile, na.rm = TRUE)[-5])
df$surv <- Surv(df$mace_months_date_vs_date_sample, df$mace)
mod <- coxph(surv ~ strata(quantile) + dm + age + sex + trop + egfr + smoke,
data = df)
ggsurvplot(survfit(mod, data = df),
pval = FALSE,
ggtheme = custom_theme(),
censor = FALSE,
legend = c(0.5, 0.2),
legend.title = "Mon3, P=0.02",
legend.labs = c("Quartile 1", "Quartile 2", "Quartile 3", "Quartile 4"),
xlab = "Follow-up (months)",
font.x = c(size = 15),
ylab = "Survival from MACE",
font.y = c(size = 15),
break.y.by = 0.2,
axes.offset = FALSE,
palette = c("blue", "dark red", "green", "orange"))非常感谢你的帮助。
回答 1
Stack Overflow用户
回答已采纳
发布于 2022-03-28 09:32:54
使用完整的数据集，我们现在可以像这样绘制coxph模型：
df$quantile <- findInterval(quantile, quantile(quantile, na.rm = TRUE)[-5])
df$surv <- Surv(df$mace_months_date_vs_date_sample, df$mace)
mod <- coxph(surv ~ strata(quantile) + dm + age + sex + trop + egfr + smoke,
data = df)
ggsurvplot(survfit(mod, data = df),
pval = FALSE,
ggtheme = custom_theme(),
censor = FALSE,
legend = c(0.5, 0.2),
legend.title = "Mon3, P=0.02",
legend.labs = c("Quartile 1", "Quartile 2", "Quartile 3", "Quartile 4"),
xlab = "Follow-up (months)",
font.x = c(size = 15),
ylab = "Survival from MACE",
font.y = c(size = 15),
break.y.by = 0.2,
axes.offset = FALSE,
palette = c("blue", "dark red", "green", "orange"))
完全可复制脚本
library(survival)
library(ggplot2)
library(survminer)
df <- structure(list(ID = c(121L, 13L, 192L, 120L, 54L, 68L, 41L, 23L,
16L, 45L, 216L, 61L, 24L, 136L, 14L, 88L, 87L, 33L, 182L, 93L,
154L, 94L, 116L, 145L, 44L, 228L, 76L, 63L, 59L, 42L, 219L, 47L,
56L, 175L, 164L, 181L, 234L, 146L, 28L, 242L, 52L, 71L, 67L,
187L, 128L, 27L, 151L, 215L, 132L, 21L, 173L, 124L, 119L, 224L,
29L, 49L, 140L, 221L, 172L, 115L, 12L, 103L, 223L, 32L, 50L,
57L, 20L, 17L, 79L, 35L, 22L, 26L, 25L, 31L, 15L, 43L, 36L, 30L,
39L, 60L, 55L, 37L, 19L, 139L, 38L, 18L, 58L, 73L, 194L, 106L,
193L, 148L, 156L, 203L, 100L, 81L, 190L, 206L, 233L, 189L, 105L,
220L, 85L, 11L, 205L, 131L, 1L, 225L, 183L, 213L, 7L, 147L, 134L,
86L, 69L, 212L, 199L, 75L, 137L, 191L, 245L, 111L, 153L, 112L,
89L, 243L, 109L, 165L, 95L, 231L, 5L, 168L, 159L, 6L, 179L, 77L,
155L, 171L, 174L, 84L, 102L, 207L, 230L, 138L, 188L, 241L, 72L,
235L, 211L, 127L, 237L, 70L, 210L, 110L, 133L, 2L, 218L, 180L,
229L, 65L, 130L, 96L, 226L, 152L, 197L, 178L, 141L, 195L, 92L,
162L, 201L, 217L, 222L, 208L, 104L, 160L, 66L, 74L, 185L, 177L,
123L, 184L, 204L, 227L, 125L, 83L, 8L, 143L, 9L, 3L, 117L, 10L,
198L, 244L, 108L, 34L, 214L, 4L, 97L, 200L, 113L, 80L, 166L,
98L, 238L, 239L, 114L, 167L, 64L, 157L, 90L, 149L, 129L, 170L,
91L, 135L, 122L, 240L, 99L, 236L, 144L, 53L, 176L, 107L, 232L,
163L, 142L, 118L, 126L, 158L, 186L, 82L, 78L, 48L, 62L, 209L,
196L, 46L, 150L, 161L, 169L, 101L, 202L, 51L, 40L), age = c(NA,
53L, 59L, 71L, 59L, 58L, 57L, 54L, 57L, 51L, 69L, 48L, 47L, 49L,
74L, 65L, 48L, 59L, 35L, 44L, 65L, 56L, 66L, 41L, 52L, 55L, 52L,
67L, 61L, 38L, 65L, 62L, 43L, 61L, 75L, 56L, 37L, 75L, 76L, 68L,
49L, 59L, 52L, 59L, 59L, 28L, 63L, 62L, 57L, 46L, 48L, 65L, 41L,
60L, 73L, 54L, 77L, 66L, 50L, 51L, 57L, 81L, 63L, 66L, 54L, 58L,
56L, 44L, 68L, 47L, 46L, 66L, 54L, 47L, 65L, 59L, 64L, 73L, 54L,
72L, 47L, 81L, 66L, 39L, 55L, 56L, 36L, 61L, 64L, 48L, 63L, 78L,
79L, 51L, 74L, 52L, 73L, 82L, 58L, 72L, 63L, 67L, 72L, 51L, 68L,
41L, 66L, 69L, 60L, 66L, 71L, 45L, 81L, 52L, 67L, 58L, 63L, 47L,
63L, 67L, 62L, 72L, 75L, 46L, 73L, 57L, 75L, 68L, 68L, 73L, 51L,
