预测抗结核药物性肝损伤的卷积神经网络与logistic回归模型的建立与比较CSCD

目的基于卷积神经网络(CNN)和多重logistic回归建立抗结核药物性肝损伤(ATB-DILI)的预测模型,评价和比较2个模型的性能。方法收集2019年1月1日至2022年10月31日镇江市第三人民医院、句容市人民医院和丹阳市第三人民医院的结核病住院患者临床及实验室检查数据。根据是否发生ATB-DILI将患者分为发生和未发生ATB-DILI组,比较2组临床特征差异。采用随机数字表法将患者按7︰3的比例分为训练集和测试集。基于训练集数据,分别采用多重logistic回归和CNN建立ATB-DILI预测模型;基于训练集和测试集数据,对2个模型预测ATB-DILI的准确度进行验证。绘制受试者工作特征(ROC)曲线,比较2种模型的灵敏度、特异度、约登指数和曲线下面积(AUC)。结果共有3012例患者纳入研究,其中294例(9.76%)被诊断为ATB-DILI;训练集2108例,测试集904例。多重logistic回归分析的结果显示,年龄、肝病史、低白蛋白血症和未预防性使用保肝药是ATB-DILI发生的独立风险因素,并以独立风险因素构建多重logistic回归模型方程。采用CNN对训练集患者数据的深度学习和分析结果显示,对ATB-DILI发生影响最大的前5位风险因素分别为肝病史、年龄、未预防使用保肝药、低白蛋白血症及饮酒,以前5位风险因素构建CNN模型。采用多重logistic回归模型预测的训练集与测试集患者ATB-DILI发生的总准确率分别为87.62%和88.27%;采用CNN模型预测训练集与测试集患者ATB-DILI发生的总准确率分别为92.36%和91.70%。CNN模型的灵敏度、特异度和AUC均高于多重logistic回归模型,差异均有统计学意义(均P<0.05)。结论多重logistic回归模型和CNN模型对ATB-DILI发生的预测性能均较好。但相较多重logistic回归模型,CNN模型对结核病住院患者ATB-DILI发生的预测性能更佳。     

妊娠高血压病人不同药物与妊娠结局关系的主成分logistic回归

目的:探讨主成分logistic回归分析方法在研究妊娠高血压病人用药与其妊娠结局关系中的作用以及不同药物对妊娠结局的影响,解决多个疾病因素变量存在共线性的问题。方法:对logistic回归模型进行共线性诊断,应用主成分分析方法对存在共线性的变量综合成一个主分量,然后进行logistic回归分析,得到并解释最终的回归模型。结果:不同药物的不同剂量对妊娠结局的影响各不相同。主成分logistic回归在研究妊娠高血压病人药物与妊娠结局关系中有较好的作用。在对疾病因素进行logistic回归分析时,若变量间存在多重共线性,采用主成分改进的logistic回归分析能得出更合理的回归模型。     

金黄色葡萄球菌环丙沙星耐药性发展趋势的logistic模型分析

目的:了解我国金黄色葡萄球菌环丙沙星耐药性的现状和变迁,探讨logistic模型在分析细菌耐药性发展规律中的应用价值。方法:从中国期刊全文数据库收集1991～2007年中国内地金黄色葡萄球菌环丙沙星耐药数据,同时收集上海地区部分年份金黄色葡萄球菌环丙沙星耐药率。应用logistic模型对金黄色葡萄球菌的环丙沙星耐药率数据拟合分析,对拟合结果做适合性拟合优度检验。结果:共收集文献637篇,含金黄色葡萄球菌94 971株,其中41 615株对环丙沙星耐药。对logistic模型拟合金黄色葡萄球菌环丙沙星耐药率进行适合性检验,得预测值与实际值基本吻合(P<0.05)。结论:1991～2007年中国内地金黄色葡萄球菌的环丙沙星耐药性遵循lo-gistic模型发展。     

结肠癌发病的危险因素logistic回归分析

目的探讨结肠癌的发病危险因素。方法对89例结肠癌患者和89例非糖尿病患者进行1:1病例对照研究,采用多因素非条件logistic回归模式分析结肠癌的发病的危险因素。结果 10次食用肉食/周,常处于坐位,一级亲属有恶性肿瘤病史,差异有明显的统计学意义(P>0.05)。结论 10次食用肉食/周,常处于坐位,一级亲属有恶性肿瘤病史是结肠癌发病的危险因素。     

