肥胖症的患病因素研究与预测

肥胖症是一组常见的代谢症候群,其发病率在中国逐年上升.影响肥胖症的因素很多,本文研究的是年龄、肥胖症家族史、吸烟时间、吸烟数量、饮酒时间、饮酒频率、饮酒数量和户外活动.另外,由于肥胖症通常会有并发症,所以,还附加了高血压,冠心病,糖尿病,高血脂这四个疾病的相关指标,总共16个指标.本文首先用单因子Logistic回归挑选出与肥胖症患病相关性较大的因素,然后用主成分分析方法消除因素间的共线性,最后用标准化自变量的Logistic回归模型将这些因素对肥胖症患病影响的重要性程度进行排序,同时拟合出患病概率的预测模型.     

代谢综合症的患病因素分析与预测

以高血压患者的患病因素为例进行解析,提出代谢综合症相关问题的解决方法.研究年龄、BMI、家族史、吸烟、饮酒、文化程度、职业等18个影响高血压疾病的因素.研究的主要目的是将这些因素对高血压患病影响的重要性程度进行排序,并建立相应患病概率的预测模型.这不仅对人们日常生活疾病的预防、监控有一定的指导意义,也给医疗保险中保单的核保提供了风险度量的判别依据.首先用相关性分析剔除与高血压患病相关性较小的因素,继而用主成分分析方法消除因素间的共线性,最后用Logistic回归拟合患病概率模型并根据系数进行排序和预测.结果显示,无论性别,对高血压疾病影响最大的首先是家族史,其次为年龄.     

女职工高血压病流行特征的统计分析

本文根据山西省运城市1828名女职工2008年体检数据研究女性职工高血压的流行特征。(1)用交叉表的Pearson卡方检验方法对高血压病的相关因素进行了研究,结果表明,年龄、超重、肥胖、家族病史与高血压病有显著关系,而职业与高血压病无显著关系。(2)用回归分析方法对高血压患病率随年龄变化的规律进行研究,建立了患病率的统计模型。结果表明,从28岁起,年龄增加1岁,患高血压病的可能性增加0.8%。研究结果表明预防高血压病应加强体育锻炼。     

基于Logistic回归的主成分估计及实证分析

应用主成分估计方法，对Logistic回归模型进行参数估计，并消除多重共线性影响.首先选取了累计贡献率达到85%以上的6个主成分，对因变量进行主成分估计，然后挑选出冠心病患者发病的主要影响因素，最后得到了因变量(冠心病发病)与6个主要影响因素(血压(sbp)、累计烟草量(tobacco)、低密度脂蛋白胆固醇(ldl)、心脏病家族史(famhist)、型表现(typea)和发病年龄(age))的回归模型.根据结果可知，心脏病家族史是导致心脏病发病最大的一个原因，它是一个不可控因素；在可控因素中，累计烟草量对冠心病发病的影响最大，因此建议患者应该控制烟草摄入量，以保证病情的稳定性.     

基于Group Bridge组变量选择方法的血压影响因素实证分析

高血压是常见的心血管疾病,针对引起血压显著变化的影响因素开展深入研究对预防高血压及其并发症均具有重要意义.为此,根据11624个样本数据,选用Group Bridge方法对血压及其年龄、文化程度等8组共35个影响因素进行拟合分析.结果显示:Group Bridge方法能够提供科学有效的稀疏拟合结果;拟合结果共选定6组中的18个影响因素,其中存在正向影响关系的因素15个,负向影响关系的因素3个;综合考虑影响因素数量和强度,发现体格方面的影响因素对血压的影响最为重要,其次是体脂指标及生活方式和行为方面,再次为疾病家族史、年龄及文化程度,最后是婚姻状况及收入水平.     

宁波市社区H型高血压患病率调查和危险因素分析

目的研究宁波市社区H型高血压患病率及其相关危险因素。方法2011-12-2012-01以宁波市海曙区西门、南门社区的高血压患者为研究对象,通过问卷调查、体格检查和实验室检查,采集研究对象的社会人口学特征、一般情况等,以2011年两个社区户籍人口构成计算成人H型高血压患病率,并对H型高血压的相关危险因素进行统计学分析。结果共2077例符合入选标准的高血压患者进入研究,1895例资料完整,其中男771例（40.69%）,女1124例（59.31%）;平均年龄（66.45±8.83）岁,高血压病程（10.73±8.18）年。高血压人群中H型高血压1489例,患病率78.58%,男性为91.96%,女性为69.40%。应用宁波市海曙区疾病预防与控制中心2011年末两社区18岁以上人口数统计结果进行计算,宁波市社区成人H型高血压患病率达15.41%。男性、高龄、高血压病程较长、卒中病史、高血压家族史患者的H型高血压患病率显著升高;logistic多元回归分析可见高龄、腰围、高血压家族史、吸烟、饮酒为H型高血压的独立危险因素。结论宁波市社区成人H型高血压患病率及高血压人群H型高血压患病率较高。积极控制血压、控制腰围、戒烟限酒是降低H型高血压发病率的关键。     

