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Analysis of ordinal outcomes with longitudinal covariates subject to missingness

机译:带有缺失的纵向协变量的序数结果分析

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摘要

We propose a mixture model for data with an ordinal outcome and a longitudinal covariate that is subject to missingness. Data from a tailored telephone delivered, smoking cessation intervention for construction laborers are used to illustrate the method, which considers as an outcome a categorical measure of smoking cessation, and evaluates the effectiveness of the motivational telephone interviews on this outcome. We propose two model structures for the longitudinal covariate, for the case when the missing data are missing at random, and when the missing data mechanism is non-ignorable. A generalized EM algorithm is used to obtain maximum likelihood estimates.
机译:我们为具有顺序结果和纵向协变量的数据提出混合模型,该协变量可能会丢失。使用针对建筑工人的量身定制的电话交付的戒烟干预措施中的数据来说明该方法,该方法将吸烟作为一种分类措施,并以此结果评估激励性电话采访的有效性。对于缺失的数据随机缺失以及缺失数据机制不可忽略的情况,我们为纵向协变量提出了两种模型结构。通用EM算法用于获得最大似然估计。

著录项

  • 来源
    《Journal of applied statistics》 |2014年第6期|1040-1052|共13页
  • 作者单位

    Division of Public Health Sciences, Department of Surgery, Washington University in St. Louis School of Medicine, St. Louis, MO, USA;

    Department of Biostatistics, University of Michigan School of Public Health, Anne Arbor, MI, USA;

    Center for Statistical Analysis & Research, New England Research Institute, Watertown, MA, USA;

    Center for Community Based Research, Dana Farber Cancer Institute, Department of Society, Human Development, and Health, Harvard School of Public Health, Boston, MA, USA;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    ordinal outcomes; longitudinal covariates; missingness;

    机译:顺序结果;纵向协变量失踪;

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