Cox regression stratified by another column than the predictor

Question

I would like to perform a cox regression analysis with value_max_min as predictor, but according 2 groups peakand drop in the column peak_drop_status(to get 2 survival curves).

cox <- coxph(Surv(mace_months_date_vs_date_sample, mace) ~ value_max_min, data = df)

and to get the plot

fit <- survfit(Surv(mace_months_date_vs_date_sample, mace) ~ value_max_min, 
               data = df)

I thought about using the group_by or the slice functions?

Library used:

library(dplyr)
library(survival)
library(survminer)

Here is the console output:

structure(list(ID = c(136L, 136L, 200L, 200L, 146L, 146L, 153L, 
153L, 137L, 137L, 214L, 214L, 85L, 85L, 96L, 96L, 172L, 172L, 
182L, 182L, 87L, 87L, 93L, 93L, 210L, 210L, 69L, 69L, 132L, 132L, 
68L, 68L, 74L, 74L, 9L, 9L, 159L, 159L, 7L, 7L, 154L, 154L, 94L, 
94L, 113L, 113L, 124L, 124L, 131L, 131L, 143L, 143L, 151L, 151L, 
213L, 213L, 225L, 225L, 160L, 160L, 226L, 226L, 133L, 133L, 105L, 
105L, 119L, 119L, 156L, 156L, 208L, 208L, 117L, 117L, 227L, 227L, 
97L, 97L, 221L, 221L, 187L, 187L, 155L, 155L, 189L, 189L, 219L, 
219L, 178L, 178L, 181L, 181L, 184L, 184L, 165L, 165L, 233L, 233L, 
180L, 180L, 192L, 192L, 79L, 79L, 183L, 183L, 139L, 139L, 199L, 
199L, 81L, 81L, 162L, 162L, 104L, 104L, 237L, 237L, 128L, 128L, 
73L, 73L, 229L, 229L, 138L, 138L, 218L, 218L, 95L, 95L, 110L, 
110L, 190L, 190L, 72L, 72L, 127L, 127L, 164L, 164L, 111L, 111L, 
194L, 194L, 216L, 216L, 188L, 188L, 71L, 71L, 67L, 67L, 88L, 
88L, 123L, 123L, 173L, 173L, 223L, 223L, 152L, 152L, 238L, 238L, 
63L, 63L, 10L, 10L, 75L, 75L, 109L, 109L, 197L, 197L, 193L, 193L, 
89L, 89L, 106L, 106L, 205L, 205L, 125L, 125L, 121L, 121L, 100L, 
100L, 234L, 234L, 6L, 6L, 211L, 211L, 228L, 228L, 175L, 175L, 
84L, 84L, 191L, 191L, 243L, 243L, 115L, 115L, 220L, 220L, 242L, 
242L, 2L, 2L, 222L, 222L, 203L, 203L, 201L, 201L, 224L, 224L, 
4L, 4L, 102L, 102L, 76L, 76L, 239L, 239L, 231L, 231L, 195L, 195L, 
134L, 134L, 171L, 171L, 83L, 83L, 217L, 217L, 5L, 5L, 141L, 141L, 
3L, 3L, 112L, 112L, 235L, 235L, 185L, 185L, 103L, 103L, 120L, 
120L, 207L, 207L, 166L, 166L, 174L, 174L, 116L, 116L, 8L, 8L, 
140L, 140L, 14L, 14L, 27L, 27L, 30L, 30L, 23L, 23L, 54L, 54L, 
13L, 13L, 16L, 16L, 39L, 39L, 44L, 44L, 42L, 42L, 51L, 51L, 245L, 
245L, 59L, 59L, 28L, 28L, 45L, 45L, 34L, 34L, 49L, 49L, 43L, 
43L, 24L, 24L, 19L, 19L, 12L, 12L, 41L, 41L, 47L, 47L, 32L, 32L, 
50L, 50L, 31L, 31L, 18L, 18L, 15L, 15L, 60L, 60L, 52L, 52L, 21L, 
21L, 29L, 29L, 38L, 38L, 55L, 55L, 33L, 33L, 56L, 56L, 244L, 
244L, 36L, 36L, 20L, 20L, 17L, 17L, 57L, 57L, 35L, 35L, 22L, 
22L, 26L, 26L, 25L, 25L, 37L, 37L, 58L, 58L, 61L, 61L), age = c(49L, 
49L, 77L, 77L, 75L, 75L, 75L, 75L, 63L, 63L, 60L, 60L, 72L, 72L, 
51L, 51L, 50L, 50L, 35L, 35L, 48L, 48L, 44L, 44L, 79L, 79L, 67L, 
67L, 57L, 57L, 58L, 58L, 46L, 46L, 57L, 57L, 59L, 59L, 71L, 71L, 
65L, 65L, 56L, 56L, 28L, 28L, 65L, 65L, 41L, 41L, 76L, 76L, 63L, 
63L, 66L, 66L, 69L, 69L, 37L, 37L, 52L, 52L, 47L, 47L, 63L, 63L, 