扬州市肺癌发病风险因素的Logistic回归分析

取扬州市中医院2010年1月-2013年12月住院治疗的肺癌患者,共425例,对照组为同期来院体检或探视病人的家属,共425例,采用自行设计的问卷进行调查,并使用Logistic回归分析来进行统计,分析扬州市肺癌病人发病的风险因素。结果表明,肺部疾病对肺癌发病率的影响最大,然后依次是内向忧郁的不良情绪,吸烟因素,患有高血压疾病;经常食用新鲜蔬菜瓜果则是肺癌的保护性因素.给出建议:应培养乐观积极的心态,大力提倡健康的生活方式,多吃新鲜蔬果,适当进行一些体育锻炼,加强控烟力度,防治慢性病.     

急性脑梗死患者脑微出血的发病情况及其危险因素分析

目的探讨急性脑梗死患者脑微出血的发病情况及其危险因素。方法选择急性脑梗死患者75例,男51例,女24例,年龄42～83（64.9±10.8）岁。行头颅CT、磁敏感成像（SWI）检查,依据脑微出血检出的结果分成阳性组（23例）与阴性组（52例）,比较两组的一般资料,探讨脑微出血发病的危险因素。结果SWI检查的检出率比CT检出率高（χ2=27.17,P＜0.05）。阳性组年龄、高血压病、糖尿病患病率均较阴性组高,差异均有统计学意义（均P＜0.05）;两组性别、血脂异常、心房颤动、冠心病、吸烟、饮酒情况比较,差异均无统计学意义（均P＞0.05）。logistic回归分析显示年龄增长、高血压病是脑微出血的独立危险因素（P＜0.05）。结论急性脑梗死患者合并存在脑微出血的比例高,SWI检查是检测脑微出血的敏感方法,年龄大、高血压病与脑微出血的发生密切相关。     

妊娠期高血压疾病危险因素的统计分析

妊娠期高血压疾病(以下简称妊高病)是妊娠期特有的疾病.随着生活节奏快、精神压力大、高龄初产妇增多等高危因素的凸现,妊高病倾向者也逐渐增多.目前,对妊高病发病高危因素的研究很多.本文基于双重logistic回归模型对影响妊高病的危险因素进行变量选择和预测分析.     

肝癌术后肺部并发症危险因素分析

目的探讨肝癌术后发生肺部并发症的相关因素分析,以期为减少或避免术后肺部并发症的发生提供思路。方法选取行肝癌根治术的肝癌患者105例,其中22例（观察组）术后发生肺部并发症,83例（对照组）术后未发生肺部并发症。比较两组患者的一般资料（性别、年龄、吸烟指数、高血压病史、呼吸系统病史、糖尿病史、术前2周呼吸道感染史、肺部听诊情况）、术前检查结果（肿瘤位置、肿瘤大小、侵犯门静脉、侵犯肝静脉、门静脉癌栓、门静脉宽度、胆红素水平、白蛋白水平、凝血酶原时间、腹水、Child评分、Child分级、ALT、AST、GPT、AFP、血红蛋白水平、血小板、血氧饱和度、ASA分级）以及手术信息（手术切口、手术方式、手术时间、麻醉时间、术中输血量）。对上述比较差异有统计学意义的相关因素,采用非条件二元多因素Logistic回归分析其与肝癌术后肺部并发症的相关性。结果单因素分析显示：低蛋白血症、手术时间、麻醉时间及术中输血量是肝癌术后发生肺部并发症（胸腔积液、肺炎）的危险因素（P＜0.05或0.01）。多因素Logistic回归分析结果显示术前白蛋白水平是发生术后肺部并发症的独立危险因素（P＜0.05）。结论术前白蛋白水平是肝癌术后肺部并发症的独立危险因素。     

Tabaistic regression and its application to the space shuttle Challenger O-ring data

In this paper, we introduce a new family of probability distributions called the tabaistic family of distributions. The members of this family can have either unimodal or bimodal probability density functions. This family can be used when the data comes from a skewed or bimodal distribution. A major application of the unimodal member of this family is in the analysis of binary or polytomous response data when covariates are present. The logistic regression (Hosmer, D.W., Lemeshow, S.: Applied Logistic Regression. Wiley, New York (2000)) and probit analysis (Finney, D.J.: Probit Analysis. Cambridge University Press, Cambridge (1971)) are widely used when the distribution is symmetric. When the distribution is asymmetric, the tabaistic regression will be a better choice. We apply the tabaistic regression to analyze the space shuttle Challenger O-ring data and will compare the results with the logistic regression and the probit analysis models.     

