Polytomous regression did not outperform dichotomous logistic regression in diagnosing serious bacterial infections in febrile children

OBJECTIVE: To compare polytomous and dichotomous logistic regression analyses in diagnosing serious bacterial infections (SBIs) in children with fever without apparent source (FWS). STUDY DESIGN AND SETTING: We analyzed data of 595 children aged 1-36 months, who attended the emergency department with fever without source. Outcome categories were SBI, subdivided in pneumonia and other-SBI (OSBI), and non-SBI. Potential predictors were selected based on previous studies and literature. Four models were developed: a polytomous model, estimating probabilities for three diagnostic categories simultaneously; two sequential dichotomous models, which differed in variable selection, discriminating SBI and non-SBI in step 1, and pneumonia and OSBI in step 2; and model 4, where each outcome category was opposed to the other two. The models were compared with respect to the area under the receiver-operating characteristic curve (AUC) for each of the three outcome categories and to the variable selection. RESULTS: Small differences were found in the variables that were selected in the polytomous and dichotomous models. The AUCs of the three outcome categories were similar for each modeling strategy. CONCLUSION: A polytomous logistic regression analysis did not outperform sequential and single application of dichotomous logistic regression analyses in diagnosing SBIs in children with FWS.     

A logistic regression model applied to Chagas' disease

The focus of this paper is the application of statistical models to the study of socioeconomic conditioning factors in perinatal Chagas' disease conducted in Rosario, Argentina. A case (154) and control (158) design was applied to investigate socioeconomic and cultural differences in pregnant women in Hospital Roque Sáenz Pe?a as to their infection status. Logistic regression models were used to evaluate the importance of antecedents linked to the infection and socioeconomic and cultural factors for infection status. For pregnant women, the importance of antecedents linked to the infection was confirmed and the women's level of schooling stood out as the predominant socioeconomic condition associated with infection. Log-linear models were used to explore the associations between certain explanatory variables. This approach pointed up the most relevant associations between such factors and Chagas' disease and provided a better understanding of the framework of relationships among them.     

A nomogram was developed to enhance the use of multinomial logistic regression modeling in diagnostic research

ObjectivesWe developed a nomogram to facilitate the interpretation and presentation of results from multinomial logistic regression models.Study Design and SettingWe analyzed data from 376 frail elderly with complaints of dyspnea. Potential underlying disease categories were heart failure (HF), chronic obstructive pulmonary disease (COPD), the combination of both (HF and COPD), and any other outcome (other). A nomogram for multinomial model was developed to depict the relative importance of each predictor and to calculate the probability for each disease category for a given patient. Additionally, model performance of the multinomial regression model was assessed.ResultsPrevalence of HF and COPD was 14% (n = 54), HF 24% (n = 90), COPD 20% (n = 75), and Other 42% (n = 157). The relative importance of the individual predictors varied across these disease categories or was even reversed. The pairwise C statistics ranged from 0.75 (between HF and Other) to 0.96 (between HF and COPD and Other). The nomogram can be used to rank the disease categories from most to least likely within each patient or to calculate the predicted probabilities.ConclusionsOur new nomogram is a useful tool to present and understand the results of a multinomial regression model and could enhance the applicability of such models in daily practice.     

Estimating adjusted NNT measures in logistic regression analysis

The number needed to treat (NNT) is a popular measure to describe the absolute effect of a new treatment compared with a standard treatment or placebo in clinical trials with binary outcome. For use of NNT measures in epidemiology to compare exposed and unexposed subjects, the terms 'number needed to be exposed' (NNE) and 'exposure impact number' (EIN) have been proposed. Additionally, in the framework of logistic regression a method was derived to perform point and interval estimation of NNT measures with adjustment for confounding by using the adjusted odds ratio (OR approach). In this paper, a new method is proposed which is based upon the average risk difference over the observed confounder values (ARD approach). A decision has to be made, whether the effect of allocating an exposure to unexposed persons or the effect of removing an exposure from exposed persons should be described. We use the term NNE for the first and the term EIN for the second situation. NNE is the average number of unexposed persons needed to be exposed to observe one extra case; EIN is the average number of exposed persons among one case can be attributed to the exposure. By means of simulations it is shown that the ARD approach is better than the OR approach in terms of bias and coverage probability, especially if the confounder distribution is wide. The proposed method is illustrated by application to data of a cohort study investigating the effect of smoking on coronary heart disease.     

