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1.
本文目的是比较不同分析策略对复杂抽样调查设计多值有序资料一水平多重logistic回归分析结果的异同。通过实例分析,利用四种不同的分析策略(将复杂抽样视为单纯随机抽样,考虑抽样设计不考虑抽样权重,考虑抽样权重不考虑抽样设计,同时考虑抽样设计和抽样权重)对复杂抽样设计多值有序资料进行建模。在四种不同分析策略的累积logistic回归模型拟合的结果中,自变量的偏回归系数、标准误差及P值均有所不同。在对复杂抽样调查设计的多值有序资料回归建模时,将抽样设计和抽样权重纳入统计分析,会得到更准确、更稳健的分析结果。  相似文献   

2.
本文目的是介绍复杂抽样调查设计多值名义资料一水平多重logistic回归模型构建,并探讨不同策略之间的差异。采用SAS中的LOGISTIC过程和SURVEYLOGISTIC过程,分别按照是否考虑抽样设计与是否考虑抽样权重共4种分析策略对数据构建广义logistic回归模型,并比较结果。不同分析策略所得结果显示,不仅参数估计值、回归系数标准误、OR值及其置信区间的估计值有所差别,而且对纳入模型的解释变量也有影响。因此,在对复杂抽样调查设计多值名义资料构建广义logistics回归模型时,既要考虑抽样设计,又要兼顾抽样权重,否则即使样本量足够大,也会导致错误的推断结论。  相似文献   

3.
复杂抽样是在抽样过程中采用除一阶段单纯随机抽样外,其他抽样方法或其组合的抽样方案。本文对复杂抽样资料的特点、基于复杂调查资料进行差异性分析、多重回归分析以及进行生存资料多重回归分析的要点进行宏观概述。为科研工作者进行复杂抽样资料的分析提供参考和借鉴。  相似文献   

4.
本文目的是介绍因果中介效应分析的理论基础以及结合一个实例采用SAS实现因果中介效应分析的具体方法。因果中介效应分析的理论基础包括基本概念以及定义因果中介效应的反事实框架。实例是关于父母提供的鼓励性环境是否会影响儿童的认知发展,分别采用传统的多重线性回归分析、不考虑协变量和考虑协变量的因果中介效应分析,通过比较3种分析方法所得到的结果,得出如下结论:①当资料中存在中介变量时,不适合采用传统的多重线性回归分析取代因果中介效应分析;②当资料中存在协变量时,不适合在忽视协变量的条件下进行因果中介效应分析。  相似文献   

5.
本文目的是介绍非配对设计多值名义资料一水平多重logistic回归分析的基本原理、建模策略及注意事项。结合实例,应用SAS 9.4构建未经变量筛选和经变量筛选的多值名义资料多重logistic回归模型。通过回归分析的计算结果可知,同一变量的回归系数在不同logit函数中存在代数关系。多值名义多重logistic回归分析可以用来处理结果变量为多值名义变量的回归建模问题,并可以结合SAS实现对自变量的筛选,以获得简洁的回归模型。  相似文献   

6.
本文目的是介绍非配对设计多值名义资料多水平多重logistic回归模型的构建与求解方法。首先介绍了有关的基本概念,涉及“多值名义结果变量”“分层或多水平数据结构”和“扩展的多重logistic回归模型”;其次,呈现了一个具有二水平结构的横断面调查资料,该资料涉及多个影响因素和一个多值有序的结果变量(在本文中,将其视为多值名义结果变量);最后,借助SAS中的两个过程(即GLIMMIX和NLMIXED)对给定的资料进行统计分析,即构建和求解“非配对设计多值名义资料多水平多重logistic回归模型”,并对相关结果进行比较和解释。  相似文献   

7.
传统的统计分析方法在进行差异性分析、线性与广义线性回归分析时,基本上都是基于样本来自无限总体、完全随机抽样的基础上估计抽样误差。而调查数据往往来自于分层、整群、多阶段或不等概率等复杂随机抽样方法,此时若采用前述提及的经典统计分析方法,则不能准确估计抽样误差。本文通过具体实例,介绍如何应用SAS软件中的SURVEYMEANS过程,更好地实现对通过各种抽样方法获得的数据进行统计描述和简单的统计分析,以便达到准确估计抽样误差、对总体参数描述和估计的目的。  相似文献   

