How to truncate Kaplan Meier curves when number at risk is < 10

Question

For a publication in a peer-reviewed scientific journal (http://www.redjournal.org), we would like to prepare Kaplan-Meier plots. The journal has the following specific guidelines for these plots:

"If your figures include curves generated from analyses using the Kaplan-Meier method or the cumulative incidence method, the following are now requirements for the presentation of these curves:

1. That the number of patients at risk is indicated;
2. That censoring marks are included;
3. That curves be truncated when there are fewer than 10 patients at risk; and
4. An estimate of the confidence interval should be included either in the figure itself or the text.”

Here, I illustrate my problem with the veteran dataset (https://github.com/tidyverse/reprex is great!). We can adress 1, 2 and 4 easily with the survminer package:

library(survival)
library(survminer)
#> Warning: package 'survminer' was built under R version 3.4.3
#> Loading required package: ggplot2
#> Loading required package: ggpubr
#> Warning: package 'ggpubr' was built under R version 3.4.3
#> Loading required package: magrittr

fit.obj <- survfit(Surv(time, status) ~ celltype, data = veteran)

ggsurvplot(fit.obj,
           conf.int = T,
           risk.table ="absolute",
           tables.theme = theme_cleantable())

I have, however, a problem with requirement 3 (truncate curves when there are fewer than 10 patients at risk). I see that all the required information is available in the survfit object:

library(survival)
fit.obj <- survfit(Surv(time, status) ~ celltype, data = veteran)
summary(fit.obj)
#> Call: survfit(formula = Surv(time, status) ~ celltype, data = veteran)
#> 
#>                 celltype=squamous 
#>  time n.risk n.event survival std.err lower 95% CI upper 95% CI
#>     1     35       2    0.943  0.0392       0.8690        1.000
#>     8     33       1    0.914  0.0473       0.8261        1.000
#>    10     32       1    0.886  0.0538       0.7863        0.998
#>    11     31       1    0.857  0.0591       0.7487        0.981
#>    15     30       1    0.829  0.0637       0.7127        0.963
#>    25     29       1    0.800  0.0676       0.6779        0.944
#>    30     27       1    0.770  0.0713       0.6426        0.924
#>    33     26       1    0.741  0.0745       0.6083        0.902
#>    42     25       1    0.711  0.0772       0.5749        0.880
#>    44     24       1    0.681  0.0794       0.5423        0.856
#>    72     23       1    0.652  0.0813       0.5105        0.832
#>    82     22       1    0.622  0.0828       0.4793        0.808
#>   110     19       1    0.589  0.0847       0.4448        0.781
#>   111     18       1    0.557  0.0861       0.4112        0.754
#>   112     17       1    0.524  0.0870       0.3784        0.726
#>   118     16       1    0.491  0.0875       0.3464        0.697
#>   126     15       1    0.458  0.0876       0.3152        0.667
#>   144     14       1    0.426  0.0873       0.2849        0.636
#>   201     13       1    0.393  0.0865       0.2553        0.605
#>   228     12       1    0.360  0.0852       0.2265        0.573
#>   242     10       1    0.324  0.0840       0.1951        0.539
#>   283      9       1    0.288  0.0820       0.1650        0.503
#>   314      8       1    0.252  0.0793       0.1362        0.467
#>   357      7       1    0.216  0.0757       0.1088        0.429
#>   389      6       1    0.180  0.0711       0.0831        0.391
#>   411      5       1    0.144  0.0654       0.0592        0.351
#>   467      4       1    0.108  0.0581       0.0377        0.310
#>   587      3       1    0.072  0.0487       0.0192        0.271
#>   991      2       1    0.036  0.0352       0.0053        0.245
#>   999      1       1    0.000     NaN           NA           NA
#> 
#>                 celltype=smallcell 
#>  time n.risk n.event survival std.err lower 95% CI upper 95% CI
#>     2     48       1   0.9792  0.0206      0.93958        1.000
#>     4     47       1   0.9583  0.0288      0.90344        1.000
#>     7     46       2   0.9167  0.0399      0.84172        0.998
#>     8     44       1   0.8958  0.0441      0.81345        0.987
#>    10     43       1   0.8750  0.0477      0.78627        0.974
#>    13     42       2   0.8333  0.0538      0.73430        0.946
#>    16     40       1   0.8125  0.0563      0.70926        0.931
#>    18     39       2   0.7708  0.0607      0.66065        0.899
#>    20     37       2   0.7292  0.0641      0.61369        0.866
#>    21     35       2   0.6875  0.0669      0.56812        0.832
#>    22     33       1   0.6667  0.0680      0.54580        0.814
#>    24     32       1   0.6458  0.0690      0.52377        0.796
#>    25     31       2   0.6042  0.0706      0.48052        0.760
#>    27     29       1   0.5833  0.0712      0.45928        0.741
#>    29     28       1   0.5625  0.0716      0.43830        0.722
#>    30     27       1   0.5417  0.0719      0.41756        0.703
#>    31     26       1   0.5208  0.0721      0.39706        0.683
#>    51     25       2   0.4792  0.0721      0.35678        0.644
#>    52     23       1   0.4583  0.0719      0.33699        0.623
#>    54     22       2   0.4167  0.0712      0.29814        0.582
#>    56     20       1   0.3958  0.0706      0.27908        0.561
#>    59     19       1   0.3750  0.0699      0.26027        0.540
#>    61     18       1   0.3542  0.0690      0.24171        0.519
#>    63     17       1   0.3333  0.0680      0.22342        0.497
#>    80     16       1   0.3125  0.0669      0.20541        0.475
#>    87     15       1   0.2917  0.0656      0.18768        0.453
#>    95     14       1   0.2708  0.0641      0.17026        0.431
#>    99     12       2   0.2257  0.0609      0.13302        0.383
#>   117      9       1   0.2006  0.0591      0.11267        0.357
#>   122      8       1   0.1755  0.0567      0.09316        0.331
#>   139      6       1   0.1463  0.0543      0.07066        0.303
#>   151      5       1   0.1170  0.0507      0.05005        0.274
#>   153      4       1   0.0878  0.0457      0.03163        0.244
#>   287      3       1   0.0585  0.0387      0.01600        0.214
#>   384      2       1   0.0293  0.0283      0.00438        0.195
#>   392      1       1   0.0000     NaN           NA           NA
#> 
#>                 celltype=adeno 
#>  time n.risk n.event survival std.err lower 95% CI upper 95% CI
#>     3     27       1   0.9630  0.0363      0.89430        1.000
#>     7     26       1   0.9259  0.0504      0.83223        1.000
#>     8     25       2   0.8519  0.0684      0.72786        0.997
#>    12     23       1   0.8148  0.0748      0.68071        0.975
#>    18     22       1   0.7778  0.0800      0.63576        0.952
#>    19     21       1   0.7407  0.0843      0.59259        0.926
#>    24     20       1   0.7037  0.0879      0.55093        0.899
#>    31     19       1   0.6667  0.0907      0.51059        0.870
#>    35     18       1   0.6296  0.0929      0.47146        0.841
#>    36     17       1   0.5926  0.0946      0.43344        0.810
#>    45     16       1   0.5556  0.0956      0.39647        0.778
#>    48     15       1   0.5185  0.0962      0.36050        0.746
#>    51     14       1   0.4815  0.0962      0.32552        0.712
#>    52     13       1   0.4444  0.0956      0.29152        0.678
#>    73     12       1   0.4074  0.0946      0.25850        0.642
#>    80     11       1   0.3704  0.0929      0.22649        0.606
#>    84      9       1   0.3292  0.0913      0.19121        0.567
#>    90      8       1   0.2881  0.0887      0.15759        0.527
#>    92      7       1   0.2469  0.0850      0.12575        0.485
#>    95      6       1   0.2058  0.0802      0.09587        0.442
#>   117      5       1   0.1646  0.0740      0.06824        0.397
#>   132      4       1   0.1235  0.0659      0.04335        0.352
#>   140      3       1   0.0823  0.0553      0.02204        0.307
#>   162      2       1   0.0412  0.0401      0.00608        0.279
#>   186      1       1   0.0000     NaN           NA           NA
#> 
#>                 celltype=large 
#>  time n.risk n.event survival std.err lower 95% CI upper 95% CI
#>    12     27       1   0.9630  0.0363      0.89430        1.000
#>    15     26       1   0.9259  0.0504      0.83223        1.000
#>    19     25       1   0.8889  0.0605      0.77791        1.000
#>    43     24       1   0.8519  0.0684      0.72786        0.997
#>    49     23       1   0.8148  0.0748      0.68071        0.975
#>    52     22       1   0.7778  0.0800      0.63576        0.952
#>    53     21       1   0.7407  0.0843      0.59259        0.926
#>   100     20       1   0.7037  0.0879      0.55093        0.899
#>   103     19       1   0.6667  0.0907      0.51059        0.870
#>   105     18       1   0.6296  0.0929      0.47146        0.841
#>   111     17       1   0.5926  0.0946      0.43344        0.810
#>   133     16       1   0.5556  0.0956      0.39647        0.778
#>   143     15       1   0.5185  0.0962      0.36050        0.746
#>   156     14       1   0.4815  0.0962      0.32552        0.712
#>   162     13       1   0.4444  0.0956      0.29152        0.678
#>   164     12       1   0.4074  0.0946      0.25850        0.642
#>   177     11       1   0.3704  0.0929      0.22649        0.606
#>   200      9       1   0.3292  0.0913      0.19121        0.567
#>   216      8       1   0.2881  0.0887      0.15759        0.527
#>   231      7       1   0.2469  0.0850      0.12575        0.485
#>   250      6       1   0.2058  0.0802      0.09587        0.442
#>   260      5       1   0.1646  0.0740      0.06824        0.397
#>   278      4       1   0.1235  0.0659      0.04335        0.352
#>   340      3       1   0.0823  0.0553      0.02204        0.307
#>   378      2       1   0.0412  0.0401      0.00608        0.279
#>   553      1       1   0.0000     NaN           NA           NA

