Two-stage orthogonality based estimation for semiparametric varying-coefficient models and its applications in analyzing AIDS data.

Semiparametric smoothing methods are usually used to model longitudinal data, and the interest is to improve efficiency for regression coefficients. This paper is concerned with the estimation in semiparametric varying-coefficient models (SVCMs) for longitudinal data. By the orthogonal projection method, local linear technique, quasi-score estimation, and quasi-maximum likelihood estimation, we propose a two-stage orthogonality-based method to estimate parameter vector, coefficient function vector, and covariance function. The developed procedures can be implemented separately and the resulting estimators do not affect each other. Under some mild conditions, asymptotic properties of the resulting estimators are established explicitly. In particular, the asymptotic behavior of the estimator of coefficient function vector at the boundaries is examined. Further, the finite sample performance of the proposed procedures is assessed by Monte Carlo simulation experiments. Finally, the proposed methodology is illustrated with an analysis of an acquired immune deficiency syndrome (AIDS) dataset.

Sparse logistic regression for diagnosis of liver fibrosis in rat by using SCAD-penalized likelihood.

The objective of the present study is to find out the quantitative relationship between progression of liver fibrosis and the levels of certain serum markers using mathematic model. We provide the sparse logistic regression by using smoothly clipped absolute deviation (SCAD) penalized function to diagnose the liver fibrosis in rats. Not only does it give a sparse solution with high accuracy, it also provides the users with the precise probabilities of classification with the class information. In the simulative case and the experiment case, the proposed method is comparable to the stepwise linear discriminant analysis (SLDA) and the sparse logistic regression with least absolute shrinkage and selection operator (LASSO) penalty, by using receiver operating characteristic (ROC) with bayesian bootstrap estimating area under the curve (AUC) diagnostic sensitivity for selected variable. Results show that the new approach provides a good correlation between the serum marker levels and the liver fibrosis induced by thioacetamide (TAA) in rats. Meanwhile, this approach might also be used in predicting the development of liver cirrhosis.

Characterization of lymph node metastasis and its clinical significance in the surgical treatment of gastric cancer.

This study aimed to characterize lymph node metastasis and determine its clinical significance in the surgical treatment of gastric cancer. The medical charts of 920 gastric cancer patients who underwent radical surgical resection between March, 2010 and March, 2013, were retrospectively reviewed and statistically analyzed. Lymphatic metastasis was observed in 69.6% of the patients (640/920). The frequency of lymph node metastasis in patients with early-stage gastric cancer was 21.4% (18/84). Lymph node metastasis was observed in all the patients with stage IIIC-IV gastric cancer. The incidence of lymph node metastasis was higher among patients with tumors >7 cm in size. The most frequently affected lymph nodes in patients with proximal, central and distal gastric cancer were station no. 1 (34.2%), no. 3 (33.8%) and no. 6 (34.3%) lymph nodes, respectively. The frequency of lymph node metastasis in patients with Borrmann type IV cancer was significantly higher compared to that in patients with other Borrmann type cancers. Our study further demonstrated that lymphatic metastasis is closely correlated with TNM stage, location, depth of invasion and size of gastric tumors. Therefore, we recommend that a sufficient number of lymph nodes be examined from each patient to determine the extent of lymph node dissection based on Borrmann type, location, size, depth of invasion and histology of the cancer.

Bayesian inference for generalized linear mixed model based on the multivariate t distribution in population pharmacokinetic study.

This article provides a fully bayesian approach for modeling of single-dose and complete pharmacokinetic data in a population pharmacokinetic (PK) model. To overcome the impact of outliers and the difficulty of computation, a generalized linear model is chosen with the hypothesis that the errors follow a multivariate Student t distribution which is a heavy-tailed distribution. The aim of this study is to investigate and implement the performance of the multivariate t distribution to analyze population pharmacokinetic data. Bayesian predictive inferences and the Metropolis-Hastings algorithm schemes are used to process the intractable posterior integration. The precision and accuracy of the proposed model are illustrated by the simulating data and a real example of theophylline data.

Assessing influence for pharmaceutical data in zero-inflated generalized Poisson mixed models.

For clustered count data with excess zeros where the observations are either over-dispersed or under-dispersed, the zero-inflated generalized Poisson mixed (ZIGPM) regression model may be appropriate, in which the baseline discrete distribution is a generalized Poisson distribution, which is a natural extension of standard Poisson distribution. Motivated by one data set drawn from a pharmaceutical study, influence diagnostics for ZIGPM models based on case-deletion and local influence analysis are developed in this work. The one-step approximations of the estimates under case-deletion model and some case-deletion measures are given. Meanwhile, local influence measures are obtained under various perturbations of the observed data or model assumptions. Results from a pharmaceutical study illustrate the usefulness of the diagnostic statistics.