Luan Lin
Research Subjects
Research Interests
Luan Lin has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Kexuan Li
Global Biometrics and Data Sciences Bristol Myers Squibb, Cambridge, Massachusetts, US
1 shared publication - Lingli Yang
Global Analytics and Data Sciences Department of Mathematical Sciences Department of Mathematics and Statistics Biogen Worcester Polytechnic Institute Texas Tech University
1 shared publication - Peng Sun1 shared publication
- Shaofei Zhao1 shared publication
- Susie Sinks1 shared publication
Latest Publications
- Open Access
Multivariate Rank-Based Analysis of Multiple Endpoints in Clinical Trials: A Global Test Approach
Clinical trials often involve the assessment of multiple endpoints to comprehensively evaluate the efficacy and safety of interventions. In the work, we consider a global nonparametric testing procedure based on multivariate rank for the analysis of multiple endpoints in clinical trials. Unlike other existing approaches that rely on pairwise comparisons for each individual endpoint, the proposed method directly incorporates the multivariate ranks of the observations. By considering the joint ranking of all endpoints, the proposed approach provides robustness against diverse data distributions and censoring mechanisms commonly encountered in clinical trials. Through extensive simulations, we demonstrate the superior performance of the multivariate rank-based approach in controlling type I error and achieving higher power compared to existing rank-based methods. The simulations illustrate the advantages of leveraging multivariate ranks and highlight the robustness of the approach in various settings. The proposed method offers an effective tool for the analysis of multiple endpoints in clinical trials, enhancing the reliability and efficiency of outcome evaluations.
Open AccessMultivariate Rank-Based Analysis of Multiple Endpoints in Clinical Trials: A Global Test Approach
Clinical trials often involve the assessment of multiple endpoints to comprehensively evaluate the efficacy and safety of interventions. In the work, we consider a global nonparametric testing procedure based on multivariate rank for the analysis of multiple endpoints in clinical trials. Unlike other existing approaches that rely on pairwise comparisons for each individual endpoint, the proposed method directly incorporates the multivariate ranks of the observations. By considering the joint ranking of all endpoints, the proposed approach provides robustness against diverse data distributions and censoring mechanisms commonly encountered in clinical trials. Through extensive simulations, we demonstrate the superior performance of the multivariate rank-based approach in controlling type I error and achieving higher power compared to existing rank-based methods. The simulations illustrate the advantages of leveraging multivariate ranks and highlight the robustness of the approach in various settings. The proposed method offers an effective tool for the analysis of multiple endpoints in clinical trials, enhancing the reliability and efficiency of outcome evaluations.