8.
本文的目的是介绍m∶n配对设计二值资料一水平多重Logistic回归分析方法。首先,介绍了需要了解的基本概念;其次,介绍了构建此类回归模型的基本原理;最后,通过一个实例介绍了使用SAS实现计算的全过程。在此过程中,获得了如下四点启示:其一,有必要确保所获得的科研资料是值得分析的;其二,有必要基于定量自变量产生派生变量;其三,有必要同时采用"逐步法""前进法"和"后退法"筛选自变量;其四,有必要采用多种方法评价不同回归模型对资料的拟合优度。  相似文献   

9.
本文目的是介绍主成分回归分析的概念、作用以及用软件实现计算的方法。先对自变量进行主成分分析,然后将主成分变量视为新的自变量,再进行多重线性回归分析。通过不引入和引入派生变量以及采取不同的策略筛选自变量,可以获得多个合格的多重线性回归模型。在回归模型自由度接近相等时,基于残差方差最小、复相关系数最大为评价指标,从众多回归模型中优中选优。得出的经验为:应慎用主成分回归分析。  相似文献   

10.
本文目的是介绍基于贝叶斯统计思想实现多重线性回归分析的方法。多重线性回归分析时,单纯基于贝叶斯理论导出的公式来估计回归模型中参数的做法并不常见。最常见的做法是基于马尔科夫链蒙特卡罗(MCMC)方法来实现多重回归分析,即把蒙特卡罗方法、贝叶斯统计思想和马尔科夫链等内容有机结合起来,共同完成多重回归分析。在资料基本满足经典统计思想建模的前提条件时,基于贝叶斯统计思想构建多重线性回归模型,其效果等价于基于经典统计思想构建的多重线性回归模型。  相似文献   

11.
OBJECTIVE: To examine the association between pathological gambling (PG) and attempted suicide in a nationally representative sample of Canadians. METHODS: Data came from the Canadian Community Health Survey, Cycle 1.2, conducted in 2002, in which 36 984 subjects, aged 15 years or older, were interviewed. Logistic regression was performed with attempted suicide (in the past year) as the dependent variable. The independent variables were PG, major depression, alcohol dependence, drug dependence, and mental health care (in the past year), as well as a range of sociodemographic variables. Survey weights and bootstrap methods were used to account for the complex survey design. RESULTS: In the final logistic regression model, which included terms for PG, major depression, alcohol dependence, and mental health care, as well as age, sex, education, and income, the odds ratio for PG and attempted suicide was 3.43 (95% confidence interval, 1.37 to 8.60). CONCLUSIONS: PG (in the past year) and attempted suicide (in the past year) are associated in a nationally representative sample of Canadians. However, it is not possible to say from these data whether this represents a causal relation.  相似文献   

12.
本文目的是介绍多值有序资料多水平多重logistic回归分析方法。此法是在层次结构数据的基础上,构建多值有序因变量随一组自变量变化而变化的回归模型。具体的做法如下:①先介绍有关的基本概念;②呈现待分析的数据结构;③扼要介绍回归模型的构建与求解;④详细介绍如何使用SAS的GLIMMIX和NLMIXED两个过程来拟合此回归模型,并对相关结果进行解释和比较;⑤讨论多水平结构数据下拟合累积logistic回归模型时需注意的问题。  相似文献   

13.
本文目的是介绍非配对设计多值有序资料一水平多重logistic回归模型的构建与求解方法。本文详细介绍了构建累积logistic回归模型的原理和具体方法,并结合实例介绍如何使用SAS软件中的LOGISTIC过程来拟合此回归模型,并对逐步回归法的输出结果进行了解释;其次讨论了有关构建累积logistic回归模型的过程中自变量筛选、模型评价以及拟合模型时需注意的问题。  相似文献   