41L, 41L, 79L, 79L, 42L, 42L, 69L, 69L, 76L, 76L, 68L, 68L, 66L, 
66L, 59L, 59L, 64L, 64L, 72L, 72L, 65L, 65L, 75L, 75L, 56L, 56L, 
80L, 80L, 68L, 68L, 58L, 58L, 61L, 61L, 59L, 59L, 68L, 68L, 60L, 
60L, 39L, 39L, 63L, 63L, 52L, 52L, 82L, 82L, 63L, 63L, 49L, 49L, 
59L, 59L, 61L, 61L, 64L, 64L, 63L, 63L, 66L, 66L, 68L, 68L, 54L, 
54L, 73L, 73L, 54L, 54L, 46L, 46L, 75L, 75L, 72L, 72L, 64L, 64L, 
69L, 69L, 68L, 68L, 59L, 59L, 52L, 52L, 65L, 65L, 51L, 51L, 48L, 
48L, 63L, 63L, 52L, 52L, 56L, 56L, 67L, 67L, 68L, 68L, 47L, 47L, 
75L, 75L, 76L, 76L, 63L, 63L, 73L, 73L, 48L, 48L, 68L, 68L, 48L, 
48L, NA, NA, 74L, 74L, 37L, 37L, 60L, 60L, 54L, 54L, 55L, 55L, 
61L, 61L, 79L, 79L, 67L, 67L, 57L, 57L, 51L, 51L, 67L, 67L, 68L, 
68L, 51L, 51L, 53L, 53L, 51L, 51L, 62L, 62L, 60L, 60L, 71L, 71L, 
58L, 58L, 52L, 52L, 72L, 72L, 73L, 73L, 76L, 76L, 81L, 81L, 48L, 
48L, 65L, 65L, 57L, 57L, 51L, 51L, 56L, 56L, 66L, 66L, 46L, 46L, 
78L, 78L, 76L, 76L, 81L, 81L, 71L, 71L, 46L, 46L, 51L, 51L, 73L, 
73L, 66L, 66L, 59L, 59L, 77L, 77L, 74L, 74L, 28L, 28L, 73L, 73L, 
54L, 54L, 59L, 59L, 53L, 53L, 57L, 57L, 54L, 54L, 52L, 52L, 38L, 
38L, 73L, 73L, 62L, 62L, 61L, 61L, 76L, 76L, 51L, 51L, 51L, 51L, 
54L, 54L, 59L, 59L, 47L, 47L, 66L, 66L, 57L, 57L, 57L, 57L, 62L, 
62L, 66L, 66L, 54L, 54L, 47L, 47L, 56L, 56L, 65L, 65L, 72L, 72L, 
49L, 49L, 46L, 46L, 73L, 73L, 55L, 55L, 47L, 47L, 59L, 59L, 43L, 
43L, 62L, 62L, 64L, 64L, 56L, 56L, 44L, 44L, 58L, 58L, 47L, 47L, 
46L, 46L, 66L, 66L, 54L, 54L, 81L, 81L, 36L, 36L, 48L, 48L), 
    sex = c(1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 
    0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 
    0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 
    0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 
    1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 
    0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 
    0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 
    0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 
    1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 
    1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 
    1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L), mace = c(0L, 0L, 0L, 0L, 1L, 1L, 
    1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 
    0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 
    0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 
    0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 
    0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 
    1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 
    0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    1L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 
    1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 
    0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 
    1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 
    1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 
    1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), mace_months_date_vs_date_sample = c(61L, 
