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In clinical and epidemiological studies, there is a growing interest in studying the heterogeneity among patients based on longitudinal characteristics to identify subtypes of the study population. Compared to clustering a single longitudinal marker, simultaneously clustering multiple longitudinal markers allow additional information to be incorporated into the clustering process, which reveals co-existing longitudinal patterns and generates deeper biological insight. In the current study, we propose a Bayesian consensus clustering (BCC) model for multivariate longitudinal data. Instead of arriving at a single overall clustering, the proposed model allows each marker to follow marker-specific local clustering and these local clusterings are aggregated to find a global (consensus) clustering. To estimate the posterior distribution of model parameters, a Gibbs sampling algorithm is proposed. We apply our proposed model to the primary biliary cirrhosis study to identify patient subtypes that may be associated with their prognosis. We also perform simulation studies to compare the clustering performance between the proposed model and existing models under several scenarios. The results demonstrate that the proposed BCC model serves as a useful tool for clustering multivariate longitudinal data. 相似文献
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Kayla Ann Andrews Joel S. Owen James McCarthy David Wesche Nathalie Gobeau Thaddeus H. Grasela Jrg J. Mhrle 《CTS Clinical and Translational Science》2021,14(2):712
Volunteer infection studies using the induced blood stage malaria (IBSM) model have been shown to facilitate antimalarial drug development. Such studies have traditionally been undertaken in single‐dose cohorts, as many as necessary to obtain the dose‐response relationship. To enhance ethical and logistic aspects of such studies, and to reduce the number of cohorts needed to establish the dose‐response relationship, we undertook a retrospective in silico analysis of previously accrued data to improve study design. A pharmacokinetic (PK)/pharmacodynamic (PD) model was developed from initial fictive‐cohort data for OZ439 (mixing the data of the three single‐dose cohorts as: n = 2 on 100 mg, 2 on 200 mg, and 4 on 500 mg). A three‐compartment model described OZ439 PKs. Net growth of parasites was modeled using a Gompertz function and drug‐induced parasite death using a Hill function. Parameter estimates for the PK and PD models were comparable for the multidose single‐cohort vs. the pooled analysis of all cohorts. Simulations based on the multidose single‐cohort design described the complete data from the original IBSM study. The novel design allows for the ascertainment of the PK/PD relationship early in the study, providing a basis for rational dose selection for subsequent cohorts and studies. Study Highlights
- WHAT IS THE CURRENT KNOWLEDGE ON THE TOPIC?
- WHAT QUESTION DID THIS STUDY ADDRESS?
- WHAT DOES THIS STUDY ADD TO OUR KNOWLEDGE?
- HOW MIGHT THIS CHANGE CLINICAL PHARMACOLOGY OR TRANSLATIONAL SCIENCE?
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