File:KMannotation.png: Difference between revisions

Revision as of 22:07, 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

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.

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.

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

Click on a date/time to view the file as it appeared at that time.

	Date/Time	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>