59L, 59L, 60L, 54L, 62L, 64L, 48L, 73L, 79L, 58L, 46L, 75L, 63L,
68L, 60L, 54L, 78L, 54L, 46L, 49L, 67L, 79L, 54L, 47L, 51L, 66L,
61L, 64L, 79L, 73L, 51L, 52L, 52L, 76L, 75L, 56L, 76L, 54L, 82L,
62L, 57L, 53L, 42L, 63L, 37L, 66L, 46L, 76L, 39L, 51L, 80L, 69L,
76L, 48L, 65L, 59L, 76L, 57L, 66L, 69L, 68L, 72L, 62L, 56L, 51L,
60L, 71L, 68L, 77L, 28L, 62L, 51L, 61L, 56L, 72L, 79L, 62L, 68L,
68L, 49L, 75L, 64L, 48L, 51L, 68L, 68L, 70L, 73L, 54L, 47L, 79L,
40L, 52L, 58L, 69L, 61L, 44L, 57L, 55L, 43L, 61L, 44L, 77L, 35L,
74L, 72L, 60L, 44L, 53L, 61L, NA, 80L, 73L, 72L), sex = c(1L,
1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
0L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L,
1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 1L,
1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 0L,
0L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L,
1L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L,
1L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L,
0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 1L,
0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L,
0L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L,
1L, 1L, 0L, 1L), mace = c(0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L,
1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 1L,
0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L,
0L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L,
0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 1L,
1L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 1L,
0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 0L,
0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L,
0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L,
1L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 1L),
mace_months_date_vs_date_sample = c(43L,
69L, 44L, 5L, 71L, 62L, 73L, 72L, 73L, 73L, 47L, 76L, 77L, 61L,
6L, 47L, 46L, 69L, 44L, 43L, 46L, 45L, 16L, 62L, 30L, 8L, 48L,
15L, 79L, 69L, 62L, 69L, 70L, 46L, 62L, 45L, 55L, 21L, 71L, 41L,
74L, 45L, 46L, 45L, 50L, 71L, 62L, 43L, 4L, 75L, 1L, 4L, 63L,
17L, 77L, 28L, 3L, 45L, 2L, 50L, 71L, 1L, 46L, 73L, 70L, 74L,
5L, 77L, 45L, 77L, 77L, 77L, 73L, 72L, 43L, 74L, 5L, 68L, 74L,
76L, 70L, 74L, 78L, 58L, 78L, 69L, 76L, 44L, 15L, 46L, 46L, 7L,
8L, 62L, 16L, 47L, 19L, 61L, 46L, 1L, 4L, 48L, 61L, 43L, 46L,
61L, 44L, 48L, 1L, 5L, 43L, 43L, 46L, 58L, 3L, 55L, 27L, 1L,
47L, 19L, 11L, 1L, 1L, 1L, 2L, 48L, 62L, 61L, 23L, 1L, 55L, 19L,
48L, 1L, 43L, 47L, 1L, 46L, 11L, 46L, 48L, 46L, 32L, 50L, 61L,
62L, 48L, 46L, 55L, 62L, 55L, 43L, 61L, 47L, 59L, 50L, 10L, 55L,
63L, 55L, 48L, 31L, 32L, 48L, 55L, 62L, 59L, 53L, 48L, 48L, 62L,
59L, 4L, 4L, 25L, 59L, 48L, 60L, 43L, 58L, 49L, 1L, 8L, 1L, 63L,
51L, 55L, 47L, 49L, 55L, 49L, 55L, 13L, 60L, 2L, 13L, 3L, 25L,
7L, 62L, 62L, 60L, 10L, 61L, 45L, 3L, 11L, 51L, 47L, 1L, 46L,
63L, 60L, 43L, 62L, 58L, 61L, 1L, 2L, 46L, 1L, 13L, 54L, 48L,
54L, 45L, 45L, 31L, 48L, 42L, 49L, 43L, 61L, 1L, 31L, 1L, 63L,
11L, 47L, 39L, 7L, 42L, 1L, 1L, 1L), trop = c(21876L, 1970L,
871L, 7860L, 1500L, NA, 320L, 8450L, 180L, 3710L, 1730L, 4470L,
2390L, 262L, 7120L, 16720L, 1247L, 2260L, NA, NA, 54592L, 1241L,
2413L, NA, 2600L, 45649L, NA, NA, 160L, 1800L, 843L, 1420L, 1360L,
NA, 1470L, 372L, 844L, 1454L, 40L, 50000L, 1740L, 11450L, 769L,
2234L, 349L, 6440L, 250L, 3654L, 8421L, 3600L, NA, 5204L, 440L,
NA, NA, NA, 40273L, 90L, 9352L, 2177L, 6020L, 10014L, 4281L,