Optimizing logistic regression coefficients for discrimination and calibration using estimation of distribution algorithms

Logistic regression is a simple and efficient supervised learning algorithm for estimating the probability of an outcome or class variable. In spite of its simplicity, logistic regression has shown very good performance in a range of fields. It is widely accepted in a range of fields because its results are easy to interpret. Fitting the logistic regression model usually involves using the principle of maximum likelihood. The Newton–Raphson algorithm is the most common numerical approach for obtaining the coefficients maximizing the likelihood of the data. This work presents a novel approach for fitting the logistic regression model based on estimation of distribution algorithms (EDAs), a tool for evolutionary computation. EDAs are suitable not only for maximizing the likelihood, but also for maximizing the area under the receiver operating characteristic curve (AUC). Thus, we tackle the logistic regression problem from a double perspective: likelihood-based to calibrate the model and AUC-based to discriminate between the different classes. Under these two objectives of calibration and discrimination, the Pareto front can be obtained in our EDA framework. These fronts are compared with those yielded by a multiobjective EDA recently introduced in the literature.      

多因素资产定价模型的统计分析

本以2000年沪市A股的股票作为研究对象，利用聚类分析将其分类并抽取部分作为样本，应用纲目数据统计分析方法，对下周收益率的影响因素作了Logistic回归，得出了股票的收益率具有短期反转的特点。     

Factor screening for simulation with multiple responses: Sequential bifurcation

The goal of factor screening is to find the really important inputs (factors) among the many inputs that may be changed in a realistic simulation experiment. A specific method is sequential bifurcation (SB), which is a sequential method that changes groups of inputs simultaneously. SB is most efficient and effective if the following assumptions are satisfied: (i) second-order polynomials are adequate approximations of the input/output functions implied by the simulation model; (ii) the signs of all first-order effects are known; and (iii) if two inputs have no important first-order effects, then they have no important second-order effects either (heredity property). This paper examines SB for random simulation with multiple responses (outputs), called multi-response SB (MSB). This MSB selects groups of inputs such that—within a group—all inputs have the same sign for a specific type of output, so no cancellation of first-order effects occurs. To obtain enough replicates (replications) for correctly classifying a group effect or an individual effect as being important or unimportant, MSB applies Wald’s sequential probability ratio test (SPRT). The initial number of replicates in this SPRT is also selected efficiently by MSB. Moreover, MSB includes a procedure to validate the three assumptions of MSB. The paper evaluates the performance of MSB through extensive Monte Carlo experiments that satisfy all MSB assumptions, and through a case study representing a logistic system in China; the results are very promising.     

A Hybrid Logistic Model for Case-Control Studies

For logistic regression in case-control studies, when risk factors associated with the outcome are exceedingly rare in the control group, the estimation of parameters in the model becomes difficult. In this paper, we propose a two-stage hybrid method to achieve this. In the first stage, we model the risk due to the rare factor, and in the second stage we model the residual risk due to the other factors using standard logistic model.     

Bayesian analysis of non-linear structural equation models with non-ignorable missing outcomes from reproductive dispersion models

Non-linear structural equation models are widely used to analyze the relationships among outcomes and latent variables in modern educational, medical, social and psychological studies. However, the existing theories and methods for analyzing non-linear structural equation models focus on the assumptions of outcomes from an exponential family, and hence can’t be used to analyze non-exponential family outcomes. In this paper, a Bayesian method is developed to analyze non-linear structural equation models in which the manifest variables are from a reproductive dispersion model (RDM) and/or may be missing with non-ignorable missingness mechanism. The non-ignorable missingness mechanism is specified by a logistic regression model. A hybrid algorithm combining the Gibbs sampler and the Metropolis–Hastings algorithm is used to obtain the joint Bayesian estimates of structural parameters, latent variables and parameters in the logistic regression model, and a procedure calculating the Bayes factor for model comparison is given via path sampling. A goodness-of-fit statistic is proposed to assess the plausibility of the posited model. A simulation study and a real example are presented to illustrate the newly developed Bayesian methodologies.