Survival Task

This task specializes mlr3::Task and mlr3::TaskSupervised for possibly-censored survival problems. The target is comprised of survival times and an event indicator. Predefined tasks are stored in mlr3::mlr_tasks.

The task_type is set to "surv".

References

Grambsch, Patricia, Therneau, Terry (1994). “Proportional hazards tests and diagnostics based on weighted residuals.” Biometrika, 81(3), 515–526. doi:10.1093/biomet/81.3.515 , https://doi.org/10.1093/biomet/81.3.515.

Super classes

mlr3::Task -> mlr3::TaskSupervised -> TaskSurv

Active bindings

censtype: character(1)
Returns the type of censoring, one of "right", "left", "counting", "interval", "interval2" or "mstate". Currently, only the "right"-censoring type is fully supported, the rest are experimental and the API will change in the future.

Methods

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

TaskSurv$new(
  id,
  backend,
  time = "time",
  event = "event",
  time2,
  type = c("right", "left", "interval", "counting", "interval2", "mstate"),
  label = NA_character_
)

Arguments

id: (character(1))
Identifier for the new instance.
backend: (DataBackend)
Either a DataBackend, or any object which is convertible to a DataBackend with as_data_backend(). E.g., a data.frame() will be converted to a DataBackendDataTable.
time: (character(1))
Name of the column for event time if data is right censored, otherwise starting time if interval censored.
event: (character(1))
Name of the column giving the event indicator. If data is right censored then "0"/FALSE means alive (no event), "1"/TRUE means dead (event). If type is "interval" then "0" means right censored, "1" means dead (event), "2" means left censored, and "3" means interval censored. If type is "interval2" then event is ignored.
time2: (character(1))
Name of the column for ending time of the interval for interval censored or counting process data, otherwise ignored.
type: (character(1))
Name of the column giving the type of censoring. Default is 'right' censoring.
label: (character(1))
Label for the new instance.

Details

Depending on the censoring type ("type"), the output of a survival task's "$target_names" is a character() vector with values the names of the columns given by the above initialization arguments. Specifically, the output is as follows (and in the specified order):

For type = "right", "left" or "mstate": ("time", "event")
For type = "interval" or "counting": ("time", "time2", "event")
For type = "interval2": ("time", "time2)

Method `truth()`

True response for specified row_ids. This is the survival outcome using the Surv format and depends on the censoring type. Defaults to all rows with role "use".

Usage

TaskSurv$truth(rows = NULL)

Arguments

rows: integer()
Row indices.

Returns

survival::Surv().

Method `formula()`

Creates a formula for survival models with survival::Surv() on the LHS (left hand side).

Usage

TaskSurv$formula(rhs = NULL, reverse = FALSE)

Arguments

rhs: If NULL, RHS (right hand side) is ".", otherwise RHS is "rhs".
reverse: If TRUE then formula calculated with 1 - status.

Returns

stats::formula().

Method `times()`

Returns the (unsorted) outcome times.

Usage

TaskSurv$times(rows = NULL)

Arguments

rows: integer()
Row indices.

Returns

numeric()

Method `status()`

Returns the event indicator (aka censoring/survival indicator). If censtype is "right" or "left" then 1 is event and 0 is censored. If censtype is "mstate" then 0 is censored and all other values are different events. If censtype is "interval" then 0 is right-censored, 1 is event, 2 is left-censored, 3 is interval-censored. See survival::Surv().

Usage

TaskSurv$status(rows = NULL)

Arguments

rows: integer()
Row indices.

Returns

integer()

Method `unique_times()`

Returns the sorted unique outcome times for "right", "left" and "mstate" types of censoring.

Usage

TaskSurv$unique_times(rows = NULL)

Arguments

rows: integer()
Row indices.

Returns

numeric()

Method `unique_event_times()`

Returns the sorted unique event (or failure) outcome times for "right", "left" and "mstate" types of censoring.

Usage

TaskSurv$unique_event_times(rows = NULL)

Arguments

rows: integer()
Row indices.

Returns

numeric()

Method `risk_set()`

Returns the row_ids of the observations at risk (not dead or censored or had other events in case of multi-state tasks) at the specified time.

Only designed for "right", "left" and "mstate" types of censoring.

Usage

TaskSurv$risk_set(time = NULL)

Arguments

time: (numeric(1))
Time to return risk set for, if NULL returns all row_ids.

Returns

integer()

Method `kaplan()`

Calls survival::survfit() to calculate the Kaplan-Meier estimator.

Usage

TaskSurv$kaplan(strata = NULL, rows = NULL, reverse = FALSE, ...)

Arguments

strata: (character())
Stratification variables to use.
rows: (integer())
Subset of row indices.
reverse: (logical())
If TRUE calculates Kaplan-Meier of censoring distribution (1-status). Default FALSE.
...: (any)
Additional arguments passed down to survival::survfit.formula().

Returns

survival::survfit.object.

Method `reverse()`

Returns the same task with the status variable reversed, i.e., 1 - status. Only designed for "left" and "right" censoring.

