A signed-rank test for clustered data

Summary .   We consider the problem of comparing two outcome measures when the pairs are clustered. Using the general principle of within-cluster resampling, we obtain a novel signed-rank test for clustered paired data. We show by a simple informative cluster size simulation model that only our test maintains the correct size under a null hypothesis of marginal symmetry compared to four other existing signed rank tests; further, our test has adequate power when cluster size is noninformative. In general, cluster size is informative if the distribution of pair-wise differences within a cluster depends on the cluster size. An application of our method to testing radiation toxicity trend is presented.     

Power estimation of the t test for detecting differential gene expression

There exist now a number of statistical methods for detecting differential gene expression in experiments with microarray data. In trials under two conditions, a version of the two-sample t statistic is usually used. However, the problem of estimating the power for these tests has so far been insufficiently studied. In this paper, we propose a method to calculate the power of the robust t test for detecting differential gene expression in experiments with twins. We discuss also the results of the implementation of this method to simulated data.     

Nonparametric estimation of stage occupation probabilities in a multistage model with current status data

Multistage models are used to describe individuals (or experimental units) moving through a succession of "stages" corresponding to distinct states (e.g., healthy, diseased, diseased with complications, dead). The resulting data can be considered to be a form of multivariate survival data containing information about the transition times and the stages occupied. Traditional survival analysis is the simplest example of a multistage model, where individuals begin in an initial stage (say, alive) and move irreversibly to a second stage (death). In this article, we consider general multistage models with a directed tree structure (progressive models) in which individuals traverse through stages in a possibly non-Markovian manner. We construct nonparametric estimators of stage occupation probabilities and marginal cumulative transition hazards. Empirical calculations of these quantities are not possible due to the lack of complete data. We consider current status information which represents a more severe form of censoring than the commonly used right censoring. Asymptotic validity of our estimators can be justified using consistency results for nonparametric regression estimators. Finite-sample behavior of our estimators is studied by simulation, in which we show that our estimators based on these limited data compare well with those based on complete data. We also apply our method to a real-life data set arising from a cardiovascular diseases study in Taiwan.     

植保网络应用技术初步研究

ASP(Active Server Pages动态服务器网页）是Microsoft公司1997年推出的,WINDOWS面向WEB特征的,功能强大而成熟一种技术。2000年以来,笔对其应用于植保网络化、可视化与自动化方面作了一些探讨与开发,建立了我省第一个植保专业网站,网址www.fizb.com。阐述了植保在互联网上的初步应用研究。     

Detecting Tropical Forests' Responses to Global Climatic and Atmospheric Change: Current Challenges and a Way Forward

Because of tropical forests' disproportionate importance for world biodiversity and for the global carbon cycle, we urgently need to understand any effects on these ecosystems from the ongoing changes in climate and atmosphere. This review, intended to complement existing data reviews on this topic, focuses on three major classes of challenges that we currently face when trying to detect and interpret directional changes in tropical forests. One is the very limited existing information on the historical context of study sites. Lasting effects from past climate, natural disturbances, and/or human activities could be significantly affecting current-day processes in tropical forests and need to be investigated for all active field sites. Second, while progress has been made in recent years on standardizing and refining research approaches, a number of methods- and data-limitations continue to affect efforts both to detect within-forest changes and to relate them to ongoing environmental change. Important outstanding needs are improved sampling designs, longer time-series of observations, filling key data gaps, and data access. Finally, forest responses to ongoing environmental change are complex. The effects of many simultaneously changing environmental factors are integrated by the plants, and their responses can involve significant lags, carryovers, and non-linearities. Specifying effects of individual environmental changes, however, is required for accurate ecosystem-process models and thus for projecting future impacts on these forests. After discussing these several types of challenges and ways to address them, I conclude with a priority agenda for this critical area of research.
Abstract in Spanish is available at http://www.blackwell-synergy.com/loi/btp .     

Joint analysis of longitudinal data and recurrent episodes data with application to medical cost analysis

This paper discusses regression analysis of longitudinal data in which the observation process may be related to the longitudinal process of interest. Such data have recently attracted a great deal of attention and some methods have been developed. However, most of those methods treat the observation process as a recurrent event process, which assumes that one observation can immediately follow another. Sometimes, this is not the case, as there may be some delay or observation duration. Such a process is often referred to as a recurrent episode process. One example is the medical cost related to hospitalization, where each hospitalization serves as a single observation. For the problem, we present a joint analysis approach for regression analysis of both longitudinal and observation processes and a simulation study is conducted that assesses the finite sample performance of the approach. The asymptotic properties of the proposed estimates are also given and the method is applied to the medical cost data that motivated this study.     

Techno-economic evaluation of an inclusion body solubilization and recombinant protein refolding process

Expression of recombinant proteins in Escherichia coli is normally accompanied by the formation of inclusion bodies (IBs). To obtain the protein product in an active (native) soluble form, the IBs must be first solubilized, and thereafter, the soluble, often denatured and reduced protein must be refolded. Several technically feasible alternatives to conduct IBs solubilization and on-column refolding have been proposed in recent years. However, rarely these on-column refolding alternatives have been evaluated from an economical point of view, questioning the feasibility of their implementation at a preparative scale. The presented study assesses the economic performance of four distinct process alternatives that include pH induced IBs solubilization and protein refolding (pH_IndSR); IBs solubilization using urea, dithiothreitol (DTT), and alkaline pH followed by batch size-exclusion protein refolding; inclusion bodies (IBs) solubilization using urea, DTT, and alkaline pH followed by simulated moving bed (SMB) size-exclusion protein refolding, and IBs solubilization using urea, DTT and alkaline pH followed by batch dilution protein refolding. The economic performance was judged on the basis of the direct fixed capital, and the production cost per unit of product (P(C)). This work shows that (1) pH_IndSR system is a relatively economical process, because of the low IBs solubilization cost; (2) substituting β-mercaptoethanol for dithiothreithol is an attractive alternative, as it significantly decreases the product cost contribution from the IBs solubilization; and (3) protein refolding by size-exclusion chromatography becomes economically attractive by changing the mode of operation of the chromatographic reactor from batch to continuous using SMB technology.     

A semiparametric model for the analysis of recurrent-event panel data

In many longitudinal studies, interest focuses on the occurrence rate of some phenomenon for the subjects in the study. When the phenomenon is nonterminating and possibly recurring, the result is a recurrent-event data set. Examples include epileptic seizures and recurrent cancers. When the recurring event is detectable only by an expensive or invasive examination, only the number of events occurring between follow-up times may be available. This article presents a semiparametric model for such data, based on a multiplicative intensity model paired with a fully flexible nonparametric baseline intensity function. A random subject-specific effect is included in the intensity model to account for the overdispersion frequently displayed in count data. Estimators are determined from quasi-likelihood estimating functions. Because only first- and second-moment assumptions are required for quasi-likelihood, the method is more robust than those based on the specification of a full parametric likelihood. Consistency of the estimators depends only on the assumption of the proportional intensity model. The semiparametric estimators are shown to be highly efficient compared with the usual parametric estimators. As with semiparametric methods in survival analysis, the method provides useful diagnostics for specific parametric models, including a quasi-score statistic for testing specific baseline intensity functions. The techniques are used to analyze cancer recurrences and a pheromone-based mating disruption experiment in moths. A simulation study confirms that, for many practical situations, the estimators possess appropriate small-sample characteristics.