Dr. Xiang Liming's areas of expertise are survival analysis, longitudinal/clustered data analysis, mixture modelling and biostatistics. Her current research work focuses on developing semiparametric methods for analysis of survival data subject to complex censoring that arises in health and biomedical studies.

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1 to 5 of 5 Results
Replication Data for: Disease progression based feature screening for ultrahigh-dimensional survival-associated biomarkers
May 12, 2026
Xiang, Liming, 2026, "Replication Data for: Disease progression based feature screening for ultrahigh-dimensional survival-associated biomarkers", https://doi.org/10.21979/N9/OSRVWN, DR-NTU (Data), V1
This dataset contains the R code for simulation studies reported in the paper "Disease progression based feature screening for ultrahigh-dimensional survival-associated biomarkers".
Replication Data for: Analysis of Competing Risks Data with Covariates Subject to Detection Limits
May 12, 2026
Xiang, Liming, 2026, "Replication Data for: Analysis of Competing Risks Data with Covariates Subject to Detection Limits", https://doi.org/10.21979/N9/NNUDA2, DR-NTU (Data), V1
This dataset contains a set of simulated data and the R code for simulation studies reported in the paper "Analysis of Competing Risks Data with Covariates Subject to Detection Limits".
Replication Data for: Regression analysis of interval-censored competing risks data with missing causes of failure: a direct likelihood approach
May 12, 2026
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".
Replication Data for: Flexible modeling of left-truncated and interval-censored competing risks data with missing event types
May 12, 2026
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".
Replication Data for: Learning association from multiple intermediate events for dynamic prediction of survival: an application to cardiovascular disease prognosis
May 12, 2026
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".
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