Objective To investigate the effect of adipose stromal vascular fraction cells(SVFs)on the survival rate of fat ransplantation.Methods 0.5mL autologous fat tissue was mixed with: ① DiI-labeled autologous SVFs (Group ...Objective To investigate the effect of adipose stromal vascular fraction cells(SVFs)on the survival rate of fat ransplantation.Methods 0.5mL autologous fat tissue was mixed with: ① DiI-labeled autologous SVFs (Group A);②展开更多
In cancer clinical trials and other medical studies, both longitudinal measurements and data on a time to an event(survival time) are often collected from the same patients. Joint analyses of these data would improve ...In cancer clinical trials and other medical studies, both longitudinal measurements and data on a time to an event(survival time) are often collected from the same patients. Joint analyses of these data would improve the efficiency of the statistical inferences. We propose a new joint model for the longitudinal proportional measurements which are restricted in a finite interval and survival times with a potential cure fraction. A penalized joint likelihood is derived based on the Laplace approximation and a semiparametric procedure based on this likelihood is developed to estimate the parameters in the joint model. A simulation study is performed to evaluate the statistical properties of the proposed procedures. The proposed model is applied to data from a clinical trial on early breast cancer.展开更多
文摘Objective To investigate the effect of adipose stromal vascular fraction cells(SVFs)on the survival rate of fat ransplantation.Methods 0.5mL autologous fat tissue was mixed with: ① DiI-labeled autologous SVFs (Group A);②
基金supported by the Fundamental Research Funds for the Central Universities of ChinaNational Natural Science Foundation of China (Grant No. 11601060)+1 种基金Dalian High Level Talent Innovation Programme (Grant No.2015R051)Research Grants from Natural Sciences and Engineering Research Council of Canada
文摘In cancer clinical trials and other medical studies, both longitudinal measurements and data on a time to an event(survival time) are often collected from the same patients. Joint analyses of these data would improve the efficiency of the statistical inferences. We propose a new joint model for the longitudinal proportional measurements which are restricted in a finite interval and survival times with a potential cure fraction. A penalized joint likelihood is derived based on the Laplace approximation and a semiparametric procedure based on this likelihood is developed to estimate the parameters in the joint model. A simulation study is performed to evaluate the statistical properties of the proposed procedures. The proposed model is applied to data from a clinical trial on early breast cancer.
