AutoScore_fine_tuning_Survival {AutoScore}

AutoScore STEP(iv) for survival outcomes: Fine-tune the score by revising cut_vec with domain knowledge (AutoScore Module 5)

Description

Domain knowledge is essential in guiding risk model development. For continuous variables, the variable transformation is a data-driven process (based on "quantile" or "kmeans" ). In this step, the automatically generated cutoff values for each continuous variable can be fine-tuned by combining, rounding, and adjusting according to the standard clinical norm. Revised cut_vec will be input with domain knowledge to update scoring table. User can choose any cut-off values/any number of categories. Then final Scoring table will be generated. Run vignette("Guide_book", package = "AutoScore") to see the guidebook or vignette.

Usage

AutoScore_fine_tuning_Survival(
  train_set,
  validation_set,
  final_variables,
  cut_vec,
  max_score = 100,
  time_point = c(1, 3, 7, 14, 30, 60, 90)
)

Arguments

train_set

A processed data.frame that contains data to be analyzed, for training.

validation_set

A processed data.frame that contains data for validation purpose.

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

max_score

Maximum total score (Default: 100).

time_point

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

Value

Generated final table of scoring model for downstream testing

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_testing_Survival.

Examples

## Please see the guidebook or vignettes