But I have no idea how I can manipulate this list. I would very much appreciate any advice on how to filter out all lines with n.risk < 10 from fit.obj.

Answer 1

I can't quite seem to get this all the way there. But I see that you can pass a data.frame rather than a fit object to the plotting function. You can do this and clip the values. For example

ss <- subset(surv_summary(fit.obj), n.risk>=10)
ggsurvplot(ss,
      conf.int = T)

But it seems in this mode it does not automatically print the table. There is a function to draw just the table with

ggrisktable(fit.obj, tables.theme = theme_cleantable())

So I guess you could just combine them. Maybe i'm missing an easier way to draw the table when using a data.frame in the same plot.

Answer 2

As a slight variation on the above answers, if you want to truncate each group individually when less than 10 patients are at risk in that group, I found this to work and not require plotting the figure and table separately:

library(survival)
library(survminer)

# truncate each line when fewer than 10 at risk
atrisk <- 10

# KM fit
fit.obj <- survfit(Surv(time, status) ~ celltype, data = veteran)

# subset each stratum separately
maxcutofftime = 0 # for plotting
strata <- rep(names(fit.obj$strata), fit.obj$strata)
for (i in names(fit.obj$strata)){
    cutofftime <- min(fit.obj$time[fit.obj$n.risk < atrisk & strata == i])
    maxcutofftime = max(maxcutofftime, cutofftime)
    cutoffs <- which(fit.obj$n.risk < atrisk & strata == i)
    fit.obj$lower[cutoffs] <- NA
    fit.obj$upper[cutoffs] <- NA
    fit.obj$surv[cutoffs] <- NA
}


# plot
ggsurvplot(fit.obj, data = veteran, risk.table = TRUE, conf.int = T, pval = F, 
           tables.theme = theme_cleantable(), xlim = c(0,maxcutofftime), break.x.by = 90)

edited to add: note that if we had used pval = T above, that would give the p-value for the truncated data, not the full data. It doesn't make much of a difference in this example as both are p<0.0001, but be careful :)