Cmprsk improvements

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: mlr3proba
 Title: Probabilistic Supervised Learning for 'mlr3'
-Version: 0.8.1
+Version: 0.8.2
 Authors@R: c(
     person("Raphael", "Sonabend", , "raphaelsonabend@gmail.com", role = "aut",
            comment = c(ORCID = "0000-0001-9225-4654")),
@@ -22,9 +22,8 @@ Authors@R: c(
     person("Markus", "Goeswein", , "markus.goeswein@outlook.de", role = "ctb")
   )
 Description: Provides extensions for probabilistic supervised learning for
-    'mlr3'.  This includes extending the regression task to probabilistic
-    and interval regression, adding a survival task, and other specialized
-    models, predictions, and measures.
+    'mlr3'. This currently includes survival analysis, competing risks and
+    density estimation.
 License: LGPL-3
 URL: https://mlr3proba.mlr-org.com, https://github.com/mlr-org/mlr3proba
 BugReports: https://github.com/mlr-org/mlr3proba/issues
@@ -46,6 +45,7 @@ Suggests:
     abind,
     coxed,
     GGally,
+    glmnet,
     knitr,
     lgr,
     lifecycle,
@@ -60,14 +60,14 @@ Suggests:
     set6 (>= 0.2.6),
     simsurv,
     survAUC,
-    testthat (>= 3.0.0),
-    glmnet
+    testthat (>= 3.0.0)
 LinkingTo:
     Rcpp
 Remotes:
+    jkropko/coxed@bc92e25,
     xoopR/distr6,
     xoopR/param6,
-    xoopR/set6,
+    xoopR/set6
 ByteCompile: true
 Config/testthat/edition: 3
 Encoding: UTF-8

NEWS.md

@@ -1,5 +1,7 @@
-# mlr3proba 0.8.1.9000
+# mlr3proba 0.8.2
 
+* fix: `coxed` package was removed from CRAN so now we install the latest working CRAN version (`0.3.3`) from GitHub
+* feat: event-weighted mean AUC(t) as default score in `msr("cmprsk.auc")`
 * feat: The pipeops `PipeOpTaskSurvRegrPEM` and `PipeOpTaskSurvClassifDiscTime` now apply the data transformation on an internal validation task if it exists. This enables the use of e.g `xgboost` regression or classification learners with `early_stopping` on the corresponding pipelines.
 
 # mlr3proba 0.8.1

R/LearnerCompRisksAalenJohansen.R

@@ -7,12 +7,16 @@
 #'
 #' This learner estimates the Cumulative Incidence Function (CIF) for competing
 #' risks using the empirical Aalen-Johansen (AJ) estimator.
-#' The probability of transitioning to each competing event is computed via the
-#' [survfit][survival::survfit.formula()] function.
+#'
+#' Transition probabilities to each event are computed from the training data via
+#' the [survfit][survival::survfit.formula()] function and predictions are made
+#' at all unique times (both events and censoring) observed in the training set.
 #'
 #' @references
 #' `r format_bib("aalen_1978")`
 #'
+#' @templateVar msr_id all
+#' @template example_cmprsk
 #' @export
 LearnerCompRisksAalenJohansen = R6Class("LearnerCompRisksAalenJohansen",
   inherit = LearnerCompRisks,
@@ -54,18 +58,18 @@ LearnerCompRisksAalenJohansen = R6Class("LearnerCompRisksAalenJohansen",
 
       times = self$model$time # unique train set time points
       n_obs = task$nrow # number of test observations
-      CIF = stats::setNames(vector("list", ncol(trans_mat)), colnames(trans_mat))
+      cif_list = stats::setNames(vector("list", ncol(trans_mat)), colnames(trans_mat))
 
-      for (i in seq_along(CIF)) {
-        CIF[[i]] = matrix(
+      for (i in seq_along(cif_list)) {
+        cif_list[[i]] = matrix(
           data = rep(trans_mat[, i], times = n_obs),
           nrow = n_obs,
           byrow = TRUE,
           dimnames = list(NULL, times)
         )
       }
 