基于半参数回归模型的脑卒中发病率影响因素分析

目的:探讨气象因素对脑卒中发病率的影响,从而对脑卒中高危人群及时采取干预措施。方法:建立多元线性回归模型和半参数回归模型,通过残差分析检验各模型的拟合效果。结果:半参数回归模型的拟合效果显著优于多元线性回归模型。结论:气温、气压等气象要素与脑卒中发病率有较密切关系,大气的相对湿度与脑卒中发病显著相关。     

用SAS软件实现高维列联表资料的统计学分析:因变量为二值变量的多重logistic回归分析简

Logistic回归分析属于概率型回归分析,适用于因变量为定性变量的数据分析和建模,但对自变量的数目和性质没有特殊要求.因变量为二值变量的多重logistic回归分析适用于因变量编码为0或1（代表阳性或者阴性）的多重logistic回归分析.从整体上理解logistic回归分析,可根据操作过程依次总结为以下几个方面：自变量筛选、建立回归模型、进行假设检验（包括对回归系数的检验、整体模型检验以及模型拟合优度检验）.近年来,logistic回归分析在众多临床医学研究,尤其是在预测因子分析中得到了广泛应用,但存在一些问题,例如缺少对模型拟合优度检验以及后续通过验证集对其预测效能进行的二次检验.本文重点介绍如何正确实施多重logistic回归分析、其SAS实现及结果解读.     

用GAM程序拟合光滑样条非参数回归

回归分析模型包括参数回归、半参数回归和非参数回归.参数回归其回归形式已知,只是其中参数待定.参数回归的最大优点在于回归结果可以外延,但缺点也很明显,它的假定条件过于严格,在不少情况下回归模型经常会受到限制而不适用.非参数回归与参数回归恰好相反,其回归函数形式不定,可适用于任意分布的资料,拟合效果一般优于参数模型,但其结果外延困难,且操作复杂.由于非参数回归具有较强的适用性,且对模型的假定不严,因而其应用有着较为广阔的前景.鉴于国内医药卫生统计领域关于非参数回归的研究和应用较少.本文简要介绍了光滑样条非参数回归,并采用SAS 8.1中的GAM程序给出光滑样条非参数回归的拟合方法。     

基于 logistic回归模型的前列地尔注射液引起静脉炎危险因素分析

目的研究前列地尔注射液发生静脉炎相关的危险因素，为临床应用中减少静脉炎的发生提供参考。方法收集 2018年 1—12月河南省人民医院输注前列地尔注射液发生静脉炎的所有病历共 29份作为试验组，收集 364例同期输注前列地尔的无静脉炎的住院病人作为对照组，对致静脉炎的可能危险因素进行单因素分析及多因素非条件 logistic回归分析。结果单因素分析及多因素非条件二元 logistic回归分析结果均显示，病人过敏史、用药剂量、溶媒体积 3个因素在试验组与对照组之间的差异有统计学意义(P＜0.05)，是前列地尔注射液引起静脉炎的独立危险因素。结论临床使用前列地尔注射液时，应加强过敏史、用药剂量、溶媒体积等危险因素的用药管理，规范使用，减少不必要的静脉炎发生。     

某部新兵跖骨行军骨折风险因素的多元logistic回归分析

目的 探讨新兵战士跖骨行军骨折的风险因素,为部队建立新兵战士健康预警机制和防治提供依据.方法 采用多元logistic回归法对东部战区某部新兵战士身高、体重、训练时间、跖骨长度、跖骨宽度、跖骨密度、足弓高度、骨龄等风险因素进行逐层筛选分析.结果 跖骨宽度、跖骨密度、足弓高度、骨龄为跖骨行军骨折的消极因素.而训练时间、跖骨长度为跖骨行军骨折的积极因素.进一步的结果显示训练时间、跖骨长度、跖骨宽度、足弓高度可进入logistic回归模型,且回归模型对数据的拟合度较好(P=0.847),具有统计学意义(χ2=52.449,P<0.001).结论 多个因素参与行军骨折的发病过程,其风险因素应得到重视,以此建立有效的预防预警措施,减低跖骨行军骨折在新兵战士群体中的发病率.     

