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

The files of this dataset have 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 the 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 type of the sample (train, val, test). These field is need to split dataset into train-validate-test samples for ML-model training;
• H0_H1 is a true hypothesis: if H0, then test statistics were simulated under S1(t)=S2(t); if H1, then test statistics were simulated under S1(t)≠S2(t);
• Hi is an alternative hypothesis (H01-H09, H11-H19, or H21-H29) for S1(t) and S2(t). Detailed description of these alternatives can be found in the paper;
• n1 is the size of the sample 1;
• n2 is the size of the sample 2;
• perc is a set (expected) censoring rate for the samples 1 and 2;
• real_perc1 is an actual censoring rate of sample 1;
• real_perc2 is an actual censoring rate of sample 2;
• Peto_test is a statistic of the Peto and Peto’s Generalized Wilcoxon test (which is computed on two samples under parameters described above);
• Gehan_test is a statistic of the Gehan’s Generalized Wilcoxon test;
• logrank_test is a statistic of the logrank test;
• CoxMantel_test is a statistic of the Cox-Mantel test;
• BN_GPH_test is a statistic of the Bagdonavičius-Nikulin test (Generalized PH model);
• BN_MCE_test is a statistic of the Bagdonavičius-Nikulin test (Multiple Crossing-Effect model);
• BN_SCE_test is a statistic of the Bagdonavičius-Nikulin test (Single Crossing-Effect model);
• Q_test is a statistic of the Q-test;
• MAX_Value_test is a statistic of the Maximum Value test;
• MIN3_test is a statistic of the MIN3 test;
• WLg_logrank_test is a statistic of the Weighted Logrank test (weighted function: 'logrank');
• WLg_TaroneWare_test is a statistic of the Weighted Logrank test (weighted function: 'Tarone-Ware');
• WLg_Breslow_test is a statistic of the Weighted Logrank test (weighted function: 'Breslow');
• WLg_PetoPrentice_test is a statistic of the Weighted Logrank test (weighted function: 'Peto-Prentice');
• WLg_Prentice_test is a statistic of the Weighted Logrank test (weighted function: 'Prentice');
• WKM_test is a statistic of the Weighted Kaplan-Meier test;
• CatBoost_test is a statistic of the proposed ML-method based on the CatBoost framework,
• XGBoost_test,
• LightAutoML_test is a statistic of the proposed ML-method based on the LightAutoML (LAMA) framework,
• SKLEARN_RF_test,
• SKLEARN_LogReg_test,
• SKLEARN_GB_test are test statistics of the proposed ML-based methods.