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(<pre> library(survival) sf <- survfit(Surv(time, status) ~ x, data = aml) plot(sf, col=c('red', 'black')) </pre>) |
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sf <- survfit(Surv(time, status) ~ x, data = aml) | sf <- survfit(Surv(time, status) ~ x, data = aml) | ||
plot(sf, col=c('red', 'black')) | plot(sf, col=c('red', 'black')) | ||
rbind(time=aml$time[aml$x == 'Maintained'], status=aml$status[aml$x == 'Maintained']) | |||
# [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] | |||
# time 9 13 13 18 23 28 31 34 45 48 161 | |||
# status 1 1 0 1 1 0 1 1 0 1 0 | |||
unique(aml$time[aml$x == 'Maintained']) | |||
# [1] 9 13 18 23 28 31 34 45 48 161 | |||
summary(sf) | |||
# Call: survfit(formula = Surv(time, status) ~ x, data = aml) | |||
# | |||
# x=Maintained | |||
# time n.risk n.event survival std.err lower 95% CI upper 95% CI | |||
# 9 11 1 0.909 0.0867 0.7541 1.000 | |||
# 13 10 1 0.818 0.1163 0.6192 1.000 | |||
# 18 8 1 0.716 0.1397 0.4884 1.000 | |||
# 23 7 1 0.614 0.1526 0.3769 0.999 | |||
# 31 5 1 0.491 0.1642 0.2549 0.946 | |||
# 34 4 1 0.368 0.1627 0.1549 0.875 | |||
# 48 2 1 0.184 0.1535 0.0359 0.944 | |||
# | |||
# x=Nonmaintained | |||
# time n.risk n.event survival std.err lower 95% CI upper 95% CI | |||
# 5 12 2 0.8333 0.1076 0.6470 1.000 | |||
# 8 10 2 0.6667 0.1361 0.4468 0.995 | |||
# 12 8 1 0.5833 0.1423 0.3616 0.941 | |||
# 23 6 1 0.4861 0.1481 0.2675 0.883 | |||
# 27 5 1 0.3889 0.1470 0.1854 0.816 | |||
# 30 4 1 0.2917 0.1387 0.1148 0.741 | |||
# 33 3 1 0.1944 0.1219 0.0569 0.664 | |||
# 43 2 1 0.0972 0.0919 0.0153 0.620 | |||
# 45 1 1 0.0000 NaN NA NA | |||
</pre> | |||
The plot is annotated using [https://github.com/ksnip/ksnip ksnip]. | |||
Case 2: no median survival time. Smallest survival time > .5. | |||
<pre> | |||
aml2 <- aml; aml2[24:46, 'time'] <- 161; aml2[24:46, 'status'] <- 0 | |||
plot(survfit(Surv(time, status) ~ 1, aml2)) | |||
summary(survfit(Surv(time, status) ~ 1, aml2)) | |||
# Call: survfit(formula = Surv(time, status) ~ 1, data = aml2) | |||
# | |||
# time n.risk n.event survival std.err lower 95% CI upper 95% CI | |||
# 5 46 2 0.957 0.0301 0.899 1.000 | |||
# 8 44 2 0.913 0.0415 0.835 0.998 | |||
# 9 42 1 0.891 0.0459 0.806 0.986 | |||
# 12 41 1 0.870 0.0497 0.777 0.973 | |||
# 13 40 1 0.848 0.0530 0.750 0.958 | |||
# 18 37 1 0.825 0.0563 0.722 0.943 | |||
# 23 36 2 0.779 0.0618 0.667 0.910 | |||
# 27 34 1 0.756 0.0641 0.640 0.893 | |||
# 30 32 1 0.733 0.0663 0.614 0.875 | |||
# 31 31 1 0.709 0.0682 0.587 0.856 | |||
# 33 30 1 0.685 0.0699 0.561 0.837 | |||
# 34 29 1 0.662 0.0714 0.536 0.817 | |||
# 43 28 1 0.638 0.0726 0.510 0.798 | |||
# 45 27 1 0.614 0.0737 0.486 0.777 | |||
# 48 25 1 0.590 0.0747 0.460 0.756 | |||
</pre> | |||
Case 3: Still no median survival time. One event at the largest survival time (vertical bar goes down a little bit). | |||
<pre> | |||
aml2[46, 'status'] <- 1 | |||
plot(survfit(Surv(time, status) ~ 1, aml2)) | |||
summary(survfit(Surv(time, status) ~ 1, aml2)) | |||