    61L, 62L, 62L, 21L, 21L, 1L, 1L, 47L, 47L, 3L, 3L, 61L, 61L, 
    31L, 31L, 2L, 2L, 44L, 44L, 46L, 46L, 43L, 43L, 61L, 61L, 
    3L, 3L, 4L, 4L, 62L, 62L, 60L, 60L, 49L, 49L, 48L, 48L, 43L, 
    43L, 46L, 46L, 45L, 45L, 62L, 62L, 4L, 4L, 61L, 61L, 47L, 
    47L, 62L, 62L, 5L, 5L, 48L, 48L, 59L, 59L, 32L, 32L, 59L, 
    59L, 4L, 4L, 63L, 63L, 8L, 8L, 4L, 4L, 49L, 49L, 1L, 1L, 
    7L, 7L, 45L, 45L, 45L, 45L, 1L, 1L, 1L, 1L, 62L, 62L, 62L, 
    62L, 45L, 45L, 1L, 1L, 61L, 61L, 46L, 46L, 55L, 55L, 44L, 
    44L, 45L, 45L, 1L, 1L, 58L, 58L, 27L, 27L, 47L, 47L, 48L, 
    48L, 25L, 25L, 55L, 55L, 50L, 50L, 44L, 44L, 63L, 63L, 50L, 
    50L, 10L, 10L, 23L, 23L, 47L, 47L, 19L, 19L, 48L, 48L, 62L, 
    62L, 62L, 62L, 1L, 1L, 15L, 15L, 47L, 47L, 61L, 61L, 45L, 
    45L, 46L, 46L, 47L, 47L, 49L, 49L, 1L, 1L, 46L, 46L, 48L, 
    48L, 45L, 45L, 15L, 15L, 55L, 55L, 1L, 1L, 62L, 62L, 55L, 
    55L, 46L, 46L, 2L, 2L, 46L, 46L, 46L, 46L, 63L, 63L, 43L, 
    43L, 16L, 16L, 55L, 55L, 1L, 1L, 55L, 55L, 8L, 8L, 46L, 46L, 
    46L, 46L, 19L, 19L, 48L, 48L, 50L, 50L, 48L, 48L, 41L, 41L, 
    50L, 50L, 4L, 4L, 62L, 62L, 62L, 62L, 17L, 17L, 25L, 25L, 
    48L, 48L, 48L, 48L, 3L, 3L, 1L, 1L, 53L, 53L, 46L, 46L, 46L, 
    46L, 51L, 51L, 59L, 59L, 55L, 55L, 59L, 59L, 55L, 55L, 1L, 
    1L, 46L, 46L, 43L, 43L, 1L, 1L, 5L, 5L, 46L, 46L, 10L, 10L, 
    11L, 11L, 16L, 16L, 55L, 55L, 3L, 3L, 6L, 6L, 71L, 71L, 68L, 
    68L, 72L, 72L, 71L, 71L, 69L, 69L, 73L, 73L, 74L, 74L, 30L, 
    30L, 69L, 69L, 1L, 1L, 11L, 11L, 79L, 79L, 71L, 71L, 73L, 
    73L, 13L, 13L, 28L, 28L, 74L, 74L, 77L, 77L, 78L, 78L, 71L, 
    71L, 73L, 73L, 69L, 69L, 73L, 73L, 70L, 70L, 72L, 72L, 69L, 
    69L, 43L, 43L, 76L, 76L, 74L, 74L, 75L, 75L, 77L, 77L, 78L, 
    78L, 70L, 70L, 69L, 69L, 70L, 70L, 60L, 60L, 5L, 5L, 5L, 
    5L, 77L, 77L, 74L, 74L, 77L, 77L, 77L, 77L, 77L, 77L, 73L, 
    73L, 74L, 74L, 76L, 76L, 76L, 76L), trop = c(262L, 262L, 
    NA, NA, 1454L, 1454L, 663L, 663L, 2107L, 2107L, 86115L, 86115L, 
    24L, 24L, 3004L, 3004L, 9352L, 9352L, NA, NA, 1247L, 1247L, 
    NA, NA, 2888L, 2888L, NA, NA, 8421L, 8421L, NA, NA, NA, NA, 
    251L, 251L, 1211L, 1211L, NA, NA, 54592L, 54592L, 1241L, 
    1241L, 8669L, 8669L, 5204L, 5204L, 751L, 751L, 2840L, 2840L, 
    250L, 250L, NA, NA, NA, NA, 1411L, 1411L, 3789L, 3789L, 1675L, 
    1675L, 1557L, 1557L, 440L, 440L, NA, NA, 6979L, 6979L, 6155L, 
    6155L, 5110L, 5110L, 87355L, 87355L, 90L, 90L, 2234L, 2234L, 
    10000L, 10000L, NA, NA, 843L, 843L, 950L, 950L, 372L, 372L, 
    NA, NA, NA, NA, NA, NA, 6212L, 6212L, 871L, 871L, 776L, 776L, 
    24160L, 24160L, NA, NA, 9951L, 9951L, 3598L, 3598L, 2040L, 
    2040L, NA, NA, 6581L, 6581L, 349L, 349L, 11L, 11L, 6694L, 
    6694L, 63L, 63L, 15543L, 15543L, NA, NA, 33017L, 33017L, 
    2483L, 2483L, NA, NA, 961L, 961L, 1470L, 1470L, 2380L, 2380L, 
    11135L, 11135L, 1730L, 1730L, NA, NA, 11450L, 11450L, 769L, 
    769L, 16720L, 16720L, 57L, 57L, NA, NA, 4281L, 4281L, NA, 
    NA, 1258L, 1258L, NA, NA, 4299L, 4299L, 13374L, 13374L, NA, 
    NA, 2844L, 2844L, 1753L, 1753L, NA, NA, 5256L, 5256L, 3624L, 
