AutoScore_parsimony_Survival {AutoScore} | R Documentation |

AutoScore STEP(ii) for survival outcomes: Select the best model with parsimony plot (AutoScore Modules 2+3+4)

```
AutoScore_parsimony_Survival(
train_set,
validation_set,
rank,
max_score = 100,
n_min = 1,
n_max = 20,
cross_validation = FALSE,
fold = 10,
categorize = "quantile",
quantiles = c(0, 0.05, 0.2, 0.8, 0.95, 1),
max_cluster = 5,
do_trace = FALSE,
auc_lim_min = 0.5,
auc_lim_max = "adaptive"
)
```

`train_set` |
A processed |

`validation_set` |
A processed |

`rank` |
the raking result generated from AutoScore STEP(i) for survival
outcomes ( |

`max_score` |
Maximum total score (Default: 100). |

`n_min` |
Minimum number of selected variables (Default: 1). |

`n_max` |
Maximum number of selected variables (Default: 20). |

`cross_validation` |
If set to |

`fold` |
The number of folds used in cross validation (Default: 10). Available if |

`categorize` |
Methods for categorize continuous variables. Options include "quantile" or "kmeans" (Default: "quantile"). |

`quantiles` |
Predefined quantiles to convert continuous variables to categorical ones. (Default: c(0, 0.05, 0.2, 0.8, 0.95, 1)) Available if |

`max_cluster` |
The max number of cluster (Default: 5). Available if |

`do_trace` |
If set to TRUE, all results based on each fold of cross-validation would be printed out and plotted (Default: FALSE). Available if |

`auc_lim_min` |
Min y_axis limit in the parsimony plot (Default: 0.5). |

`auc_lim_max` |
Max y_axis limit in the parsimony plot (Default: "adaptive"). |

This is the second step of the general AutoScore-Survival workflow for
ordinal outcomes, to generate the parsimony plot to help select a
parsimonious model. In this step, it goes through AutoScore-Survival Module 2,3 and
4 multiple times and to evaluate the performance under different variable
list. The generated parsimony plot would give researcher an intuitive
figure to choose the best models. If data size is small (eg, <5000), an
independent validation set may not be a wise choice. Then, we suggest using
cross-validation to maximize the utility of data. Set
`cross_validation=TRUE`

.

List of iAUC (ie, the integrated AUC by integral under a time-dependent AUC curve for different number of variables

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

`AutoScore_rank_Survival`

,
`AutoScore_weighting_Survival`

,
`AutoScore_fine_tuning_Survival`

,
`AutoScore_testing_Survival`

.

```
## Not run:
# see AutoScore-Survival Guidebook for the whole 5-step workflow
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=10)
iAUC <- AutoScore_parsimony_Survival(
train_set = train_set, validation_set = validation_set,
rank = ranking, max_score = 100, n_min = 1, n_max = 20,
categorize = "quantile", quantiles = c(0, 0.05, 0.2, 0.8, 0.95, 1)
)
## End(Not run)
```

[Package *AutoScore* version 1.0.0 Index]