Recurrent event data are commonly encountered in biomedical studies. In many situations, they are... more Recurrent event data are commonly encountered in biomedical studies. In many situations, they are subject to an informative terminal event, e.g., death. Joint modeling of recurrent and terminal events has attracted substantial recent research interests. On the other hand, there may exist a large number of covariates in such data. How to conduct variable selection for joint frailty proportional hazards models has become a challenge in practical data analysis. We tackle this issue on the basis of the “Minimum approximated Information Criterion”(MIC) method. The proposed method can be conveniently implemented in SAS Proc NLMIXED for commonly-used frailty distributions. Its finite-sample behavior is evaluated through simulation studies. We apply the proposed method to model recurrent opportunistic diseases in the presence of death in an AIDS study.
Journal of Intelligent Transportation Systems, 2011
Intelligent transportation systems–based lane management technologies were introduced to work zon... more Intelligent transportation systems–based lane management technologies were introduced to work zones in an attempt to reduce congestion and diminish queue lengths. Two forms of lane merging—the early merge and the late merge—were designed to advise drivers on definite merging locations. This study suggests two SDLMS—early merge and late merge—to supplement the current Florida Maintenance of Traffic (MOT) plans. Data were
Journal of Computational and Graphical Statistics, 2006
ABSTRACT This article proposes a data-driven tree method, called “treed variance” (TV), to model ... more ABSTRACT This article proposes a data-driven tree method, called “treed variance” (TV), to model heteroscedasticity in linear regression. Specifically, we use a score test statistic to recursively bisect data into heterogenous groups, and then adopt the pruning methodology of CART to determine the best tree size. The proposed method provides not only a piecewise constant modeling of the error variance, but also facilitates a natural check of homoscedasticity. We assess the performance of the TV method via simulation studies and illustrate its use with an empirical example.
This paper provides explicit sample size determination formulas for planning a long-term trial in... more This paper provides explicit sample size determination formulas for planning a long-term trial in patients with chronic disease by using available results from existing short-term studies that may predict long-term disease progression patterns. The sample size calculation formulas are flexible to incorporate different nonlinear disease progression patterns. Various within-patient correlation structures are considered. By using the proposed formulas, sample size sensitivity can be easily explored for possible choices of study duration, assumed nonlinear disease progression patterns, randomization ratio, and expected clinical meaningful difference in the end of study. In addition, sample size calculation formulas are provided when the primary endpoint is change from baseline. Discussions on the relationship among required sample size, study duration, randomization ratio are also included.
Recurrent event data are commonly encountered in biomedical studies. In many situations, they are... more Recurrent event data are commonly encountered in biomedical studies. In many situations, they are subject to an informative terminal event, e.g., death. Joint modeling of recurrent and terminal events has attracted substantial recent research interests. On the other hand, there may exist a large number of covariates in such data. How to conduct variable selection for joint frailty proportional hazards models has become a challenge in practical data analysis. We tackle this issue on the basis of the “Minimum approximated Information Criterion”(MIC) method. The proposed method can be conveniently implemented in SAS Proc NLMIXED for commonly-used frailty distributions. Its finite-sample behavior is evaluated through simulation studies. We apply the proposed method to model recurrent opportunistic diseases in the presence of death in an AIDS study.
Journal of Intelligent Transportation Systems, 2011
Intelligent transportation systems–based lane management technologies were introduced to work zon... more Intelligent transportation systems–based lane management technologies were introduced to work zones in an attempt to reduce congestion and diminish queue lengths. Two forms of lane merging—the early merge and the late merge—were designed to advise drivers on definite merging locations. This study suggests two SDLMS—early merge and late merge—to supplement the current Florida Maintenance of Traffic (MOT) plans. Data were
Journal of Computational and Graphical Statistics, 2006
ABSTRACT This article proposes a data-driven tree method, called “treed variance” (TV), to model ... more ABSTRACT This article proposes a data-driven tree method, called “treed variance” (TV), to model heteroscedasticity in linear regression. Specifically, we use a score test statistic to recursively bisect data into heterogenous groups, and then adopt the pruning methodology of CART to determine the best tree size. The proposed method provides not only a piecewise constant modeling of the error variance, but also facilitates a natural check of homoscedasticity. We assess the performance of the TV method via simulation studies and illustrate its use with an empirical example.
This paper provides explicit sample size determination formulas for planning a long-term trial in... more This paper provides explicit sample size determination formulas for planning a long-term trial in patients with chronic disease by using available results from existing short-term studies that may predict long-term disease progression patterns. The sample size calculation formulas are flexible to incorporate different nonlinear disease progression patterns. Various within-patient correlation structures are considered. By using the proposed formulas, sample size sensitivity can be easily explored for possible choices of study duration, assumed nonlinear disease progression patterns, randomization ratio, and expected clinical meaningful difference in the end of study. In addition, sample size calculation formulas are provided when the primary endpoint is change from baseline. Discussions on the relationship among required sample size, study duration, randomization ratio are also included.
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