    3624L, NA, NA, 21876L, 21876L, 8903L, 8903L, 844L, 844L, 
    5654L, 5654L, 3569L, 3569L, 45649L, 45649L, NA, NA, NA, NA, 
    4927L, 4927L, NA, NA, 2177L, 2177L, 5247L, 5247L, 50000L, 
    50000L, 438L, 438L, 1480L, 1480L, 50L, 50L, NA, NA, NA, NA, 
    27L, 27L, 2956L, 2956L, NA, NA, 3000L, 3000L, NA, NA, 6630L, 
    6630L, 1911L, 1911L, NA, NA, 2797L, 2797L, 6672L, 6672L, 
    1627L, 1627L, 123L, 123L, 7671L, 7671L, NA, NA, NA, NA, 2340L, 
    2340L, 10014L, 10014L, 7860L, 7860L, 67927L, 67927L, NA, 
    NA, NA, NA, 2413L, 2413L, 1035L, 1035L, 40273L, 40273L, 7120L, 
    7120L, 6440L, 6440L, 3340L, 3340L, 8450L, 8450L, 1500L, 1500L, 
    1970L, 1970L, 180L, 180L, 990L, 990L, 2600L, 2600L, 1800L, 
    1800L, 5280L, 5280L, 83L, 83L, 160L, 160L, 40L, 40L, 3710L, 
    3710L, 400L, 400L, NA, NA, 2100L, 2100L, 2390L, 2390L, 9320L, 
    9320L, 6020L, 6020L, 320L, 320L, 1420L, 1420L, 1710L, 1710L, 
    15300L, 15300L, 6490L, 6490L, 6390L, 6390L, 6300L, 6300L, 
    470L, 470L, 1740L, 1740L, 3600L, 3600L, NA, NA, 3930L, 3930L, 
    NA, NA, 2260L, 2260L, 1360L, 1360L, 846L, 846L, 15940L, 15940L, 
    NA, NA, 840L, 840L, 5010L, 5010L, NA, NA, 5330L, 5330L, 500L, 
    500L, 1080L, 1080L, NA, NA, NA, NA, 4470L, 4470L), egfr = c(90L, 
    90L, 48L, 48L, 65L, 65L, 35L, 35L, 84L, 84L, 90L, 90L, 64L, 
    64L, 61L, 61L, 86L, 86L, 56L, 56L, 90L, 90L, 62L, 62L, 75L, 
    75L, 56L, 56L, 86L, 86L, 90L, 90L, 89L, 89L, 84L, 84L, 86L, 
    86L, 65L, 65L, 86L, 86L, 61L, 61L, 90L, 90L, 73L, 73L, 61L, 
    61L, 77L, 77L, 60L, 60L, 58L, 58L, 80L, 80L, 58L, 58L, 90L, 
    90L, 64L, 64L, 68L, 68L, 90L, 90L, 61L, 61L, 80L, 80L, 90L, 
    90L, 36L, 36L, 90L, 90L, 90L, 90L, 59L, 59L, 90L, 90L, 77L, 
    77L, 64L, 64L, 52L, 52L, 90L, 90L, 33L, 33L, 90L, 90L, 90L, 
    90L, 90L, 90L, 90L, 90L, 90L, 90L, 69L, 69L, 90L, 90L, 46L, 
    46L, 90L, 90L, 75L, 75L, 54L, 54L, 90L, 90L, 54L, 54L, 90L, 
    90L, 82L, 82L, 49L, 49L, 35L, 35L, 90L, 90L, 66L, 66L, 90L, 
    90L, 86L, 86L, 90L, 90L, 45L, 45L, 72L, 72L, 68L, 68L, 51L, 
    51L, 90L, 90L, 90L, 90L, 90L, 90L, 58L, 58L, 84L, 84L, 42L, 
    42L, 90L, 90L, 86L, 86L, 90L, 90L, 90L, 90L, 90L, 90L, 87L, 
    87L, 67L, 67L, 51L, 51L, 81L, 81L, 74L, 74L, 63L, 63L, 90L, 
    90L, 56L, 56L, 87L, 87L, 84L, 84L, 78L, 78L, 63L, 63L, 63L, 
    63L, 63L, 63L, 67L, 67L, 64L, 64L, 68L, 68L, 78L, 78L, 68L, 
    68L, 90L, 90L, 69L, 69L, 90L, 90L, 90L, 90L, 75L, 75L, 85L, 
    85L, 85L, 85L, 52L, 52L, 69L, 69L, 90L, 90L, 76L, 76L, 90L, 
    90L, 54L, 54L, 86L, 86L, 90L, 90L, 61L, 61L, 72L, 72L, 76L, 
    76L, 69L, 69L, 85L, 85L, 86L, 86L, 42L, 42L, 72L, 72L, 71L, 
    71L, 58L, 58L, 68L, 68L, 86L, 86L, 75L, 75L, 84L, 84L, 63L, 
    63L, 63L, 63L, 78L, 78L, 90L, 90L, 48L, 48L, 55L, 55L, 81L, 
    81L, 87L, 87L, 99L, 99L, 77L, 77L, 56L, 56L, 69L, 69L, 66L, 
    66L, 67L, 67L, 85L, 85L, 90L, 90L, 65L, 65L, 68L, 68L, 76L, 
    76L, 84L, 84L, 90L, 90L, 59L, 59L, 88L, 88L, 79L, 79L, 85L, 
    85L, 90L, 90L, 90L, 90L, 77L, 77L, 90L, 90L, 49L, 49L, 62L, 
