An Asymptotically Optimal Adaptive Selection Procedure in the Proportional Hazards Model with Conditionally Independent Censoring

Assume k independent populations are given which are distributed according to R, …,R (ϑ_i ∈ Θ ⊆ R ). Taking samples of size n the population with the smallest ϑ-value is to be selected. Using the framework of Le Cam's decision theory (Le Cam , 1986; Strasser , 1985) under mild regularity assumptions, an asymptotically optimal selection procedure is derived for the sequence of localized models. In the proportional hazards model with conditionally independent censoring, an asymptotically optimal adaptive selection procedure is constructed by substituting the unknown nuisance parameter by a kernel estimator.     

Semiparametric estimation of proportional mean residual life model in presence of censoring

A mean residual life function is the average remaining life of a surviving subject, as it varies with time. The proportional mean residual life model was proposed by Oakes and Dasu (1990, Biometrika77, 409-410) in regression analysis to study its association with related covariates in absence of censoring. In this article, we develop some semiparametric estimation procedures to take censoring into account. The proposed methodology is evaluated via simulation studies, and further applied to a clinical trial of chemotherapy in postoperative radiotherapy of lung cancer patients.     

Semiparametric regression analysis of longitudinal data with informative drop-outs

Informative drop-out arises in longitudinal studies when the subject's follow-up time depends on the unobserved values of the response variable. We specify a semiparametric linear regression model for the repeatedly measured response variable and an accelerated failure time model for the time to informative drop-out. The error terms from the two models are assumed to have a common, but completely arbitrary joint distribution. Using a rank-based estimator for the accelerated failure time model and an artificial censoring device, we construct an asymptotically unbiased estimating function for the linear regression model. The resultant estimator is shown to be consistent and asymptotically normal. A resampling scheme is developed to estimate the limiting covariance matrix. Extensive simulation studies demonstrate that the proposed methods are suitable for practical use. Illustrations with data taken from two AIDS clinical trials are provided.     

抗菌肽临床应用前景分析

抗菌肽是生物天然免疫的重要组成部分,几乎存在于所有种类的生物中。目前已发现的抗菌肽超过2 000种。抗菌肽具有广谱抗菌活性,对大多数革兰氏阳性菌、革兰氏阴性菌和真菌具有强大的抑制作用（包括多药物耐受微生物）,而且这种作用具有较好的选择性。这些特点使抗菌肽具有成为抗感染药物的重大潜力;但抗菌肽的临床应用也面临着一些困难,如抗菌肽大量生产、体内稳定性、微生物耐受等。对抗菌肽临床应用面临的问题及正在进行临床研究和临床前研究的抗菌肽做一简要综述。     

Quantile regression for doubly censored data

Double censoring often occurs in registry studies when left censoring is present in addition to right censoring. In this work, we propose a new analysis strategy for such doubly censored data by adopting a quantile regression model. We develop computationally simple estimation and inference procedures by appropriately using the embedded martingale structure. Asymptotic properties, including the uniform consistency and weak convergence, are established for the resulting estimators. Moreover, we propose conditional inference to address the special identifiability issues attached to the double censoring setting. We further show that the proposed method can be readily adapted to handle left truncation. Simulation studies demonstrate good finite-sample performance of the new inferential procedures. The practical utility of our method is illustrated by an analysis of the onset of the most commonly investigated respiratory infection, Pseudomonas aeruginosa, in children with cystic fibrosis through the use of the U.S. Cystic Fibrosis Registry.     

蛋白质定向进化的研究进展

定向进化是改造蛋白质分子的一种有效的新策略。主要是在实验室里模拟自然进化过程,通过由易错PCR、致突变菌株诱变等方法对编码蛋白质的基因进行随机诱变,由DNA改组、随机引导重组和交错延伸等方法进行突变基因体外重组,设计高通量筛选方法来选出需要的突变株。它不仅可快速产生工业上有用的新酶,而且对研究蛋白质的结构与功能的关系具有非常重要的意义。     

Likelihood-Based Modeling and Analysis of Data Underdispersed Relative to the Poisson Distribution

By using a generalization of the Poisson process, distributions can be constructed that show appropriate amounts of underdispersion relative to the Poisson distribution that may be apparent from observed data. These are then used to examine the differences between the distributions of numbers of fetal implants in mice corresponding to different doses of the herbicide 2,4,5-T.