两种软件对血管外给药一二室模型 药动学参数的拟合效果

［摘要］目的比较两种软件对血管外给药一、二室模型药动学参数的拟合效果。方法采用DAS2.0软件药动学模块和SPSS12.0软件的非线性回归方法对一系列时间 血药浓度模拟数据，以及实测数据进行拟合。结果模拟结果显示SPSS12.0非线性回归方法拟合效果优于DAS2.0软件，但实测数据的拟合结果两者一致。结论SPSS12.0和DAS2.0软件的拟合效果均良好，可以作为血管外给药一、二室模型药动学参数的拟合方法。     

Conditional Logistic Regression With Survey Data

When data consist of clusters of potentially correlated observations then conditional logistic regression can be used to estimate the association between a binary outcome and covariates conditionally on the cluster effects. Surveys can use multistage sampling with potentially differential probabilities of sampling individuals from the same conditioning cluster (e.g., family). We show that conditional logistic regression of survey data using standard inflation (weighted) estimation (i.e., observations are weighted by the inverse of their inclusion probabilities) can result in biased estimators when the sample sizes of the observations sampled from the conditioning clusters are small. We propose using methods based on weighted pseudo-pairwise likelihoods that combine the conditional logistic likelihoods for all pairs of observations consisting of a positive and a negative outcome within a conditioning cluster and weights the pairwise likelihoods by the inverse of their joint inclusion probabilities within the cluster. Design-based variance estimators for regression coefficient estimators are provided. Limited simulations demonstrate that the proposed methods produce approximately unbiased regression coefficients and variance estimates, but can be considerably less efficient than maximum likelihood estimation when the sampling is uninformative. The proposed methods are illustrated with an analysis of data from the Hispanic Health and Nutrition Examination Survey.     

大学新生心理健康状况调查分析及影响因素的多因素累积比数Logistic回归分析

目的:了解2006级新生的心理健康状况,为制定针对性的能促进新生尽快适应大学生活的措施提供依据。方法:通过两阶段抽样获得样本,采用"大学生人格问卷"和自编的"大学新生适应能力影响因素调查问卷",对729名新生进行调查。通过单因素Logistic回归分析和多因素累积比数Logistic回归分析,筛选影响大学新生心理健康的可能因素。结果:729人中,Ⅰ类有124人(17.03%),Ⅱ类有234人(32.14%),Ⅲ类有370人(50.83%)。年龄、性别、月生活费用、家庭住址之的差别对新生心理的影响无差别;而是否独子、对学校、所学专业不同的满意度、不同专业间所造成的影响有意义。     

二分类、多分类Logistic回归模型SAS程序实现的探讨

目的:探讨在SAS中使用多个不同的过程分析Logistic回归模型。方法:通过具体实例说明相应的SAS程序并解释。结果:Logistic、catmod与genmod过程都可以拟合Logistic回归模型。结论:每个过程有其各自的特点,在实际处理数据时要能正确使用它们。     

Multinomial Logistic Regression Ensembles

This article proposes a method for multiclass classification problems using ensembles of multinomial logistic regression models. A multinomial logit model is used as a base classifier in ensembles from random partitions of predictors. The multinomial logit model can be applied to each mutually exclusive subset of the feature space without variable selection. By combining multiple models the proposed method can handle a huge database without a constraint needed for analyzing high-dimensional data, and the random partition can improve the prediction accuracy by reducing the correlation among base classifiers. The proposed method is implemented using R, and the performance including overall prediction accuracy, sensitivity, and specificity for each category is evaluated on two real data sets and simulation data sets. To investigate the quality of prediction in terms of sensitivity and specificity, the area under the receiver operating characteristic (ROC) curve (AUC) is also examined. The performance of the proposed model is compared to a single multinomial logit model and it shows a substantial improvement in overall prediction accuracy. The proposed method is also compared with other classification methods such as the random forest, support vector machines, and random multinomial logit model.     

Use of Alternating Logistic Regression in Studies of Drug-Use Clustering

This article describes the alternating logistic regression (ALR) method, and places this method in the context of other statistical approaches to the analysis of complex survey data, including the conditional form of logistic regression with matching on neighborhood characteristics. Unlike conditional logistic regression, the ALR method provides for an explicit estimation of the magnitude of clustering of drug use within neighborhoods and within subgroups of the neighborhood defined by male-female or age indicators, with and without covariate adjustments. The application of these ALR methods is illustrated with estimates for the magnitude of clustering of daily marijuana use and weekly marijuana use within neighborhoods of the United States, based on data from the National Household Survey on Drug Abuse samples from 1990 through 1996. [Translations are provided in the International Abstracts Section of this issue.]     