    62L, 71L, 71L, 87L, 87L, 51L, 51L, 90L, 90L, 90L, 90L, 79L, 
    79L, 90L, 90L, 90L, 90L, 52L, 52L, 75L, 75L, 71L, 71L, 68L, 
    68L, 83L, 83L, 88L, 88L, 51L, 51L, 87L, 87L, 99L, 99L, 78L, 
    78L, 90L, 90L), dm = c(0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 
    1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 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, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 
    1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 
    0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 
    1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 
    0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 
    1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 
    1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L), smoke = c(1L, 1L, 
    0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 
    1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 
    0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 
    1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 
    1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 
    0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 
    1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 
    1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 
    1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 
    1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 
    1L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 
    1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 
    1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 
    1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 
    0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 
    0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 
    1L, 1L), peak_drop_status = c("max_val", "min_val", "max_val", 
    "min_val", "max_val", "min_val", "max_val", "min_val", "max_val", 
    "min_val", "max_val", "min_val", "max_val", "min_val", "max_val", 
    "min_val", "max_val", "min_val", "max_val", "min_val", "max_val", 
    "min_val", "max_val", "min_val", "max_val", "min_val", "max_val", 
    "min_val", "max_val", "min_val", "max_val", "min_val", "max_val", 
    "min_val", "max_val", "min_val", "max_val", "min_val", "max_val", 
    "min_val", "max_val", "min_val", "max_val", "min_val", "max_val", 
    "min_val", "max_val", "min_val", "max_val", "min_val", "max_val", 
    "min_val", "max_val", "min_val", "max_val", "min_val", "max_val", 
    "min_val", "max_val", "min_val", "max_val", "min_val", "max_val", 
    "min_val", "max_val", "min_val", "max_val", "min_val", "max_val", 
    "min_val", "max_val", "min_val", "max_val", "min_val", "max_val", 
    "min_val", "max_val", "min_val", "max_val", "min_val", "max_val", 
    "min_val", "max_val", "min_val", "max_val", "min_val", "max_val", 
    "min_val", "max_val", "min_val", "max_val", "min_val", "max_val", 
    "min_val", "max_val", "min_val", "max_val", "min_val", "max_val", 
    "min_val", "max_val", "min_val", "max_val", "min_val", "max_val", 
