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A nonparametric regression method for multiple longitudinal phenotypes using multivariate adaptive splines
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作者 Wensheng ZHU Heping ZHANG 《Frontiers of Mathematics in China》 SCIE CSCD 2013年第3期731-743,共13页
In genetic studies of complex diseases, particularly mental illnesses, and behavior disorders, two distinct characteristics have emerged in some data sets. First, genetic data sets are collected with a large number of... In genetic studies of complex diseases, particularly mental illnesses, and behavior disorders, two distinct characteristics have emerged in some data sets. First, genetic data sets are collected with a large number of phenotypes that are potentially related to the complex disease under study. Second, each phenotype is collected from the same subject repeatedly over time. In this study, we present a nonparametric regression approach to study multivariate and time-repeated phenotypes together by using the technique of the multivariate adaptive regression splines for analysis of longitudinal data (MASAL), which makes it possible to identify genes, gene-gene and gene-environment, including time, interactions associated with the phenotypes of interest. Furthermore, we propose a permutation test to assess the associations between the phenotypes and selected markers. Through simulation, we demonstrate that our proposed approach has advantages over the existing methods that examine each longitudinal phenotype separately or analyze the summarized values of phenotypes by compressing them into one-time-point phenotypes. Application of the proposed method to the Framingham Heart Study illustrates that the use of multivariate longitudinal phenotypes enhanced the significance of the association test. 展开更多
关键词 Multivariate phenotypes longitudinal data analysis geneticassociation test multivariate adaptive regression splines
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Ovarian Cancer Screening Based on Mixture Change-Point Model
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作者 ZOU Chenchen FANG Xiangzhong ZHAI Guanghe 《Journal of Systems Science & Complexity》 SCIE EI CSCD 2015年第2期471-488,共18页
Ovarian cancer is one of the most deadly female genital malignant tumors in many regions while an effective early screening strategy can save numerous lives.CA125 and HE4 are tumor markers validated efficacious as wel... Ovarian cancer is one of the most deadly female genital malignant tumors in many regions while an effective early screening strategy can save numerous lives.CA125 and HE4 are tumor markers validated efficacious as well as most commonly used in recent screening research of ovarian cancer.In this paper,the authors construct a change-point and mixture model on the basis of longitudinal CA125 and HE4 levels and estimated parameters using maximum likelihood method with the preclinical duration assumed right-censored,which is more adaptive and yields comparable results in comparison to the Bayesian approach raised by Skates.Consistency of estimators is proved.The authors also run a 5-year simulation of sequential screening by calculating the risk of cancer and hypothesis testing the true incidence time respectively.Results show that diagnosis based on hypothesis test performs better in early detection. 展开更多
关键词 Change-point mixture model longitudinal data analysis maximum likelihood estimation ovarian caner screening.
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