AutoScore_testing_Survival {AutoScore}

AutoScore STEP(v) for survival outcomes: Evaluate the final score with ROC analysis (AutoScore Module 6)

Description

AutoScore STEP(v) for survival outcomes: Evaluate the final score with ROC analysis (AutoScore Module 6)

Usage

AutoScore_testing_Survival(
  test_set,
  final_variables,
  cut_vec,
  scoring_table,
  threshold = "best",
  with_label = TRUE,
  time_point = c(1, 3, 7, 14, 30, 60, 90)
)

Arguments

test_set

A processed data.frame that contains data for testing purpose. This data.frame should have same format as train_set (same variable names and outcomes)

final_variables

A vector containing the list of selected variables, selected from Step(ii) AutoScore_parsimony. Run vignette("Guide_book", package = "AutoScore") to see the guidebook or vignette.

cut_vec

Generated from STEP(iii) AutoScore_weighting_Survival().Please follow the guidebook

scoring_table

The final scoring table after fine-tuning, generated from STEP(iv) AutoScore_fine_tuning.Please follow the guidebook

threshold

Score threshold for the ROC analysis to generate sensitivity, specificity, etc. If set to "best", the optimal threshold will be calculated (Default:"best").

with_label

Set to TRUE if there are labels('label_time' and 'label_status') in the test_set and performance will be evaluated accordingly (Default:TRUE).

time_point

The time points to be evaluated using time-dependent AUC(t).

Value

A data frame with predicted score and the outcome for downstream visualization.

References

• Xie F, Ning Y, Yuan H, et al. AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data. J Biomed Inform. 2022;125:103959. doi:10.1016/j.jbi.2021.103959

See Also

AutoScore_rank_Survival, AutoScore_parsimony_Survival, AutoScore_weighting_Survival, AutoScore_fine_tuning_Survival.

Examples

## Please see the guidebook or vignettes