    "min_val", "max_val", "min_val", "max_val", "min_val", "max_val", 
    "min_val", "max_val", "min_val", "max_val", "min_val", "max_val", 
    "min_val", "max_val", "min_val", "max_val", "min_val", "max_val", 
    "min_val", "max_val", "min_val", "max_val", "min_val", "max_val", 
    "min_val", "max_val", "min_val", "max_val", "min_val", "max_val", 
    "min_val", "max_val", "min_val", "max_val", "min_val", "max_val", 
    "min_val", "max_val", "min_val", "max_val", "min_val", "max_val", 
    "min_val", "max_val", "min_val", "max_val", "min_val", "max_val", 
    "min_val", "max_val", "min_val", "max_val", "min_val", "max_val", 
    "min_val", "max_val", "min_val", "max_val", "min_val", "max_val", 
    "min_val", "max_val", "min_val", "max_val", "min_val", "max_val", 
    "min_val", "max_val", "min_val", "max_val", "min_val", "max_val", 
    "min_val", "max_val", "min_val", "max_val", "min_val", "max_val", 
    "min_val", "max_val", "min_val", "max_val", "min_val", "max_val", 
    "min_val", "max_val", "min_val", "max_val", "min_val", "max_val", 
    "min_val", "max_val", "min_val", "max_val", "min_val", "max_val", 
    "min_val", "max_val", "min_val", "max_val", "min_val", "max_val", 
    "min_val", "max_val", "min_val", "max_val", "min_val", "max_val", 
    "min_val", "max_val", "min_val", "max_val", "min_val", "max_val", 
    "min_val", "max_val", "min_val", "max_val", "min_val", "max_val", 
    "min_val", "max_val", "min_val", "max_val", "min_val", "max_val", 
    "min_val", "max_val", "min_val", "max_val", "min_val", "max_val", 
    "min_val", "max_val", "min_val", "max_val", "min_val", "max_val", 
    "min_val", "max_val", "min_val", "max_val", "min_val", "max_val", 
    "min_val", "max_val", "min_val", "max_val", "min_val", "max_val", 
    "min_val", "max_val", "min_val", "max_val", "min_val", "max_val", 
    "min_val", "max_val", "min_val", "max_val", "min_val", "max_val", 
    "min_val", "max_val", "min_val", "max_val", "min_val", "max_val", 
    "min_val", "max_val", "min_val", "max_val", "min_val", "max_val", 
    "min_val", "max_val", "min_val", "max_val", "min_val", "max_val", 
    "min_val", "max_val", "min_val", "max_val", "min_val", "max_val", 
    "min_val", "max_val", "min_val", "max_val", "min_val", "max_val", 
    "min_val", "max_val", "min_val", "max_val", "min_val", "max_val", 
    "min_val", "max_val", "min_val", "max_val", "min_val", "max_val", 
    "min_val", "max_val", "min_val", "max_val", "min_val", "max_val", 
    "min_val", "max_val", "min_val", "max_val", "min_val", "max_val", 
    "min_val", "max_val", "min_val", "max_val", "min_val", "max_val", 
    "min_val", "max_val", "min_val", "max_val", "min_val", "max_val", 
    "min_val", "max_val", "min_val", "max_val", "min_val", "max_val", 
    "min_val", "max_val", "min_val", "max_val", "min_val", "max_val", 
    "min_val", "max_val", "min_val", "max_val", "min_val", "max_val", 
    "min_val", "max_val", "min_val", "max_val", "min_val", "max_val", 
    "min_val", "max_val", "min_val", "max_val", "min_val", "max_val", 