1710L, 15300L, 5010L, NA, 840L, 776L, NA, 5330L, 500L, 1080L,
6490L, 6300L, 2100L, 15940L, 3340L, 990L, 470L, NA, NA, 9320L,
NA, 3930L, 6390L, NA, 11L, 11135L, 5256L, 1753L, NA, NA, 50L,
8903L, 3598L, 2483L, NA, NA, NA, 1557L, 5247L, 24L, 2993L, 3624L,
751L, NA, NA, 24160L, NA, NA, 5687L, 1911L, NA, NA, 1855L, 9951L,
13374L, 2107L, 4927L, 83L, 2380L, 663L, NA, NA, NA, NA, NA, NA,
NA, 1627L, NA, 1211L, 5654L, NA, NA, 10000L, NA, NA, NA, 2956L,
67927L, NA, 63L, NA, 4790L, NA, NA, 3569L, 961L, 6581L, 253L,
2888L, 33017L, 1675L, 438L, 15543L, 6212L, 6694L, NA, 1945L,
3004L, 3789L, NA, 2844L, 950L, 123L, 6630L, 3220L, 2040L, NA,
6672L, 1480L, 6979L, NA, 1411L, 5711L, NA, 2340L, NA, 57L, NA,
33L, 5110L, NA, 2797L, 1035L, 2840L, 251L, 7671L, 6155L, 4299L,
NA, 846L, 2339L, 400L, 86115L, 27L, 87355L, NA, 8669L, NA, NA,
NA, 1258L, 3000L, NA, 137L, 3866L, NA, 1312L, NA, NA, NA, NA,
NA, 2103L, 1586L, 601L, 1472L, 1692L, NA, 2102L, 6452L, NA, NA,
1244L, 2051L, 1007L, NA, NA, NA, 1726L, 3400L, 2143L, NA, 236L,
3930L, 31026L, NA, NA, NA, NA, 5280L, 1230L), egfr = c(87L, 87L,
90L, 58L, 81L, 90L, 79L, 55L, 99L, 65L, 51L, 90L, 90L, 90L, 78L,
58L, 90L, 79L, 56L, 62L, 86L, 61L, 84L, 63L, 56L, 63L, 90L, 90L,
85L, 69L, 64L, 85L, 90L, 67L, 45L, 90L, 78L, 65L, 90L, 69L, 71L,
90L, 90L, 59L, 54L, 90L, 60L, 68L, 86L, 87L, 42L, 73L, 90L, 85L,
51L, 76L, 63L, 90L, 86L, 68L, 88L, 71L, 90L, 90L, 90L, 68L, 75L,
71L, 90L, 83L, 88L, 51L, 87L, 77L, 49L, 84L, 52L, 48L, 77L, 62L,
90L, 99L, 59L, 90L, 90L, 90L, 78L, 90L, 68L, 63L, 81L, 76L, 61L,
75L, 84L, 90L, 90L, 48L, 90L, 77L, 68L, 90L, 64L, 90L, 90L, 61L,
84L, 80L, 69L, 58L, 65L, 86L, 86L, 46L, 56L, 90L, 46L, 87L, 84L,
68L, 67L, 72L, 35L, 86L, 74L, 78L, 67L, 90L, 90L, 90L, 76L, 90L,
86L, 63L, 63L, 53L, 90L, 90L, 75L, 64L, 69L, 68L, 52L, 49L, 90L,
65L, 86L, 42L, 63L, 90L, 90L, 73L, 75L, 66L, 64L, 90L, 35L, 90L,
82L, 90L, 84L, 61L, 90L, 86L, 51L, 52L, 69L, 54L, 90L, 75L, 85L,
72L, 90L, 80L, 54L, 58L, 90L, 89L, 72L, 90L, 84L, 33L, 74L, 36L,
56L, 61L, 63L, 77L, 84L, 85L, 90L, 90L, 51L, 90L, 90L, 68L, 90L,
52L, 90L, 48L, 90L, 82L, 86L, 67L, 90L, 76L, 14L, 63L, 59L, 82L,
90L, 39L, 77L, 90L, 78L, 74L, 54L, 36L, 58L, 69L, NA, 53L, 90L,
90L, 90L, 88L, 90L, 90L, 90L, 90L, 90L, 63L, 87L, 48L, 90L, 55L,
70L, 65L, 90L, NA, 90L, 90L, 72L, 66L, 55L), dm = c(0L, 1L, 1L,
1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L,
0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L,
0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L,
0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L,
0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L,
1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L,
1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L,
0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L,
1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L,
1L, 0L, 0L, 2L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, NA, 1L, 1L, 0L, 0L, 0L, 0L,
0L, 0L), smoke = c(0L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L,
0L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 1L,
0L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 1L,
1L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L,
1L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L,
1L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 0L,
1L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 1L,
0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L,
