A reproducible simulation dataset and benchmark for parametric competing-risks inference under censoring and latent dependence.
6 affiliations
- 1Department of Information Technology, Hindusthan College of Engineering and Technology, Valley Campus, Pollachi Highway, Othakkalmandapam, Coimbatore, Tamil Nadu, 641032, India.
- 2Department of Electronics and Communication Engineering, SNS College of Technology, SNS Kalvi Nagar, Sathy Main Road, Vazhiyampalayam, Saravanampatti Post, Coimbatore, Tamil Nadu, 641035, India.
- 3Department of Computer Science and Engineering, Dr. Mahalingam College of Engineering and Technology, Mackkinaickenpatti Post, Udumalai Road, Pollachi, Coimbatore, Tamil Nadu, 642003, India.
- 4Department of Electronics and Communication Engineering, Kalaignarkarunanidhi Institute of Technology, Kannampalayam Post, Coimbatore, Tamil Nadu, 641402, India.
- 5Department of Computer Science and Engineering, Shree Venkateshwara Hi-Tech Engineering College, Erode-Gobi Main Road, Sri Kalivani Nagar, Othakuthirai, Gobichettipalayam, Erode, Tamil Nadu, 638455, India.
- 6Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, No. 42, Avadi-Vel Tech Road, Vel Nagar, Avadi, Chennai, Tamil Nadu, 600062, India.
Abstract
This article describes a simulation dataset for evaluating parametric competing-risks inference under censoring and latent dependence. The dataset was generated in Python and contains 648,000 replicate records from 303 block-specific scenarios, representing 291 unique statistical conditions. The design includes exponential, Weibull and Gompertz lifetime models, two, three and five competing causes, and sample sizes ranging from 20 to 200. Type-I, Type-II and hybrid censoring are considered. A separate dependence block uses Clayton, Frank and Gumbel copulas with Kendall's tau values of 0.25, 0.50 and 0.75, with inference performed under a working-independence assumption. Replicate records contain estimates, test statistics and availability indicators for Wald, likelihood-ratio and score tests. The release provides 303 Parquet partitions, scenario and parameter registries, summaries of bias and root mean squared error, Monte Carlo uncertainty estimates, and bootstrap coverage checks. A representative subject-level sample contains 35,250 rows. Supporting materials include an executed notebook, figures, a field-level data dictionary, validation records and file checksums. Recorded identifiers and deterministic seeds support regeneration of individual simulated studies. Researchers can use the data to evaluate statistical procedures across the documented conditions, examine test availability and boundary cases, reproduce benchmark calculations, and develop teaching examples.
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BibTeX
@article{sabrigiriraj2026reproducible,
title = {{A reproducible simulation dataset and benchmark for parametric competing-risks inference under censoring and latent dependence.}},
author = {Sabrigiriraj M and Manoharan K and Senthil Madasamy N and Govindaraj V and Prathipa K and Ravi T},
journal = {Data in brief},
year = {2026},
month = aug,
doi = {10.1016/j.dib.2026.113340},
url = {https://europepmc.org/article/MED/42927294},
note = {CC BY},
}
RIS
TY - JOUR
TI - A reproducible simulation dataset and benchmark for parametric competing-risks inference under censoring and latent dependence.
AU - Sabrigiriraj M
AU - Manoharan K
AU - Senthil Madasamy N
AU - Govindaraj V
AU - Prathipa K
AU - Ravi T
PY - 2026
DA - 2026/08/01
JO - Data in brief
DO - 10.1016/j.dib.2026.113340
UR - https://europepmc.org/article/MED/42927294
AB - This article describes a simulation dataset for evaluating parametric competing-risks inference under censoring and latent dependence. The dataset was generated in Python and contains 648,000 replicate records from 303 block-specific scenarios, representing 291 unique statistical conditions. The design includes exponential, Weibull and Gompertz lifetime models, two, three and five competing causes, and sample sizes ranging from 20 to 200. Type-I, Type-II and hybrid censoring are considered. A separate dependence block uses Clayton, Frank and Gumbel copulas with Kendall's tau values of 0.25, 0.50 and 0.75, with inference performed under a working-independence assumption. Replicate records contain estimates, test statistics and availability indicators for Wald, likelihood-ratio and score tests. The release provides 303 Parquet partitions, scenario and parameter registries, summaries of bias and root mean squared error, Monte Carlo uncertainty estimates, and bootstrap coverage checks. A representative subject-level sample contains 35,250 rows. Supporting materials include an executed notebook, figures, a field-level data dictionary, validation records and file checksums. Recorded identifiers and deterministic seeds support regeneration of individual simulated studies. Researchers can use the data to evaluate statistical procedures across the documented conditions, examine test availability and boundary cases, reproduce benchmark calculations, and develop teaching examples.
SN - 2352-3409
ER -