主成分Logistic回归在早期鉴别卵巢囊肿研究中的应用及其软件开发

目的:应用主成分L og istic回归分析方法对卵巢囊肿进行早期鉴别诊断,并对此实施软件开发以便于门诊辅助诊断,进而提高疾病鉴别诊断的准确性和效率。方法:首先对L og istic回归模型进行共线性诊断,然后筛选出对鉴别卵巢囊肿有统计学意义的检查指标并建立L og istic回归模型,利用该模型对卵巢囊肿进行早期鉴别诊断。程序开发选用delph i 7.0软件。结果:孕次、产次、流产次数间存在着中等程度的共线关系;除孕次、形态、内壁结构外,其余11项指标均纳入模型;回顾性判别符合率为87.86%,前瞻性差别符合率为85.14%,判别效果较好。结论:主成分L og istic回归实现了卵巢囊肿的早期鉴别诊断,开发的软件可以用于门诊辅助诊断。     

Covariate Adjustment for Logistic Regression Analysis of Binary Clinical Trial Data

In linear regression models, covariate-adjusted analysis is not expected to change the estimates of the treatment effect in the clinical trials with randomized treatment assignment but rather to increase the precision of the estimates. However, the covariate-adjusted treatment effect estimates are generally not equivalent to the unadjusted estimates in logistic regression analysis for binary clinical trial data. In this article, we report the results of a simulation study conducted to quantify the magnitude of difference between the estimands underlying the two estimators in logistic regression. The simulation results demonstrated that both unadjusted and adjusted analyses preserved Type I error at the nominal level. The covariate-adjusted analysis produced unbiased, larger treatment effect estimates, larger standard error, and increased power compared with the unadjusted analysis when the sample size was large. The unadjusted analysis resulted in biased estimates of treatment effect. Analysis results for five phase 3 diabetes trials of the same compound were consistent with the simulation findings. Therefore, covariate-adjusted analysis is recommended for evaluating binary outcomes in clinical data.     

Maximum Likelihood Estimation of Titer via a Power Family of Four-Parameter Logistic Model

For many laboratory assays, the readouts are presence or absence of a particular function, and the binary outcomes are correlated. The research interest is often focused on the estimation of titers, at which 50% positivity occurs. The classical approach by Reed and Muench (RM) assumes linear dose–response relationship around the potential titer, and uses only information from two points around the potential titer, which is inefficient in both precision and accuracy. While the model-based methods such as four-parameter logistic regression (4PL) use all the data, they do not consider the correlation among binary outcomes from same identities, which may lead to estimates with overstated precision. We propose estimating titers from two different anchors: independent responses from same identities or exchangeable responses from same identities. Marginal distributions of responses are linked to covariates of dilution factors by the 4PL model for independent responses and by a power family of the 4PL models for exchangeable responses. The maximum-likelihood procedure is used to get estimates of parameters and titers. The superiority of proposed methods over the classical approach is demonstrated both in simulation studies and in analysis of real data from hemagglutination assays.     

Covariate Imbalance and Adjustment for Logistic Regression Analysis of Clinical Trial Data

In logistic regression analysis for binary clinical trial data, adjusted treatment effect estimates are often not equivalent to unadjusted estimates in the presence of influential covariates. This article uses simulation to quantify the benefit of covariate adjustment in logistic regression. However, International Conference on Harmonization guidelines suggest that covariate adjustment be prespecified. Unplanned adjusted analyses should be considered secondary. Results suggest that if adjustment is not possible or unplanned in a logistic setting, balance in continuous covariates can alleviate some (but never all) of the shortcomings of unadjusted analyses. The case of log binomial regression is also explored.     

红花注射液致过敏性休克危险因素的Logistic回归分析

目的：探讨红花注射液引起过敏性休克的危险因素,为临床安全用药提供参考。方法：通过检索文献,采用病例对照研究的方法,对红花注射液引起的过敏性休克患者和对照组共78例进行基本信息和用药情况的调查;应用Logistic回归模型进行单因素及多因素分析。结果：单因素分析具有显著意义（P< 0.05）的影响因素为年龄、联合用药、基础疾病、过敏发生时间;将其引入Logistic回归方程,经分析认为均有统计学意义（P< 0.05）,其OR值分别为4.144、14.908、0.181、4.735。结论：年龄、联合用药、基础疾病、过敏发生时间为红花注射液引起过敏性休克的危险因素,临床要引起重视,减少或避免相应危害。