Estimating population size for a continuous time frailty model with covariates in a capture-recapture study

A continuous time frailty capture-recapture model is proposed for estimating population size of a closed population with the use of observed covariates to explain individuals' heterogeneity in presence of a random effect. A conditional likelihood approach is used to derive the estimate of parameters, and the Horvitz-Thompson estimator is adopted to estimate the unknown population size. Asymptotic normality of the estimates is obtained. Simulation results and a real example show that the proposed method works satisfactorily.     

Modelling nematode movement using time-fractional dynamics

We use a correlated random walk model in two dimensions to simulate the movement of the slug parasitic nematode Phasmarhabditis hermaphrodita in homogeneous environments. The model incorporates the observed statistical distributions of turning angle and speed derived from time-lapse studies of individual nematode trails. We identify strong temporal correlations between the turning angles and speed that preclude the case of a simple random walk in which successive steps are independent. These correlated random walks are appropriately modelled using an anomalous diffusion model, more precisely using a fractional sub-diffusion model for which the associated stochastic process is characterised by strong memory effects in the probability density function.     

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.     

A space-time conditional intensity model for invasive meningococcal disease occurrence

A novel point process model continuous in space-time is proposed for quantifying the transmission dynamics of the two most common meningococcal antigenic sequence types observed in Germany 2002-2008. Modeling is based on the conditional intensity function (CIF), which is described by a superposition of additive and multiplicative components. As an epidemiological interesting finding, spread behavior was shown to depend on type in addition to age: basic reproduction numbers were 0.25 (95% CI 0.19-0.34) and 0.11 (95% CI 0.07-0.17) for types B:P1.7-2,4:F1-5 and C:P1.5,2:F3-3, respectively. Altogether, the proposed methodology represents a comprehensive and universal regression framework for the modeling, simulation, and inference of self-exciting spatiotemporal point processes based on the CIF. Usability of the modeling in biometric practice is promoted by an implementation in the R package surveillance.     

制备遗传工程小鼠的技术改进

遗传工程小鼠是当今生命科学领域集成度最高的研究体系之一。特别在“人类基因组计划和小鼠基因组计划”完成后,遗传工程小鼠在制备人类疾病模型、药物开发和评价、基因功能分析以及比较基因组学中发挥着越来越重要的作用。由此,也推动了遗传工程小鼠相关技术的快速发展。就遗传工程小鼠制备的现况、存在的问题以及新策略等相关问题进行了总结。     

Robust tests for treatment effects based on censored recurrent event data observed over multiple periods

We derive semiparametric methods for estimating and testing treatment effects when censored recurrent event data are available over multiple periods. These methods are based on estimating functions motivated by a working "mixed-Poisson" assumption under which conditioning can eliminate subject-specific random effects. Robust pseudoscore test statistics are obtained via "sandwich" variance estimation. The relative efficiency of conditional versus marginal analyses is assessed analytically under a mixed time-homogeneous Poisson model. The robustness and empirical power of the semiparametric approach are assessed through simulation. Adaptations to handle recurrent events arising in crossover trials are described and these methods are applied to data from a two-period crossover trial of patients with bronchial asthma.     

A unified parametric regression model for recapture studies with random removals in continuous time

Conditional likelihood based on counting processes are combined with a Horvitz-Thompson estimator to yield a population size estimator that is more efficient than the existing ones. Random removals are allowed in the recapturing process. Simulation studies are shown to assess the performance of the proposed estimators. Examples on a bird banding and a small mammal recapturing study are given.     

体外培养骨髓巨核细胞脱氢酶活性的细胞化学观察

本文采用液体培养体系结合酶细胞化学方法,对体外培养不同发育阶段的小鼠肾髓巨核细胞乳酸脱氢酶、苹果酸脱氢酶、谷氨酸脱氢酶的活性变化进行了动态观察。在9天培养期间,巨核细胞的增殖数在5—7天达到高峰,并随时间有不同程度分化。对培养3、5、7、9天的巨核细胞进行酶细胞化学研究,结果表明,巨核细胞在发育成熟前,三种酶活性均有增高。提示巨核细胞在分化过程中,糖酵解及三羧酸循环代谢均有增强。     

Evaluation of FKBP and DHFR based destabilizing domains in Saccharomyces cerevisiae

Two orthogonal destabilizing domains have been developed based on mutants of human FKBP12 as well as bacterial DHFR and these engineered domains have been used to control protein concentration in a variety of contexts in vitro and in vivo. FKBP12 based destabilizing domains cannot be rescued in the yeast Saccharomyces cerevisiae; ecDHFR based destabilizing domains are not degraded as efficiently in S. cerevisiae as in mammalian cells or Plasmodium, but provide a starting point for the development of domains with increased signal-to-noise in S. cerevisiae.     

Population size estimation in a two-list surveillance system with a discrete covariate

Summary .   Capture–recapture methods are widely adopted to estimate sizes of populations of public health interest using information from surveillance systems. For a two-list surveillance system with a discrete covariate, a population is divided into several subpopulations. A unified framework is proposed in which the logits of presence probabilities are decomposed into case effects and list effects. The estimators for the whole population and subpopulation sizes, their adjusted versions, and asymptotic standard errors admit closed-form expressions. Asymptotic and bootstrap individual and simultaneous confidence intervals are easily constructed. Conditional likelihood ratio tests are used to select one from three possible models. Real examples are investigated.     

Nonparametric association analysis of bivariate left‐truncated competing risks data

We develop time‐varying association analyses for onset ages of two lung infections to address the statistical challenges in utilizing registry data where onset ages are left‐truncated by ages of entry and competing‐risk censored by deaths. Two types of association estimators are proposed based on conditional cause‐specific hazard function and cumulative incidence function that are adapted from unconditional quantities to handle left truncation. Asymptotic properties of the estimators are established by using the empirical process techniques. Our simulation study shows that the estimators perform well with moderate sample sizes. We apply our methods to the Cystic Fibrosis Foundation Registry data to study the relationship between onset ages of Pseudomonas aeruginosa and Staphylococcus aureus infections.     

Estimating the species accumulation curve using mixtures

As a significant tool in ecological studies, the species accumulation curve or the collector's curve is the graph of the expected number of detected species as a function of sampling effort. The problem of estimating the species accumulation curve based on an empirical data set arising from quadrat sampling is studied in a nonparametric binomial mixture model. It will be shown that estimating the species accumulation curve not only is independent of the unknown number of species but also includes estimating the number of species as a limiting case. For the purpose of interpolation, moment-based estimators, associated with asymptotic confidence intervals, are developed from several points of view. A likelihood-based procedure is developed for the purpose of extrapolation, associated with bootstrap confidence intervals. The proposed methods are illustrated by ecological data sets.