eval_Coxmos_models function

eval_Coxmos_models

The eval_Coxmos_models function facilitates the comprehensive evaluation of multiple Coxmos models in a concurrent manner. It is designed to provide a detailed assessment of the models' performance by calculating: C-Index, Integrative Brier Score and the Area Under the Curve (AUC) for each model at specified time points. The results generated by this function are primed for visualization using the plot_evaluation() function. Important, use always raw data.

eval_Coxmos_models(
  lst_models,
  X_test,
  Y_test,
  pred.method = "cenROC",
  pred.attr = "mean",
  times = NULL,
  PARALLEL = FALSE,
  max_time_points = 15,
  verbose = FALSE,
  progress_bar = TRUE
)

Arguments

• lst_models: List of Coxmos models. Each object of the list must be named.
• X_test: Numeric matrix or data.frame. Explanatory variables for test data (raw format). Qualitative variables must be transform into binary variables.
• Y_test: Numeric matrix or data.frame. Response variables for test data. Object must have two columns named as "time" and "event". For event column, accepted values are: 0/1 or FALSE/TRUE for censored and event observations.
• pred.method: Character. AUC evaluation algorithm method for evaluate the model performance. Must be one of the following: "risksetROC", "survivalROC", "cenROC", "nsROC", "smoothROCtime_C", "smoothROCtime_I" (default: "cenROC").
• pred.attr: Character. Way to evaluate the metric selected. Must be one of the following: "mean" or "median" (default: "mean").
• times: Numeric vector. Time points where the AUC will be evaluated. If NULL, a maximum of 'max_time_points' points will be selected equally distributed (default: NULL).
• PARALLEL: Logical. Run the cross validation with multicore option. As many cores as your total cores - 1 will be used. It could lead to higher RAM consumption (default: FALSE).
• max_time_points: Numeric. Maximum number of time points to use for evaluating the model (default: 15).
• verbose: Logical. If verbose = TRUE, extra messages could be displayed (default: FALSE).
• progress_bar: Logical. If progress_bar = TRUE, progress bar is shown (default = TRUE).

Returns

A list of four objects. df: A data.frame which the global predictions for all models. This data.frame is used to plot the information by the function plot_evaluation(). lst_AUC: A list of models where the user can check the linear predictors computed, the global AUC, the AUC per time point and the predicted time points selected. lst_BRIER: A list of models where the user can check the predicted time points selected, the Brier Score per time point and the Integrative Brier score (computed by survcomp::sbrier.score2proba). time: Time used for evaluation process.

Details

The function begins by validating the names of the models provided in the lst_models list and ensures that there are at least two events present in the dataset. It then checks for the availability of the specified evaluation method and ensures that the test times are consistent with the training times of the models.

The core of the function revolves around the evaluation of each model. Depending on the user's preference, the evaluations can be executed in parallel, which can significantly expedite the process, especially when dealing with a large number of models. The function employs various evaluation methods, as specified by the pred.method parameter, to compute the AUC values. These methods include but are not limited to "risksetROC", "survivalROC", and "cenROC". The metric Integrative Brier Score is computed by survcomp::sbrier.score2proba() function.

Post-evaluation, the function collates the results, including training times, AIC values, c-index, Brier scores, and AUC values for each time point. The results are then transformed into a structured data frame, making it conducive for further analysis and visualization. It's worth noting that potential issues in AUC computation, often arising from sparse samples, are flagged to the user for further inspection.

Examples

data("X_proteomic")
data("Y_proteomic")
set.seed(123)
index_train <- caret::createDataPartition(Y_proteomic$event, p = .5, list = FALSE, times = 1)
X_train <- X_proteomic[index_train,1:50]
Y_train <- Y_proteomic[index_train,]

X_test <- X_proteomic[-index_train,1:50]
Y_test <- Y_proteomic[-index_train,]

model_icox <- splsicox(X_train, Y_train, n.comp = 2)
model_drcox <- splsdrcox_penalty(X_train, Y_train, n.comp = 2)
lst_models <- list("splsicox" = model_icox, "splsdrcox" = model_drcox)
eval_Coxmos_models(lst_models, X_test, Y_test, pred.method = "cenROC")

References

\insertRef C_INDEXCoxmos

\insertRef survcomp_IBRIERCoxmos

\insertRef survivalROCCoxmos

\insertRef risksetROCCoxmos

\insertRef cenROCCoxmos

\insertRef nsROCCoxmos

\insertRef smoothROCtime_RPackageCoxmos

Author(s)

Pedro Salguero Garcia. Maintainer: pedsalga@upv.edu.es