# Call: survfit(formula = Surv(time, status) ~ 1, data = aml2) | |||
# | |||
# time n.risk n.event survival std.err lower 95% CI upper 95% CI | |||
# 5 46 2 0.957 0.0301 0.899 1.000 | |||
# 8 44 2 0.913 0.0415 0.835 0.998 | |||
# 9 42 1 0.891 0.0459 0.806 0.986 | |||
# 12 41 1 0.870 0.0497 0.777 0.973 | |||
# 13 40 1 0.848 0.0530 0.750 0.958 | |||
# 18 37 1 0.825 0.0563 0.722 0.943 | |||
# 23 36 2 0.779 0.0618 0.667 0.910 | |||
# 27 34 1 0.756 0.0641 0.640 0.893 | |||
# 30 32 1 0.733 0.0663 0.614 0.875 | |||
# 31 31 1 0.709 0.0682 0.587 0.856 | |||
# 33 30 1 0.685 0.0699 0.561 0.837 | |||
# 34 29 1 0.662 0.0714 0.536 0.817 | |||
# 43 28 1 0.638 0.0726 0.510 0.798 | |||
# 45 27 1 0.614 0.0737 0.486 0.777 | |||
# 48 25 1 0.590 0.0747 0.460 0.756 | |||
# 161 24 1 0.565 0.0756 0.435 0.735 | |||
</pre> | |||
Case 4: With median survival time (last survival < .5). Lots of events at the largest survival time (vertical bar goes down a lot). | |||
<pre> | |||
aml2 <- aml; aml2[24:46, 'time'] <- 161; aml2[24:46, 'status'] <- 1 | |||
plot(survfit(Surv(time, status) ~ 1, aml2)) | |||
summary(survfit(Surv(time, status) ~ 1, aml2)) | |||
# Call: survfit(formula = Surv(time, status) ~ 1, data = aml2) | |||
# | |||
# time n.risk n.event survival std.err lower 95% CI upper 95% CI | |||
# 5 46 2 0.9565 0.0301 0.89937 1.000 | |||
# 8 44 2 0.9130 0.0415 0.83514 0.998 | |||
# 9 42 1 0.8913 0.0459 0.80575 0.986 | |||
# 12 41 1 0.8696 0.0497 0.77749 0.973 | |||
# 13 40 1 0.8478 0.0530 0.75013 0.958 | |||
# 18 37 1 0.8249 0.0563 0.72168 0.943 | |||
# 23 36 2 0.7791 0.0618 0.66695 0.910 | |||
# 27 34 1 0.7562 0.0641 0.64048 0.893 | |||
# 30 32 1 0.7325 0.0663 0.61350 0.875 | |||
# 31 31 1 0.7089 0.0682 0.58705 0.856 | |||
# 33 30 1 0.6853 0.0699 0.56107 0.837 | |||
# 34 29 1 0.6616 0.0714 0.53553 0.817 | |||
# 43 28 1 0.6380 0.0726 0.51040 0.798 | |||
# 45 27 1 0.6144 0.0737 0.48566 0.777 | |||
# 48 25 1 0.5898 0.0747 0.46010 0.756 | |||
# 161 24 23 0.0246 0.0243 0.00355 0.170 | |||
</pre> | </pre> | ||
Latest revision as of 22:12, 13 October 2020
Summary
library(survival)
sf <- survfit(Surv(time, status) ~ x, data = aml)
plot(sf, col=c('red', 'black'))
rbind(time=aml$time[aml$x == 'Maintained'], status=aml$status[aml$x == 'Maintained'])
# [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11]
# time 9 13 13 18 23 28 31 34 45 48 161
# status 1 1 0 1 1 0 1 1 0 1 0
unique(aml$time[aml$x == 'Maintained'])
# [1] 9 13 18 23 28 31 34 45 48 161
summary(sf)
# Call: survfit(formula = Surv(time, status) ~ x, data = aml)
#
# x=Maintained
# time n.risk n.event survival std.err lower 95% CI upper 95% CI
# 9 11 1 0.909 0.0867 0.7541 1.000
# 13 10 1 0.818 0.1163 0.6192 1.000
# 18 8 1 0.716 0.1397 0.4884 1.000
# 23 7 1 0.614 0.1526 0.3769 0.999
# 31 5 1 0.491 0.1642 0.2549 0.946
# 34 4 1 0.368 0.1627 0.1549 0.875
# 48 2 1 0.184 0.1535 0.0359 0.944
#
# x=Nonmaintained
# time n.risk n.event survival std.err lower 95% CI upper 95% CI
# 5 12 2 0.8333 0.1076 0.6470 1.000
# 8 10 2 0.6667 0.1361 0.4468 0.995
# 12 8 1 0.5833 0.1423 0.3616 0.941
# 23 6 1 0.4861 0.1481 0.2675 0.883
# 27 5 1 0.3889 0.1470 0.1854 0.816
# 30 4 1 0.2917 0.1387 0.1148 0.741
# 33 3 1 0.1944 0.1219 0.0569 0.664
# 43 2 1 0.0972 0.0919 0.0153 0.620
# 45 1 1 0.0000 NaN NA NA
The plot is annotated using ksnip.
