README.md

metadata

license: cc-by-4.0

Machine Learning for Two-Sample Testing under Right-Censored Data: A Simulation Study

• Petr PHILONENKO, Ph.D. in Computer Science;
• Sergey POSTOVALOV, D.Sc. in Computer Science.

About

This dataset is a supplement to the github repositiry and paper addressed to solve the two-sample problem under right-censored observations using Machine Learning. The problem statement can be formualted as H0: S1(t)=S2(t) versus H: S1(t)≠S_2(t) where S1(t) and S2(t) are survival functions of samples X1 and X2.

This dataset contains the synthetic data simulated by the Monte Carlo method and Inverse Transform Sampling.

Repository

This dataset has following structure:

data
├── 1_raw
│   └── two_sample_problem_dataset.tsv.gz
├── 2_samples
│   ├── sample_train.tsv.gz
│   └── sample_simulation.tsv.gz
└── 3_dataset_with_ML_pred
    └── dataset_with_ML_pred.tsv.gz

• two_sample_problem_dataset.tsv.gz is a raw simulated data. In the github repositiry, this file must be located in the ML_for_TwoSampleTesting/proposed_ml_for_two_sample_testing /data/1_raw/;
• sample_train.tsv.gz and sample_simulation.tsv.gz are train and test samples splited from the two_sample_problem_dataset.tsv.gz. In the github repositiry, these files must be located in the ML_for_TwoSampleTesting/proposed_ml_for_two_sample_testing/data/2_samples/;
• dataset_with_ML_pred.tsv.gz is the test sample supplemented by the predictions of the proposed ML-methods. In the github repositiry, this file must be located in ML_for_TwoSampleTesting/proposed_ml_for_two_sample_testing/data/3_dataset_with_ML_pred/.

Dataset & Samples

In these files there are following fields:

• sample is a sample type (train, val, test);
• H0_H1 is a true hypothesis (H0 or H1);
• Hi is an alternative hypothesis (H01-H09, H11-H19, or H21-H29);
• n1 is the size of sample 1;
• n2 is the size of sample 2;
• real_perc1 is an actual censoring rate of sample 1;
• real_perc2 is an actual censoring rate of sample 2;
• perc is the set censoring rate for the samples 1 and 2;
• Peto_test, Gehan_test, logrank_test, CoxMantel_test, BN_GPH_test, BN_MCE_test, BN_SCE_test, Q_test, MAX_Value_test, MIN3_test, WLg_logrank_test, WLg_TaroneWare_test, WLg_Breslow_test, WLg_PetoPrentice_test, WLg_Prentice_test, WKM_test are test statistics of classical two-sample tests under right-censored data;
• CatBoost_test, XGBoost_test, LightAutoML_test, SKLEARN_RF_test, SKLEARN_LogReg_test, SKLEARN_GB_test are test statistics of the proposed ML-based methods.