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Modeling early alcohol initiation: A comparison of linear regression, logistic regression, and discrete time hazard models.

机译:早期酒精引发建模:线性回归,逻辑回归和离散时间危害模型的比较。

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摘要

Scope and Method of Study: In social science research there is often a need to study the occurrence of a rare event whose distribution is not normal and whose data structure is nested. Common statistical methods for these questions require either the violation of important statistical assumptions or the mishandling of missing data. For data that involve whether an event occurs and when it occurs, the most appropriate statistical model are discrete-time hazard models. However, until recently a method that uses discrete-time hazard models and appropriately adjusts the standard errors to account for the nested structure of the data did not exist. The present study develops three models that combine discrete-time hazard models and hierarchical linear modeling, to model Age of First Use of alcohol, and compares and contrasts these models with more commonly used multiple regression and logistic regression models. To illustrate the advantages of this method, the study evaluates the effects of several common covariates of alcohol use, such as Age of First Opportunity (AFO) of using alcohol, Family Attention (FA), Externalizing Behavior (EXT), Socioeconomic Status (SES), and Gender in a sample of 1785 youth from Caracas, Venezuela.;Findings and Conclusions: Age of first opportunity of using alcohol appears to be the most influential variable in the models. The highest hazard rate of alcohol initiation was found at the first year of opportunity to use alcohol. The results obtained in this study varied across models depending on whether or not AFO was included in models as a covariate. When models did not control for AFO all other independent variables of this study become significant predictors of alcohol initiation in all models except for the logistic regression model where controlling for AFO did not make statistically significant differences in predicting alcohol use. Even though all models considered in the present study have their own advantages, hazard models are seen as the most appropriate in modeling age of first alcohol use. The main advantages of hazard models is in their ability to handle a particular kind of missing data called right censoring, such as youth who report delaying their initiation of alcohol use for all years covered in a given study. In investigating alcohol initiation, only about 18% reported no use of alcohol in this study, but when investigating illicit drugs, many more participants will be in a no-user group. For modeling early ages of drug initiation or any other event occurrence, when a vast majority of participants have not yet experienced it, hazard models should be used.
机译:研究范围和方法:在社会科学研究中,经常需要研究罕见事件的发生,该事件的分布不正常且数据结构嵌套。这些问题的常用统计方法需要违反重要的统计假设或对丢失的数据进行错误的处理。对于涉及事件是否发生以及何时发生的数据,最合适的统计模型是离散时间危害模型。但是,直到最近,还没有使用离散时间危害模型并适当调整标准误差以解决数据嵌套结构的方法。本研究开发了三个模型,这些模型结合了离散时间危害模型和分层线性模型,以对首次使用酒精的年龄进行建模,并将这些模型与更常用的多元回归和逻辑回归模型进行比较和对比。为了说明这种方法的优势,该研究评估了几种常见的酒精使用协变量的影响,例如使用酒精的首次机会年龄(AFO),家庭注意力(FA),外在行为(EXT),社会经济地位(SES) ),以及来自委内瑞拉加拉加斯的1785名年轻人的性别样本;发现与结论:首次使用酒精饮料的年龄似乎是模型中最具影响力的变量。在使用酒精的第一年发现最高的酒精引发危害率。这项研究中获得的结果在各个模型中有所不同,具体取决于是否将AFO作为协变量包含在模型中。当模型不能控制AFO时,本研究的所有其他自变量均成为所有模型中酒精起始的重要预测指标,但逻辑回归模型除外,在逻辑回归模型中,控制AFO在预测酒精使用方面没有统计学上的显着差异。即使本研究中考虑的所有模型都有其自身的优势,但危害模型仍被认为是最适合首次饮酒年龄的模型。危害模型的主要优点在于它们能够处理称为正确审查的一种特定类型的缺失数据,例如,青年报告说,在给定研究涵盖的所有年份中,他们都开始推迟使用酒精。在调查酒精引发问题时,只有约18%的人在本研究中报告未使用酒精,但是在调查违禁药物时,将有更多的参与者属于非使用者群体。为了模拟药物启动的早期年龄或任何其他事件的发生,当绝大多数参与者尚未经历这种情况时,应使用危害模型。

著录项

  • 作者

    Danelia, Ketevan.;

  • 作者单位

    Oklahoma State University.;

  • 授予单位 Oklahoma State University.;
  • 学科 Sociology Theory and Methods.;Health Sciences Public Health.
  • 学位 Ph.D.
  • 年度 2011
  • 页码 136 p.
  • 总页数 136
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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