A Marginal Model for Analyzing Discrete Outcomes From Longitudinal Surveys With Outcomes Subject to Multiple-Cause Nonresponse
多原因无应答下纵向调查离散结局分析的边缘模型AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要明确分析纵向调查的重复类别结局与状态转移，直接对应面板数据；未研究离散选择行为或因果识别。
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多原因无应答下纵向调查离散结局分析的边缘模型
- 研究问题
- 纵向调查中的类别结局存在多种原因造成的无应答时，如何同时处理状态转移、调查设计和缺失导致的估计问题？
- 方法与思路
- 作者结合多元链接函数构建 Markov 边缘模型，以多项 logit 建模无应答原因，并使用兼顾调查与缺失权重的估计方程。
- 材料陈述的贡献
- 摘要称，论文发展 Taylor 展开和 jackknife 方差估计并考察敏感性，老龄调查应用提示体力活动可能预测功能限制转移。
- 阅读关注点
- 应核对无应答仅依赖已观测响应和指定协变量的假设、两类权重的构造，以及方差估计如何反映调查设计。
- 阅读边界
- 仅据版本未注明的 OpenAlex 摘要，无法验证无应答模型或量化敏感性；体力活动的预测关联不能解释为因果效应。
原文摘要
Techniques for analyzing categorical outcomes obtained from longitudinal survey samples, with outcomes subject to multiple-cause nonresponse, are developed within the framework of weighted generalized estimating equations. Development of these techniques was motivated by disability data obtained from the Longitudinal Study of Aging (LSOA), a longitudinal survey sample containing missing follow-up for many elderly participants. We posit a model that combines different multivariate link functions to permit fitting Markov models to an outcome with categories represented by a mixture of ordinal success states and multiple failure states. Extending the missing data approach of Robins, Rotnitzky, and Zhao to longitudinal survey sample settings, we use multiple-logit models to model the probability of multiple reasons for missing success or failure outcomes. Given the assumption that the probability of nonresponse depends only on observed responses and covariates specified in the missing data model, weighted estimating equations that permit the incorporation of both survey and missing data weights are used in estimation of parameters specified in the Markov models. Taylor series and jackknife variance estimators are developed for parameters estimated from these models and are presented within the context of adjusting for survey considerations and multiple-cause nonresponse. The sensitivity of marginal model results to different features of the survey design and missing data considerations are explored. Analyses of the LSOA suggest that participation in physical activity may be an important predictor of transitions in functional limitations among older adults.