This app calculates the survival curve for time-to-event data using the Kaplan-Meier estimator.
input: containing the local training data (columns: features; rows: samples)
survival_function: CSV file containing the survival function datasurvival_plot: PNG image showing the survival plot (Kaplan-Meier plot)logrank_test: CSV file containing the pairwise logrank-test results
This app is not compatible with other FeatureCloud apps.
Use the config file to customize your training. Just upload it together with your training data as config.yml
fc_kaplan_meier:
files: # file names
input: lymphoma1.csv # name of the input CSV/TSV/sas7bdat file
output: # name of the output files
survival_function: survival_function # name of the CSV file containing the survival function data
survival_plot: survival_plot # name of the PNG image showing the survival plot (Kaplan-Meier plot)
logrank_test: logrank_test # If a category column is given: name of CSV file containing the pairwise logrank-test results
# parameters
parameters:
duration_col: Time # name of the column containing the time values
event_col: Censor # name of the column containing the event values (1=event occurred, 0=censored)
category_col: Stage_group # name of the column containing the categories that shall be analysed separately (e.g. treatment A vs. treatment B)
differential_privacy: none # amount of differential privacy added to the computation (none, low, middle or high)
multipletesting_method: bonferroni # Method used for testing and adjustment of pvalues in the pairwise logrank test
#bonferroni : one-step correction
#sidak : one-step correction
#holm-sidak : step down method using Sidak adjustments
#holm : step-down method using Bonferroni adjustments
#simes-hochberg : step-up method (independent)
#hommel : closed method based on Simes tests (non-negative)
#fdr_bh : Benjamini/Hochberg (non-negative)
#fdr_by : Benjamini/Yekutieli (negative)
#fdr_tsbh : two stage fdr correction (non-negative)
#fdr_tsbky : two stage fdr correction (non-negative)- Event times and counts are exchanged
- Differential privacy can be applied to make the resulting survival curves private.