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21 to 30 of 8,386 Results
Replication Data for: Regression analysis of interval-censored competing risks data with missing causes of failure: a direct likelihood approach
May 12, 2026 - Liming XIANG
Xiang, Liming, 2026, "Replication Data for: Regression analysis of interval-censored competing risks data with missing causes of failure: a direct likelihood approach", https://doi.org/10.21979/N9/MPYZO1, DR-NTU (Data), V1
This dataset contains the MATLAB code used to generate the simulation results reported in the paper "Regression analysis of interval-censored competing risks data with missing causes of failure: a direct likelihood approach".
Mcode_direct likelihood approach.m
May 12, 2026 - Replication Data for: Regression analysis of interval-censored competing risks data with missing causes of failure: a direct likelihood approach
MATLAB Source Code - 6.2 KB - MD5: 8b35e79205a9ab973da77bce7db1d767
README.md
May 12, 2026 - Replication Data for: Regression analysis of interval-censored competing risks data with missing causes of failure: a direct likelihood approach
Markdown Text - 708 B - MD5: 51f4fc03aad7e023695b95094d14639d
Documentation
Replication Data for: Flexible modeling of left-truncated and interval-censored competing risks data with missing event types
May 12, 2026 - Liming XIANG
Xiang, Liming, 2026, "Replication Data for: Flexible modeling of left-truncated and interval-censored competing risks data with missing event types", https://doi.org/10.21979/N9/HCO7AV, DR-NTU (Data), V1
This dataset contains the MATLAB code used to generate the simulation results reported in the paper "Flexible modeling of left-truncated and interval-censored competing risks data with missing event types".
Mcode.m
May 12, 2026 - Replication Data for: Flexible modeling of left-truncated and interval-censored competing risks data with missing event types
MATLAB Source Code - 6.4 KB - MD5: cb4af006e4ed61c6477494bcb30ee23e
Replication Data for: Learning association from multiple intermediate events for dynamic prediction of survival: an application to cardiovascular disease prognosis
May 12, 2026 - Liming XIANG
Xiang, Liming, 2026, "Replication Data for: Learning association from multiple intermediate events for dynamic prediction of survival: an application to cardiovascular disease prognosis", https://doi.org/10.21979/N9/TQODZO, DR-NTU (Data), V1
This dataset contains simulation results and R code used to generate the results in the paper "Learning association from multiple intermediate events for dynamic prediction of survival: an application to cardiovascular disease prognosis".
CalCopula.R
May 12, 2026 - Replication Data for: Learning association from multiple intermediate events for dynamic prediction of survival: an application to cardiovascular disease prognosis
R Syntax - 10.2 KB - MD5: e3e47645df658dd02c145288c49ce9d9
CalRunTime.R
May 12, 2026 - Replication Data for: Learning association from multiple intermediate events for dynamic prediction of survival: an application to cardiovascular disease prognosis
R Syntax - 5.1 KB - MD5: c7ddbc1e8eca147f7e4c77964aa6f9d0
clayton_divergence_recovery.R
May 12, 2026 - Replication Data for: Learning association from multiple intermediate events for dynamic prediction of survival: an application to cardiovascular disease prognosis
R Syntax - 18.0 KB - MD5: c5cf9dc20c5349f052417c72a16013aa
framinghan_calibration_cens.R
May 12, 2026 - Replication Data for: Learning association from multiple intermediate events for dynamic prediction of survival: an application to cardiovascular disease prognosis
R Syntax - 2.7 KB - MD5: b8b399c1febf10e671a1f2def1f6632e
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