-      list(cif = CIF)
+      list(cif = cif_list)
     }
   )
 )

R/MeasureCompRisksAUC.R

@@ -5,37 +5,42 @@
 #' @aliases MeasureCompRisksAUC mlr_measures_cmprsk.auc
 #'
 #' @description
-#' Calculates the cause-specific ROC-AUC(t) at a **specific time point**,
-#' see Blanche et al. (2013).
-#' Can also return the mean AUC(t) over all competing causes.
+#' Calculates the cause-specific time-dependent ROC-AUC at a **specific time point**,
+#' as described in Blanche et al. (2013).
+#'
+#' By default, this measure returns a **cause-independent AUC(t)** score,
+#' calculated as a weighted average of the cause-specific AUCs.
+#' The weights correspond to the relative event frequencies of each cause,
+#' following Equation (7) in Heyard et al. (2020).
 #'
 #' @details
 #' Calls [riskRegression::Score()] with:
 #' - `metric = "auc"`
 #' - `cens.method = "ipcw"`
 #' - `cens.model = "km"`
 #'
-#' Note that the IPC weights (estimated via the Kaplan-Meier) are calculated
-#' using the test data.
+#' Notes on the `riskRegression` implementation:
+#' 1. IPCW weights are estimated using the **test data only**.
+#' 2. No extrapolation is supported: if `time_horizon` exceeds the maximum observed
+#' time on the test data, an error is thrown.
+#' 3. The choice of `time_horizon` is critical: if, at that time, no events of a
+#' given cause have occurred and all predicted CIFs are zero, `riskRegression`
+#' will return `NaN` for that cause-specific AUC (and subsequently for the
+#' summary AUC).
 #'
 #' @section Parameter details:
-#' - `cause` (`numeric(1)`)\cr
-#'  Integer number indicating which cause to use (Default: `1`).
-#'  If `"mean"`, then the mean AUC(t) over all causes is returned.
+#' - `cause` (`numeric(1)|"mean"`)\cr
+#'  Integer number indicating which cause to use.
+#'  Default value is `"mean"` which returns a weighted mean of the cause-specific AUCs.
 #' - `time_horizon` (`numeric(1)`)\cr
 #'  Single time point at which to return the score.
-#'  If `NULL`, we issue a warning and the median time from the test set is used.
+#'  If `NULL`, the **median time point** from the test set is used.
 #'
 #' @references
-#' `r format_bib("blanche_2013")`
-#'
-#' @examplesIf mlr3misc::require_namespaces(c("riskRegression"), quietly = TRUE)
-#' t = tsk("pbc")
-#' l = lrn("cmprsk.aalen")
-#' p = l$train(t)$predict(t)
-#'
-#' p$score(msr("cmprsk.auc", time_horizon = 42))
+#' `r format_bib("blanche_2013", "heyard_2020")`
 #'
+#' @templateVar msr_id auc
+#' @template example_cmprsk
 #' @export
 MeasureCompRisksAUC = R6Class(
   "MeasureCompRisksAUC",
@@ -45,18 +50,16 @@ MeasureCompRisksAUC = R6Class(
     #' Creates a new instance of this [R6][R6::R6Class] class.
     initialize = function() {
       param_set = ps(
-        cause = p_int(lower = 1, default = 1, special_vals = list("mean")),
+        cause = p_int(lower = 1, init = "mean", special_vals = list("mean")),
         time_horizon = p_dbl(lower = 0, default = NULL, special_vals = list(NULL))
       )
 
-      param_set$set_values(cause = 1)
-
       super$initialize(
         id = "cmprsk.auc",
         param_set = param_set,
         range = c(0, 1),
         minimize = FALSE,
-        #properties = "requires_task", (only if we want `cen.model = cox`)
+        properties = "na_score",
         packages = "riskRegression",
         label = "Blanche's Time-dependent IPCW ROC-AUC score",
         man = "mlr3proba::mlr_measures_cmprsk.auc"
@@ -68,33 +71,37 @@ MeasureCompRisksAUC = R6Class(
     .score = function(prediction, task, ...) {
       pv = self$param_set$values
 
