Analysis of factors of pulmonary fungal infection in mice in respiratory medicine department based on logistic regression analysis model and Progranulin

The objective of this research is to solve the current medical problems of a high incidence of fungal infections in the lungs, high misdiagnosis rate, and high mortality. In this study, firstly, the logistic regression model was used to conduct. Risk factors of pulmonary fungal infection in respiratory department were analyzed. Then a model of pulmonary fungal infection in mice was constructed, and the expression difference of Progranulin (PGRN) in serum was detected by enzyme-linked immuno sorbent assay (ELISA). The expression of PGRN in lung tissues of mice infected by pulmonary fungi was detected by Western bolt method and quantitative polymerase chain reaction (PCR). The PGRN protein and mRNA expression in the lung epithelial cells of mice were detected after the infection. Results logistic regression model was used to analyze the main risk factors affecting pulmonary infection in mice. The risk factors of pulmonary fungal infection were indent catheter, hypoproteinemia, long-term use of glucocorticoid and long-term use of antibiotics. The PGRN content in serum was obviously higher than that before pulmonary fungal infection (P < 0.01). The expression of PGRN mRNA and protein in lung tissue was obviously higher than that before infection (P < 0.01). The expression of PGRN mRNA and protein in lung tissues of the infected group was obviously higher than that of the non-infected group (P < 0.01). The expression of PGRN protein in the lung epithelial cells of mice was obviously higher at 24 h after infection than before infection (P < 0.01), and the expression of PGRN mRNA was obviously higher at 12 h after infection than before infection (P < 0.01), indicating that PGRN is highly expressed in fungal pulmonary infection and is involved in disease progression. Therefore, this study provides a new idea for the diagnosis and treatment of fungal pulmonary infection in the later stage and has a good guiding significance for the diagnosis and treatment of fungal pulmonary infection.     

A Bayesian approach to a logistic regression model with incomplete information

Summary .   We consider a set of independent Bernoulli trials with possibly different success probabilities that depend on covariate values. However, the available data consist only of aggregate numbers of successes among subsets of the trials along with all of the covariate values. We still wish to estimate the parameters of a modeled relationship between the covariates and the success probabilities, e.g., a logistic regression model. In this article, estimation of the parameters is made from a Bayesian perspective by using a Markov chain Monte Carlo algorithm based only on the available data. The proposed methodology is applied to both simulation studies and real data from a dose–response study of a toxic chemical, perchlorate.     

应用神经网络和多元回归技术预测森林产量

应用传统统计技术常会因样本小和测量数据不符某种分布而受到限制。本文评价一种前馈型神经网络算法以预测落叶阔叶林产量。另外,还介绍一种由定性变为定量的数据变换方法,以用相对小的样本建立多元回归预测模型。数据变换方法有助于改善多元回归模型的预测效果。在本实验的条件下,研究结果表明神经网络技术能够产生最好的预测效果.     

沿海蝗区东亚飞蝗(Locusta migratoria manilensis)产卵场所选择的Logistic回归模型

以沿海蝗区南大港水库为研究区域,通过2002和2003两年野外450,50m规则栅格取样获取东亚飞蝗卵块、植物种类及其密度、土壤特性如含盐量、5cm含水量、pH、有机质及地形（阴坡和阳坡）等数据,采用多元Logistic回归模型,运用SAS软件筛选出与飞蝗产卵场所选择密切相关的变量,建立用于预测飞蝗产卵场所选择的Logistic回归模型。结果表明用植株密度（veg—d）和土壤含水量（water）所组建的模型能较好地预测飞蝗产卵选择,log（P（Y=1）/1-P（Y=1））=21．63-76.23/water-5．43log（water）-0．86（veg_d）。利用拟合优度（Goodness of fit）、预测准确性（Predictive accuracy）及模型x^2统计（Model chi—square statistic）等指标对模型进行评价的结果表明,所组建的用于预测飞蝗产卵场所选择的Logistic回归模型是可靠的,且能较好地预测事件是否发生。研究结果为区域蝗灾早期预警提供了科学依据和方法,对今后预测飞蝗产卵地点选择及防治决策有较高的实用性和应用价值。     

Interpreting parameters in the logistic regression model with random effects

Logistic regression with random effects is used to study the relationship between explanatory variables and a binary outcome in cases with nonindependent outcomes. In this paper, we examine in detail the interpretation of both fixed effects and random effects parameters. As heterogeneity measures, the random effects parameters included in the model are not easily interpreted. We discuss different alternative measures of heterogeneity and suggest using a median odds ratio measure that is a function of the original random effects parameters. The measure allows a simple interpretation, in terms of well-known odds ratios, that greatly facilitates communication between the data analyst and the subject-matter researcher. Three examples from different subject areas, mainly taken from our own experience, serve to motivate and illustrate different aspects of parameter interpretation in these models.     

沿海蝗区东亚飞蝗(Locusta migratoria manilensis)产卵场所选择的Logistic回归模型

以沿海蝗区南大港水库为研究区域,通过2002和2003两年野外450, 50 m规则栅格取样获取东亚飞蝗卵块、植物种类及其密度、土壤特性如含盐量、5 cm含水量、pH、有机质及地形（阴坡和阳坡）等数据,采用多元Logistic回归模型,运用SAS软件筛选出与飞蝗产卵场所选择密切相关的变量,建立用于预测飞蝗产卵场所选择的Logistic回归模型。结果表明用植株密度（veg_d）和土壤含水量（water）所组建的模型能较好地预测飞蝗产卵选择,logP(Y=1)1-P(Y=1)=21.63-76.23water-5.43log(water)-086(veg_d)。利用拟合优度(Goodness of fit)、预测准确性(Predictive accuracy)及模型x2统计(Model chi-square statistic)等指标对模型进行评价的结果表明,所组建的用于预测飞蝗产卵场所选择的Logistic回归模型是可靠的,且能较好地预测事件是否发生。研究结果为区域蝗灾早期预警提供了科学依据和方法,对今后预测飞蝗产卵地点选择及防治决策有较高的实用性和应用价值。     

Variable selection for logistic regression using a prediction-focused information criterion

In biostatistical practice, it is common to use information criteria as a guide for model selection. We propose new versions of the focused information criterion (FIC) for variable selection in logistic regression. The FIC gives, depending on the quantity to be estimated, possibly different sets of selected variables. The standard version of the FIC measures the mean squared error of the estimator of the quantity of interest in the selected model. In this article, we propose more general versions of the FIC, allowing other risk measures such as the one based on L(p) error. When prediction of an event is important, as is often the case in medical applications, we construct an FIC using the error rate as a natural risk measure. The advantages of using an information criterion which depends on both the quantity of interest and the selected risk measure are illustrated by means of a simulation study and application to a study on diabetic retinopathy.