1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 1L,
1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L,
1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 1L,
0L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L,
1L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L,
0L, NA, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L), delta_mon3_baseline_to_m1 = c(-83.2,
-60.4, -52.2, -46.1, -45, -36.2, -34, -33.5, -30.6, -28.5, -27.1,
-26.4, -25.5, -25.3, -20.5, -15.7, -13.8, -13.4, -8.97, -8.61,
-7.73, -6.43, -5.33, -5.03, -4.53, -3.49, 0.407, 2.77, 3.37,
4.49, 7.3, 8.13, 9.43, 10.6, 11.1, 12.7, 13, 14.5, 15.4, 15.5,
19.3, 19.4, 19.7, 21.4, 22, 23.3, 23.4, 26.5, 26.7, 27.4, 29.6,
29.7, 31.9, 34.9, 39.5, 64.7, 66.8, 70.4, 72.1, 73, 84.1, 95.7,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA)), class = "data.frame", row.names = c("1",
"2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13",
"14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24",
"25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35",
"36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46",
"47", "48", "49", "50", "51", "52", "53", "54", "55", "56", "57",
"58", "59", "60", "61", "62", "63", "64", "65", "66", "67", "68",
"69", "70", "71", "72", "73", "74", "75", "76", "77", "78", "79",
"80", "81", "82", "83", "84", "85", "86", "87", "88", "89", "90",
"91", "92", "93", "94", "95", "96", "97", "98", "99", "100",
"101", "102", "103", "104", "105", "106", "107", "108", "109",
"110", "111", "112", "113", "114", "115", "116", "117", "118",
"119", "120", "121", "122", "123", "124", "125", "126", "127",
"128", "129", "130", "131", "132", "133", "134", "135", "136",
"137", "138", "139", "140", "141", "142", "143", "144", "145",
"146", "147", "148", "149", "150", "151", "152", "153", "154",
"155", "156", "157", "158", "159", "160", "161", "162", "163",
"164", "165", "166", "167", "168", "169", "170", "171", "172",
"173", "174", "175", "176", "177", "178", "179", "180", "181",
"182", "183", "184", "185", "186", "187", "188", "189", "190",
"191", "192", "193", "194", "195", "196", "197", "198", "199",
"200", "201", "202", "203", "204", "205", "206", "207", "208",
"209", "210", "211", "212", "213", "214", "215", "216", "217",
"218", "219", "220", "221", "222", "223", "224", "225", "226",
"227", "228", "229", "230", "231", "232", "233", "234", "235",
"236", "237", "238", "239", "240", "241", "242", "243", "244",
"245"))
quantile <- df$delta_mon3_baseline_to_m1
df$quantile <- findInterval(quantile, quantile(quantile, na.rm = TRUE)[-5])
df$surv <- Surv(df$mace_months_date_vs_date_sample, df$mace)
mod <- coxph(surv ~ strata(quantile) + dm + age + sex + trop + egfr + smoke,
data = df)
custom_theme <- function(){
theme_survminer() %+replace%
theme(
legend.background = element_rect(fill = "white", color = "black"),
plot.title=element_text(hjust=0.9)
)
}
ggsurvplot(survfit(mod, data = df),
pval = FALSE,
ggtheme = custom_theme(),
censor = FALSE,
legend = c(0.5, 0.2),
legend.title = "Mon3, P=0.02",
legend.labs = c("Quartile 1", "Quartile 2", "Quartile 3", "Quartile 4"),
xlab = "Follow-up (months)",
font.x = c(size = 15),
ylab = "Survival from MACE",
font.y = c(size = 15),
break.y.by = 0.2,
axes.offset = FALSE,
palette = c("blue", "dark red", "green", "orange"))页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/71626460
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