Case 2: no median survival time. Smallest survival time > .5.
aml2 <- aml; aml2[24:46, 'time'] <- 161; aml2[24:46, 'status'] <- 0 plot(survfit(Surv(time, status) ~ 1, aml2)) summary(survfit(Surv(time, status) ~ 1, aml2)) # Call: survfit(formula = Surv(time, status) ~ 1, data = aml2) # # time n.risk n.event survival std.err lower 95% CI upper 95% CI # 5 46 2 0.957 0.0301 0.899 1.000 # 8 44 2 0.913 0.0415 0.835 0.998 # 9 42 1 0.891 0.0459 0.806 0.986 # 12 41 1 0.870 0.0497 0.777 0.973 # 13 40 1 0.848 0.0530 0.750 0.958 # 18 37 1 0.825 0.0563 0.722 0.943 # 23 36 2 0.779 0.0618 0.667 0.910 # 27 34 1 0.756 0.0641 0.640 0.893 # 30 32 1 0.733 0.0663 0.614 0.875 # 31 31 1 0.709 0.0682 0.587 0.856 # 33 30 1 0.685 0.0699 0.561 0.837 # 34 29 1 0.662 0.0714 0.536 0.817 # 43 28 1 0.638 0.0726 0.510 0.798 # 45 27 1 0.614 0.0737 0.486 0.777 # 48 25 1 0.590 0.0747 0.460 0.756
Case 3: Still no median survival time. One event at the largest survival time (vertical bar goes down a little bit).
aml2[46, 'status'] <- 1 plot(survfit(Surv(time, status) ~ 1, aml2)) summary(survfit(Surv(time, status) ~ 1, aml2)) # Call: survfit(formula = Surv(time, status) ~ 1, data = aml2) # # time n.risk n.event survival std.err lower 95% CI upper 95% CI # 5 46 2 0.957 0.0301 0.899 1.000 # 8 44 2 0.913 0.0415 0.835 0.998 # 9 42 1 0.891 0.0459 0.806 0.986 # 12 41 1 0.870 0.0497 0.777 0.973 # 13 40 1 0.848 0.0530 0.750 0.958 # 18 37 1 0.825 0.0563 0.722 0.943 # 23 36 2 0.779 0.0618 0.667 0.910 # 27 34 1 0.756 0.0641 0.640 0.893 # 30 32 1 0.733 0.0663 0.614 0.875 # 31 31 1 0.709 0.0682 0.587 0.856 # 33 30 1 0.685 0.0699 0.561 0.837 # 34 29 1 0.662 0.0714 0.536 0.817 # 43 28 1 0.638 0.0726 0.510 0.798 # 45 27 1 0.614 0.0737 0.486 0.777 # 48 25 1 0.590 0.0747 0.460 0.756 # 161 24 1 0.565 0.0756 0.435 0.735
Case 4: With median survival time (last survival < .5). Lots of events at the largest survival time (vertical bar goes down a lot).
aml2 <- aml; aml2[24:46, 'time'] <- 161; aml2[24:46, 'status'] <- 1 plot(survfit(Surv(time, status) ~ 1, aml2)) summary(survfit(Surv(time, status) ~ 1, aml2)) # Call: survfit(formula = Surv(time, status) ~ 1, data = aml2) # # time n.risk n.event survival std.err lower 95% CI upper 95% CI # 5 46 2 0.9565 0.0301 0.89937 1.000 # 8 44 2 0.9130 0.0415 0.83514 0.998 # 9 42 1 0.8913 0.0459 0.80575 0.986 # 12 41 1 0.8696 0.0497 0.77749 0.973 # 13 40 1 0.8478 0.0530 0.75013 0.958 # 18 37 1 0.8249 0.0563 0.72168 0.943 # 23 36 2 0.7791 0.0618 0.66695 0.910 # 27 34 1 0.7562 0.0641 0.64048 0.893 # 30 32 1 0.7325 0.0663 0.61350 0.875 # 31 31 1 0.7089 0.0682 0.58705 0.856 # 33 30 1 0.6853 0.0699 0.56107 0.837 # 34 29 1 0.6616 0.0714 0.53553 0.817 # 43 28 1 0.6380 0.0726 0.51040 0.798 # 45 27 1 0.6144 0.0737 0.48566 0.777 # 48 25 1 0.5898 0.0747 0.46010 0.756 # 161 24 23 0.0246 0.0243 0.00355 0.170
File history
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| Date/Time | Thumbnail | Dimensions | User | Comment | |
|---|---|---|---|---|---|
| current | 16:59, 3 May 2020 |  | 732 × 546 (58 KB) | Brb (talk | contribs) | Reverted to version as of 16:57, 3 May 2020 (EDT) |
| 16:58, 3 May 2020 |  | 732 × 546 (47 KB) | Brb (talk | contribs) | Reverted to version as of 21:54, 2 May 2020 (EDT) | |
| 16:57, 3 May 2020 |  | 732 × 546 (58 KB) | Brb (talk | contribs) | ||
| 21:54, 2 May 2020 |  | 732 × 546 (47 KB) | Brb (talk | contribs) | <pre> library(survival) sf <- survfit(Surv(time, status) ~ x, data = aml) plot(sf, col=c('red', 'black')) </pre> |
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