Usage

TaskSurv$reverse()

Returns

TaskSurv.

Method `cens_prop()`

Returns the proportion of censoring for this survival task. By default, this is returned for all observations, otherwise only the specified ones (rows).

Only designed for "right" and "left" censoring.

Usage

TaskSurv$cens_prop(rows = NULL)

Arguments

rows: integer()
Row indices.

Returns

numeric()

Method `admin_cens_prop()`

Returns an estimated proportion of administratively censored observations (i.e. censored at or after a user-specified time point). Our main assumption here is that in an administratively censored dataset, the maximum censoring time is likely close to the maximum event time and so we expect higher proportion of censored subjects near the study end date.

Only designed for "right" and "left" censoring.

Usage

TaskSurv$admin_cens_prop(rows = NULL, admin_time = NULL, quantile_prob = 0.99)

Arguments

rows: integer()
Row indices.
admin_time: (numeric(1))
Administrative censoring time (in case it is known a priori).
quantile_prob: (numeric(1))
Quantile probability value with which we calculate the cutoff time for administrative censoring. Ignored, if admin_time is given. By default, quantile_prob is equal to $0.99$, which translates to a time point very close to the maximum outcome time in the dataset. A lower value will result in an earlier time point and therefore in a more relaxed definition (i.e. higher proportion) of administrative censoring.

Returns

numeric()

Method `dep_cens_prop()`

Returns the proportion of covariates (task features) that are found to be significantly associated with censoring. This function fits a logistic regression model via glm with the censoring status as the response and using all features as predictors. If a covariate is significantly associated with the censoring status, it suggests that censoring may be informative (dependent) rather than random (non-informative). This methodology is more suitable for low-dimensional datasets where the number of features is relatively small compared to the number of observations.

Only designed for "right" and "left" censoring.

Usage

TaskSurv$dep_cens_prop(rows = NULL, method = "holm", sign_level = 0.05)

Arguments

rows: integer()
Row indices.
method: (character(1))
Method to adjust p-values for multiple comparisons, see p.adjust.methods. Default is "holm".
sign_level: (numeric(1))
Significance level for each coefficient's p-value from the logistic regression model. Default is $0.05$.

Returns

numeric()

Method `prop_haz()`

Checks if the data satisfy the proportional hazards (PH) assumption using the Grambsch-Therneau test, Grambsch (1994). Uses cox.zph. This method should be used only for low-dimensional datasets where the number of features is relatively small compared to the number of observations.

Only designed for "right" and "left" censoring.

Usage

TaskSurv$prop_haz()

Returns

numeric()
If no errors, the p-value of the global chi-square test. A p-value $< 0.05$ is an indication of possible PH violation.

Method `clone()`

The objects of this class are cloneable with this method.

Usage

TaskSurv$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Examples

library(mlr3)
task = tsk("lung")

# meta data
task$target_names # target is always (time, status) for right-censoring tasks
#> [1] "time"   "status"
task$feature_names
#> [1] "age"       "meal.cal"  "pat.karno" "ph.ecog"   "ph.karno"  "sex"      
#> [7] "wt.loss"  
task$formula()
#> Surv(time, status, type = "right") ~ .
#> <environment: namespace:survival>