14.
非配对设计二值资料一水平多重Logistic回归分析   总被引:1,自引:0,他引:1       下载免费PDF全文
本文的目的是介绍非配对设计二值资料一水平多重Logistic回归模型的构建与求解方法。基于SAS软件分别对以列联表和数据库形式呈现的定性资料进行全面分析,并得出了4个对提高模型拟合优度很有价值的结论:第一,若资料以列联表形式呈现,应拟合"加权"Logistic回归模型;第二,若资料中包含定量自变量,不适合将其定性化;第三,若资料中包含定量自变量,应依据定量自变量和二值自变量产生出派生自变量;第四,若资料中有定性自变量时,必须将多值名义或有序自变量进行哑变量变换,不需要依据二值自变量产生出派生自变量。  相似文献   

15.
The logistic regression analysis of psychiatric data   总被引:6,自引:0,他引:6  
Logistic regression is presented as the statistical method of choice for analyzing the effects of independent variables on a binary dependent variable in terms of the probability of being in one of its two categories vs the other. The method, which must be applied by computer, is illustrated on data from the DSM-III field trials. The dependent variable is treatment with behaviourally-oriented psychotherapy vs treatment with psychoanalytically-oriented psychotherapy, and the independent variables are several patient and clinician characteristics. Like ordinary multiple regression, the method is shown capable of analyzing categorical as well as continuous independent variables. Unlike ordinary multiple regression when applied to binary data, logistic regression analysis necessarily yields estimated probabilities that lie between 0 and 1. The measure of association derived from logistic regression analysis, the odds ratio, is defined. Methods for making inferences about it are presented and illustrated.  相似文献   

16.
OBJECTIVE: Herpes simplex virus encephalitis (HSVE) is associated with significant morbidity and mortality, even with appropriate antiviral therapy. In the present investigation, the first to assess efficacy of corticosteroid treatment with aciclovir therapy in HSVE, multiple logistic regression analysis was performed of predictors of outcome in adult patients with HSVE. METHODS: A non-randomised retrospective study of 45 patients with HSVE treated with aciclovir was conducted. The patients were divided into poor and good groups based on outcome at three months after completion of aciclovir treatment. The variables evaluated were: clinical variables (sex, age, days after onset at initiation of aciclovir, Glasgow Coma Scale (GCS) at initiation of aciclovir, initial and maximum values for the cell numbers and protein concentration in the cerebrospinal fluid, and corticosteroid administration); neuroradiological variables (detection of lesions by initial cranial computed tomography and by initial magnetic resonance imaging); and one neurophysiological variable (detection of periodic lateralised epileptiform discharges on the initial electroencephalogram). Single variable logistic regression analysis was performed followed by multiple logistic regression analysis. The best set of predictors for the outcome of HSVE was estimated by stepwise logistic regression analysis. RESULTS: A poor outcome was evident with older age, lower GCS score at initiation of aciclovir, and no administration of corticosteroid. Patient age, GCS at initiation of aciclovir, and corticosteroid administration were found to be significant independent predictors of outcome on multiple logistic regression analysis, and these three variables also formed the best set of predictors (R(2) = 0.594, p<0.0001). CONCLUSION: Combination therapy using both aciclovir and corticosteroid represents one of the predictors of outcome in HSVE.  相似文献   

17.
Cigarette smoking has been associated with an increased risk of suicide. Patients with psychosis are more likely to smoke cigarettes and are also at an increased risk of suicide. The aim of this study was to compare risk for suicidal behavior among patients with psychosis who were current smokers, previous smokers and nonsmokers. We studied 1812 of the 1825 participants who took part in the Australian Survey of High Impact Psychosis (SHIP) for whom smoking data was available. We identified predictors for lifetime suicide attempts using univariate logistic regression analysis. These variables were retained for the multiple logistic regression models if they were a significant predictor of lifetime suicide attempts. A series of multiple logistic regressions were then conducted to predict lifetime suicide attempts using current smoking status and lifetime smoking status as independent variables, respectively, while controlling for the retained predictor variables. Current smoking and lifetime smoking were statistically significant predictors of lifetime suicide attempts. However adding the covariates to a logistic regression model reduced this association to non-significance. The strongest predictors were self-harm in the past 12 months, the presence of lifetime depressive symptoms and a diagnosis of psychotic depression. Identification of suicide risk factors is essential for successful suicide prevention. While previous research highlights the importance of cigarette smoking as an important risk factor for suicidal behaviors including in patients with psychosis, these results must be interpreted within the context of methodological issues.  相似文献   

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