Comparison of logistic regression and neural network analysis applied to predicting living setting after hip fracture

PURPOSE: Describe and compare the characteristics of artificial neural networks and logistic regression to develop prediction models in epidemiological research. METHODS: The sample included 3708 persons with hip fracture from 46 different states included in the Uniform Data System for Medical Rehabilitation. Mean age was 75.5 years (sd=14.2), 73.7% of patients were female, and 82% were non-Hispanic white. Average length of stay was 17.0 days (sd=10.6). The primary outcome measure was living setting (at home vs. not at home) at 80 to 180 days after discharge. RESULTS: Statistically significant variables (p <.05) in the logistic model included follow-up therapy, sphincter control, self-care ability, marital status, age, and length of stay. Areas under the receiver operating characteristic curves were 0.67 for logistic regression and 0.73 for neural network analysis. Calibration curves indicated a slightly better fit for the neural network model. CONCLUSIONS: Follow-up therapy and independent bowel and/or bladder function were strong predictors of living at home up to 6 months after hospitalization for hip fracture. No practical differences were found between the predictive ability of logistic regression and neural network analysis in this sample.     

Intermediate and advanced topics in multilevel logistic regression analysis

Multilevel data occur frequently in health services, population and public health, and epidemiologic research. In such research, binary outcomes are common. Multilevel logistic regression models allow one to account for the clustering of subjects within clusters of higher‐level units when estimating the effect of subject and cluster characteristics on subject outcomes. A search of the PubMed database demonstrated that the use of multilevel or hierarchical regression models is increasing rapidly. However, our impression is that many analysts simply use multilevel regression models to account for the nuisance of within‐cluster homogeneity that is induced by clustering. In this article, we describe a suite of analyses that can complement the fitting of multilevel logistic regression models. These ancillary analyses permit analysts to estimate the marginal or population‐average effect of covariates measured at the subject and cluster level, in contrast to the within‐cluster or cluster‐specific effects arising from the original multilevel logistic regression model. We describe the interval odds ratio and the proportion of opposed odds ratios, which are summary measures of effect for cluster‐level covariates. We describe the variance partition coefficient and the median odds ratio which are measures of components of variance and heterogeneity in outcomes. These measures allow one to quantify the magnitude of the general contextual effect. We describe an R² measure that allows analysts to quantify the proportion of variation explained by different multilevel logistic regression models. We illustrate the application and interpretation of these measures by analyzing mortality in patients hospitalized with a diagnosis of acute myocardial infarction. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.     

Conditional logistic regression analysis of coronary heart disease

A case-control study on 215 pairs was carried out in Jinan. Conditional Logistic regression analysis showed that the following five factors were associated with coronary heart disease, i.e., hypertension, hypercholesterolemia and heavy smoking, serum copper and HDL-C/TC. The former three were risk factors, and the latter two were protective factors. There is remarkable dose-response relation between heavy smoking and coronary heart disease. The data analysis show that these five factors are contributory to coronary heart disease with synergism.     

A new logistic regression approach for the evaluation of diagnostic test results.

The value of a dichotomous diagnostic test is often described in terms of sensitivity, specificity, and likelihood ratios (LRs). Although it is known that these test characteristics vary between subgroups of patients, they are generally interpreted, on average, without considering information on patient characteristics, such as clinical signs and symptoms, or on previous test results. This article presents a reformulation of the logistic regression model that allows to calculate the LRs of diagnostic test results conditional on these covariates. The proposed method starts with estimating logistic regression models for the prior and posterior odds of disease. The regression model for the prior odds is based on patient characteristics, whereas the regression model for the posterior odds also includes the diagnostic test of interest. Following the Bayes theorem, the authors demontsrate that the regression model for the LR can be derived from taking the differences between the regression coefficients of the 2 models. In a clinical example, they demonstrate that the LRs of positive and negative test results and the sensitivity and specificity of the diagnostic test varied considerably between patients with different risk profiles, even when a constant odds ratio was assumed. The proposed logistic regression approach proves an efficient method to determine the performance of tests at the level of the individual patient risk profile and to examine the effect of patient characteristics on diagnostic test characteristics.     