-      # data with (time, event) columns for IPCW calculation
-      # uses test set data as it needs to match predicted CIF rows/observations
-      data = data.table(time = prediction$truth[, 1L], event = prediction$truth[, 2L])
-      lhs = "Hist(time, event)"
-      form = formulate(lhs, rhs = "1", env = getNamespace("prodlim"))
-
-      # single time point for AUC or median time
-      if (is.null(pv$time)) {
-        # TODO: add the warning again when this is not the default measure
-        # warning("No time horizon specified. We use median time from the test set")
-        time_horizon = median(data$time)
+      # Prepare test set data (for IPCW)
+      # uses test set observations as it needs to match exactly the number of
+      # rows (observations) in the predicted CIF matrix
+      data = data.table(
+        time = prediction$truth[, 1L],
+        event = prediction$truth[, 2L]
+      )
+      form = formulate(lhs = "Hist(time, event)", rhs = "1", env = getNamespace("prodlim"))
+
+      # Define evaluation time (single time point for AUC)
+      time_horizon = if (is.null(pv$time_horizon)) {
+        median(data$time)
       } else {
-        time_horizon = assert_number(pv$time_horizon, lower = 0, finite = TRUE, na.ok = FALSE)
+        assert_number(pv$time_horizon, lower = 0, finite = TRUE, na.ok = FALSE)
       }
 
       # list of predicted CIF matrices
-      cif = prediction$cif
+      cif_list = prediction$cif
+      causes = names(cif_list)
 
       cause = pv$cause
-      if (test_integerish(cause)) {
+      if (test_int(cause)) {
+        cause = as.character(cause)
+
         # check if cause exists
-        if (cause %nin% names(cif)) {
-          stopf("Given cause (%i) is not included in the CIF causes", cause)
+        if (cause %nin% causes) {
+          stopf("Invalid cause. Use one of: %s", paste(causes, collapse = ", "))
         }
 
         # get cause-specific CIF
-        cif_mat = cif[[as.character(cause)]]
+        cif_mat = cif_list[[cause]]
 
         # get CIF on the time horizon
         mat = .interp_cif(cif_mat, eval_times = time_horizon)
@@ -112,10 +119,10 @@ MeasureCompRisksAUC = R6Class(
         times = NULL # fix: no global binding
         res$AUC$score[times == time_horizon][["AUC"]]
       } else {
-        # iterate through cause-specific CIFs, get AUC(t), return the mean
-        AUCs = sapply(names(cif), function(cause) {
+        # iterate through cause-specific CIFs, get AUC(t)
+        aucs = vapply(causes, function(cause) {
           # get cause-specific CIF
-          cif_mat = cif[[cause]]
+          cif_mat = cif_list[[cause]]
 
           # get CIF on the time horizon
           mat = .interp_cif(cif_mat, eval_times = time_horizon)
@@ -132,10 +139,11 @@ MeasureCompRisksAUC = R6Class(
 
           times = NULL # fix: no global binding
           res$AUC$score[times == time_horizon][["AUC"]]
-        })
+        }, numeric(1L))
 
-        # return mean (weighted?)
-        mean(AUCs)
+        event = data[event != 0, event] # remove censored obs (if they exist)
+        w = prop.table(table(event)) # observed proportions per cause
+        sum(w[names(aucs)] * aucs) # weighted mean
       }
     }
   )