# survival data
task$truth() # survival::Surv() object
#>   [1]  455   210  1022+  310   361   218   166   170   567   613   707    61 
#>  [13]  301    81   371   520   574   118   390    12   473    26   107    53 
#>  [25]  814   965+   93   731   460   153   433   583    95   303   519   643 
#>  [37]  765    53   246   689     5   687   345   444   223    60   163    65 
#>  [49]  821+  428   230   840+  305    11   226   426   705   363   176   791 
#>  [61]   95   196+  167   806+  284   641   147   740+  163   655    88   245 
#>  [73]   30   477   559+  450   156   529+  429   351    15   181   283    13 
#>  [85]  212   524   288   363   199   550    54   558   207    92    60   551+
#>  [97]  293   353   267   511+  457   337   201   404+  222    62   458+  353 
#> [109]  163    31   229   156   329   291   179   376+  384+  268   292+  142 
#> [121]  413+  266+  320   181   285   301+  348   197   382+  303+  296+  180 
#> [133]  145   269+  300+  284+  292+  332+  285   259+  110   286   270   225+
#> [145]  269   225+  243+  276+  135    79    59   240+  202+  235+  239   252+
#> [157]  221+  185+  222+  183   211+  175+  197+  203+  191+  105+  174+  177+
task$times() # (unsorted) times
#>   [1]  455  210 1022  310  361  218  166  170  567  613  707   61  301   81  371
#>  [16]  520  574  118  390   12  473   26  107   53  814  965   93  731  460  153
#>  [31]  433  583   95  303  519  643  765   53  246  689    5  687  345  444  223
#>  [46]   60  163   65  821  428  230  840  305   11  226  426  705  363  176  791
#>  [61]   95  196  167  806  284  641  147  740  163  655   88  245   30  477  559
#>  [76]  450  156  529  429  351   15  181  283   13  212  524  288  363  199  550
#>  [91]   54  558  207   92   60  551  293  353  267  511  457  337  201  404  222
#> [106]   62  458  353  163   31  229  156  329  291  179  376  384  268  292  142
#> [121]  413  266  320  181  285  301  348  197  382  303  296  180  145  269  300
#> [136]  284  292  332  285  259  110  286  270  225  269  225  243  276  135   79
#> [151]   59  240  202  235  239  252  221  185  222  183  211  175  197  203  191
#> [166]  105  174  177
task$status() # event indicators (1 = death, 0 = censored)
#>   [1] 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1
#>  [38] 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 0 1 1 1 1 1 1
#>  [75] 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 0 1 1 1 1
#> [112] 1 1 1 1 0 0 1 0 1 0 0 1 1 1 0 1 1 0 0 0 1 1 0 0 0 0 0 1 0 1 1 1 0 1 0 0 0
#> [149] 1 1 1 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0
task$unique_times() # sorted unique times
#>   [1]    5   11   12   13   15   26   30   31   53   54   59   60   61   62   65
#>  [16]   79   81   88   92   93   95  105  107  110  118  135  142  145  147  153
#>  [31]  156  163  166  167  170  174  175  176  177  179  180  181  183  185  191
#>  [46]  196  197  199  201  202  203  207  210  211  212  218  221  222  223  225
#>  [61]  226  229  230  235  239  240  243  245  246  252  259  266  267  268  269
#>  [76]  270  276  283  284  285  286  288  291  292  293  296  300  301  303  305
#>  [91]  310  320  329  332  337  345  348  351  353  361  363  371  376  382  384
#> [106]  390  404  413  426  428  429  433  444  450  455  457  458  460  473  477
#> [121]  511  519  520  524  529  550  551  558  559  567  574  583  613  641  643
#> [136]  655  687  689  705  707  731  740  765  791  806  814  821  840  965 1022
task$unique_event_times() # sorted unique event times
#>   [1]   5  11  12  13  15  26  30  31  53  54  59  60  61  62  65  79  81  88
#>  [19]  92  93  95 107 110 118 135 142 145 147 153 156 163 166 167 170 176 179
#>  [37] 180 181 183 197 199 201 207 210 212 218 222 223 226 229 230 239 245 246
#>  [55] 267 268 269 270 283 284 285 286 288 291 293 301 303 305 310 320 329 337
#>  [73] 345 348 351 353 361 363 371 390 426 428 429 433 444 450 455 457 460 473
#>  [91] 477 519 520 524 550 558 567 574 583 613 641 643 655 687 689 705 707 731
#> [109] 765 791 814
task$risk_set(time = 700) # observation ids that are not censored or dead at t = 700
#>  [1]  3 11 25 26 28 37 49 52 57 60 64 68
task$kaplan(strata = "sex") # stratified Kaplan-Meier
#> Call: survfit(formula = f, data = data)
#> 
#>         n events median 0.95LCL 0.95UCL
#> sex=f  64     38    426     345     641
#> sex=m 104     83    284     229     353
task$kaplan(reverse = TRUE) # Kaplan-Meier of the censoring distribution
#> Call: survfit(formula = f, data = data)
#> 
#>        n events median 0.95LCL 0.95UCL
#> [1,] 168     47    740     511      NA

# proportion of censored observations across all dataset
task$cens_prop()
#> [1] 0.2797619
# proportion of censored observations at or after the 95% time quantile
task$admin_cens_prop(quantile_prob = 0.95)
#> [1] 0.1276596
# proportion of variables that are significantly associated with the
# censoring status via a logistic regression model
task$dep_cens_prop() # 0 indicates independent censoring
#> [1] 0
# data barely satisfies proportional hazards assumption (p > 0.05)
task$prop_haz()
#> [1] 0.0608371
# veteran data is definitely non-PH (p << 0.05)
tsk("veteran")$prop_haz()
#> [1] 3.225193e-05

References

See also

Super classes

Active bindings

Methods

Public methods

Method new()

Usage

Arguments

Details

Method truth()

Usage

Arguments

Returns

Method formula()

Usage

Arguments

Returns

Method times()

Usage

Arguments

Returns

Method status()

Usage

Arguments

Returns

Method unique_times()

Usage

Arguments

Returns

Method unique_event_times()

Usage

Arguments

Returns

Method risk_set()

Usage

Arguments

Returns

Method kaplan()

Usage

Arguments

Returns

Method reverse()

Usage

Returns

Method cens_prop()

Usage

Arguments

Returns

Method admin_cens_prop()

Usage

Arguments

Returns

Method dep_cens_prop()

Usage

Arguments

Returns

Method prop_haz()

Usage

Returns

Method clone()

Usage

Arguments

Examples

Method `new()`

Method `truth()`

Method `formula()`

Method `times()`

Method `status()`

Method `unique_times()`

Method `unique_event_times()`

Method `risk_set()`

Method `kaplan()`

Method `reverse()`

Method `cens_prop()`

Method `admin_cens_prop()`

Method `dep_cens_prop()`

Method `prop_haz()`

Method `clone()`