AutoScore_weighting_Survival: AutoScore STEP(iii) for survival outcomes: Generate the...

In AutoScore: An Interpretable Machine Learning-Based Automatic Clinical Score Generator

AutoScore STEP(iii) for survival outcomes: Generate the initial score with the final list of variables (Re-run AutoScore Modules 2+3)

Description

AutoScore STEP(iii) for survival outcomes: Generate the initial score with the final list of variables (Re-run AutoScore Modules 2+3)

Usage

AutoScore_weighting_Survival(
  train_set,
  validation_set,
  final_variables,
  max_score = 100,
  categorize = "quantile",
  max_cluster = 5,
  quantiles = c(0, 0.05, 0.2, 0.8, 0.95, 1),
  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.
max_score	Maximum total score (Default: 100).
categorize	Methods for categorize continuous variables. Options include "quantile" or "kmeans" (Default: "quantile").
max_cluster	The max number of cluster (Default: 5). Available if categorize = "kmeans".
quantiles	Predefined quantiles to convert continuous variables to categorical ones. (Default: c(0, 0.05, 0.2, 0.8, 0.95, 1)) Available if categorize = "quantile".
time_point	The time points to be evaluated using time-dependent AUC(t).

Value

Generated cut_vec for downstream fine-tuning process STEP(iv) AutoScore_fine_tuning.

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_fine_tuning_Survival, AutoScore_testing_Survival.

Examples

## Not run: 
data("sample_data_survival") #
out_split <- split_data(data = sample_data_survival, ratio = c(0.7, 0.1, 0.2))
train_set <- out_split$train_set
validation_set <- out_split$validation_set
ranking <- AutoScore_rank_Survival(train_set, ntree=5)
num_var <- 6
final_variables <- names(ranking[1:num_var])
cut_vec <- AutoScore_weighting_Survival(
  train_set = train_set, validation_set = validation_set,
  final_variables = final_variables, max_score = 100,
  categorize = "quantile", quantiles = c(0, 0.05, 0.2, 0.8, 0.95, 1),
  time_point = c(1,3,7,14,30,60,90)
)

## End(Not run)

AutoScore documentation built on Oct. 16, 2022, 1:06 a.m.