女大学生就业危机感的多元logistic回归分析

目的探讨在校女大学生的就业危机感现状及其影响因素，为针对性教育管理提供依据。方法采用自行设计问卷结合SCL-90自评症状量表对福建省两所大学的在校女大学生进行抽样调查，以单因素和多因素非条件logistic回归进行资料分析。结果847名女大学生就业危机感的检出率为39．1％，不同学校、年级、专业间具有统计学意义，专业、患慢性病、学习困难、父亲无业、下岗或务农等7个因素为其主要影响因素。结论高校应积极筹建大学生就业心理危机干预的预警机制，特别要对在就业中处于弱势的女大学生加强心理监测和积极干预，帮助她们增强职业意识和信心，合理规划人生，健康成长。     

Bias reduction in conditional logistic regression

We employ a general bias preventive approach developed by Firth (Biometrika 1993; 80:27-38) to reduce the bias of an estimator of the log-odds ratio parameter in a matched case-control study by solving a modified score equation. We also propose a method to calculate the standard error of the resultant estimator. A closed-form expression for the estimator of the log-odds ratio parameter is derived in the case of a dichotomous exposure variable. Finite sample properties of the estimator are investigated via a simulation study. Finally, we apply the method to analyze a matched case-control data from a low birthweight study.     

脑梗塞遗传因素Logistic回归及通径分析

目的：探讨遗传因素在脑梗塞发病中的作用。方法：对张家口医学院第一、第二附属医院神经内科住院的脑梗塞新发病例共112例，以同期住院的非心脑血管病人为对照，采用1：1配对设计，并进行问卷调查了解家系亲属中脑梗塞的发病情况，进行单因素及多因素的条件Logistic回归分析，通径分析。结果：父亲患病年龄、母亲患病年龄、同胞患病及二级亲属患病与脑梗塞的发病呈显正相关，但通过通径分析综合研究认为：遗传因素只是脑梗塞发病危险因素中的一个方面，其解释力仅为26％，故认为环境因素，特别是个体本身所具备的某些特征对脑梗塞发病的作用更大。     

影响巨大儿发生的多因素logistic回归分析

目的研究影响巨大儿发生的危险因素。方法回顾性队列研究法对3 869例产妇进行调查,进行logistic回归分析。结果对巨大儿产生影响的因素有8项,对总判断力(correct class%)为91.8%。依OR值大小,各因素影响程度从强到弱依次为:新生儿性别、分娩方式、孕周、宫高、孕次、孕妇身高、腹围和孕早期BMI。结论①男婴发生巨大儿的可能性大于女婴,孕妇的孕周越大、宫高越高、孕次越多、身高越高、腹围越大、孕早期BMI越大,发生巨大儿的可能性越大。②巨大儿剖腹产的可能性大。     

低出生体重危险因素Logistic回归分析

目的探讨不同原因低出生体重儿发生的危险因素。方法对90名低出生体重儿（早产低出生体重儿42名，足月低出生体重儿48名）和250名正常对照组婴儿进行相关因素调查，采用多项式Logistic回归分析法分析低出生体重发生的危险因素。结果母亲有急性羊膜炎及阴道流夜pH值使试纸变色是早产低出生体重儿发生的危险因素（OR=1．584，7．727，P〈0．01）；与足月低出生体重儿发生率呈正相关的因素有：宫颈分泌物PCR解脲支原体抗原（UUAg）阳性和发生胎儿宫内窘迫（OR=3．988，0．580，P〈0．001，0．05）；与2者均有关的因素是：血清解脲支原体抗体（UUAb）IgM阳性、血清解脲支原体抗体（UUAb）IgG阳性、胎盘解脲支原体（UU）培养阳性和孕周等。结论低出生体重发生是多种因素联合作用造成的，尤其是母亲受到感染时，应采取综合措施降低低出生体重儿的发生率。