R/PipeOpPredClassifSurvIPCW.R

@@ -26,15 +26,15 @@
 #' to a [PredictionSurv].
 #' Each input classification probability prediction corresponds to the
 #' probability of having the event up to the specified cutoff time
-#' \eqn{\hat{\pi}(\bold{X}_i) = P(T_i < \tau|\bold{X}_i)},
+#' \eqn{\hat{\pi}(\textbf{X}_i) = P(T_i < \tau|\textbf{X}_i)},
 #' see Vock et al. (2016) and [PipeOpTaskSurvClassifIPCW].
 #' Therefore, these predictions serve as **continuous risk scores** that can be
 #' directly interpreted as `crank` predictions in the right-censored survival
 #' setting. We also map them to the survival distribution prediction `distr`,
 #' at the specified cutoff time point \eqn{\tau}, i.e. as
-#' \eqn{S_i(\tau) = 1 - \hat{\pi}(\bold{X}_i)}.
+#' \eqn{S_i(\tau) = 1 - \hat{\pi}(\textbf{X}_i)}.
 #' Survival measures that use the survival distribution (eg [ISBS][mlr_measures_surv.brier])
-#' should be evaluated exactly at the cutoff time point \eqn{\tau}, see example.
+#' should be evaluated exactly at the cutoff time point \eqn{\tau}.
 #'
 #' @references
 #' `r format_bib("vock_2016")`

R/TaskGeneratorCoxed.R

@@ -5,8 +5,8 @@
 #' @description
 #' A [TaskGenerator][mlr3::TaskGenerator] calling [coxed::sim.survdata()].
 #'
-#' This generator creates a survival dataset using \CRANpkg{coxed}, and exposes
-#' some parameters from the `sim.survdata()` function.
+#' This generator creates a survival dataset using `coxed`, and exposes
+#' some parameters from the [coxed::sim.survdata()] function.
 #' We don't include the parameters `X` (user-specified variables), `covariate`,
 #' `low`, `high`, `compare`, `beta` and `hazard.fun` for this generator.
 #' The latter means that no user-specified hazard function can be used and the
@@ -18,7 +18,7 @@
 #' @template seealso_task_generator
 #' @references
 #' `r format_bib("harden_2019")`
-#' @examplesIf mlr3misc::require_namespaces(c("coxed"), quietly = TRUE)
+#' @examplesIf mlr3misc::require_namespaces("coxed", quietly = TRUE)
 #'   library(mlr3)
 #'
 #'   # time horizon = 365 days, censoring proportion = 60%, 6 covariates normally
@@ -72,8 +72,7 @@ TaskGeneratorCoxed = R6Class("TaskGeneratorCoxed",
     .generate = function(n) {
       require_namespaces("coxed")
 
-      pv = self$param_set$values
-      data = invoke(coxed::sim.survdata, N = n, .args = pv)[[1]]
+      data = invoke(coxed::sim.survdata, N = n, .args = self$param_set$values)[[1]]
       data = map_at(data, "failed", as.integer)
 
       TaskSurv$new(id = self$id, backend = data, time = "y",

R/bibentries.R

@@ -449,5 +449,25 @@ bibentries = c(
     pages   = "299--321",
     url     = "https://doi.org/10.1177/1471082X17748083",
     year    = "2018"
+  ),
+  heyard_2020 = bibentry("article",
+    author  = "Heyard, Rachel and Timsit, Jean-Francois and Held, Leonhard",
+    title   = "Validation of discrete time-to-event prediction models in the presence of competing risks",
+    journal = "Biometrical Journal",
+    volume  = "62",
+    number  = "3",
+    pages   = "643--657",
+    year    = "2020",
+    url     = "https://doi.org/10.1002/BIMJ.201800293"
+  ),
+  schoop_2011 = bibentry("article",
+    author  = "Schoop, Roland and Beyersmann, Jan and Schumacher, Martin and Binder, Harald",
+    title   = "Quantifying the predictive accuracy of time-to-event models in the presence of competing risks",
+    journal = "Biometrical Journal",
+    volume  = "53",
+    number  = "1",
+    pages   = "88--112",
+    year    = "2011",
+    url     = "https://doi.org/10.1002/BIMJ.201000073"
   )
 )