Analysis of Multivariate Longitudinal Outcomes With Nonignorable Dropouts and Missing Covariates
含不可忽略退出和缺失协变量的多元纵向结果分析AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要明确分析治疗机构的全国面板调查，核心问题包括多元纵向结果、时变协变量及样本退出。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
关注这篇论文的作者
含不可忽略退出和缺失协变量的多元纵向结果分析
- 研究问题
- 在治疗机构的纵向调查中，如何同时处理多项结果、可能不可忽略的退出，以及退出时缺失的时变协变量？
- 方法与思路
- 作者用潜变量与线性混合模型连接结果和协变量，以选择模型刻画退出、转移模型处理协变量，并采用 EM 估计。
- 材料陈述的贡献
- 摘要称，该联合方法用于美沙酮治疗机构数据，分析显示治疗实践有所改善，并可帮助识别需要改善的机构类型。
- 阅读关注点
- 应核对潜变量测量假设、退出对潜变量的依赖、缺失协变量模型，以及不可忽略缺失的识别和敏感性分析。
- 阅读边界
- 依据版本未注明的 OpenAlex 摘要；摘要未说明识别条件及敏感性结果，治疗实践的纵向改善也不能直接解释为因果效应。
原文摘要
This article analyzes changes in treatment practices in outpatient methadone treatment units from a national panel study. The analysis of this dataset is challenging due to several difficulties, including multiple longitudinal outcomes, nonignorable nonresponses, and missing covariates. Specifically, the data included several variables that measure the effectiveness of methadone treatment practices for each unit. A substantial percentage of units (33%%) did not respond during the follow-up. These dropout units tended to be units with less effective treatment practices; the dropout mechanism thus may be nonignorable. Finally, the time-varying covariates for the units that dropped out were missing at the time of dropout. A valid analysis hence needs to address these three issues simultaneously. Our approach assumes that the observed outcomes measure a latent variable (e.g., treatment practice effectiveness) with error. We model the relationship between this latent variable and covariates using a linear mixed model. To account for nonignorable dropouts, we apply a selection model in which the dropout probability depends on the latent variable. Finally, we accommodate missing time-varying covariates by modeling them using a transition model. In view of multidimensional integration in full-likelihood estimation, we develop the EM algorithm to estimate the model parameters. We apply the proposed approach to the methadone treatment practices data. Our results show that methadone treatment practices have improved in the last decade. Our results are also useful for identifying the types of methadone treatment units that need improvement.