    "min_val"), value_max_min = c(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, 308.408676147461, -283.636077880859, 
    NA, NA, 208.791275024414, -5.3211898803711, 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, -14.9628820419311, -218.279922485352, 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, -15.319938659668, 
    -279.422790527344, 248.09851074219, -30.822647094727, 116.716430664065, 
    -8.8325366973877, NA, NA, 10.0856704711914, -29.1057052612305, 
    179.8525390625, -10.1883692741394, 130.585632324218, -39.6044845581057, 
    32.883270263672, -5.3593330383301, -17.5934886932374, -821.989379882808, 
    375.086456298828, -5.7297992706299, NA, NA, NA, NA, NA, NA, 
    419.108337402341, -1.28273773193359, 55.87646484375, -17.770830154419, 
    NA, NA, 44.05969238281, -6.7603330612182, 36.9793767929077, 
    -58.77816772461, 47.6982421875, -48.2563076019287, 180.041305541992, 
    -3.5863590240479, 19.503479003907, -66.49755859375, NA, NA, 
    33.036499023438, -3.0688781738281, 83.0613746643061, -562.289733886719, 
    -5.5973930358887, -162.939453124998, 18.5003929138184, -95.700927734375, 
    164.985534667969, 9.7361946105957, 27.7907447814941, -69.5900268554681, 
    159.863708496094, -22.477741241455, -21.2021789550781, -372.002563476562, 
    153.190795898438, 5.9852733612061, 15.9482421875, -32.590072631836, 
    243.90209960937, -24.0595645904541, 131.392028808594, -5.5808029174805, 
    192.978088378906, -56.510217666626, 104.543823242188, -173.209777832031, 
    NA, NA, 174.83570098877, -69.742797851558, 12.743041992187, 
    -502.216308593755, 28.5669360160827, -388.549621582031, -15.8679084777832, 
    -308.033813476562, -12.657926082611, -133.534645795822, -0.800338745117202, 
    -170.645233154297, -8.9742355346679, -44.6486473083496, 3.6423645019532, 
    -0.407896041870121, 163.853607177735, -13.0253372192383, 
    327.035522460937, -3.5568542480469, 336.011077880859, -9.2046012878418
    )), row.names = c(NA, -364L), class = c("tbl_df", "tbl", 
"data.frame"))

Thank you,

user_4747043 · Answer

To do a stratified Cox model, you would specify strata(var) in your model formula as follows:

library("survival")
library("survminer")
library("dplyr")

cox <- coxph(
    Surv(mace_months_date_vs_date_sample, mace) ~ value_max_min + strata(peak_drop_status),
    data = df
)

Your survfit isn't going to be very useful by the way, as you're supplying a numeric variable it's fitting a KM curve to every unique value of value_max_min. So you get a whole bunch of non-informative Kaplan-Meier curves:

fit <- survfit(
    Surv(mace_months_date_vs_date_sample, mace) ~ value_max_min, 
    data = df
)

plot(fit)

It might be better for visualisation purposes to split your data into groups based on quantiles of the predictor:

med <- median(df$value_max_min, na.rm=TRUE)
fit <- survfit(
    Surv(mace_months_date_vs_date_sample, mace) ~ (value_max_min > med),
    data = df
)
plot(fit)

Cox regression stratified by another column than the predictor

1 Answers