单变量分类纵向数据插补方法的比较

Comparison of imputation methods for univariate categorical longitudinal data.

作者信息

Emery Kevin, Studer Matthias, Berchtold André

机构信息

Swiss Centre of Expertise in Life Course Research LIVES, Geneva, Switzerland.

Institute of Demographics and Socioeconomics, University of Geneva, Geneva, Switzerland.

出版信息

Qual Quant. 2025;59(2):1767-1791. doi: 10.1007/s11135-024-02028-z. Epub 2024 Dec 26.

DOI:10.1007/s11135-024-02028-z

PMID:40433560

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12104099/

Abstract

UNLABELLED

The life course paradigm emphasizes the need to study not only the situation at a given point in time, but also its evolution over the life course in the medium and long term. These trajectories are often represented by categorical data. This article aims to provide a comprehensive review of the multiple imputation methods proposed so far in the context of univariate categorical data and to assess their practical relevance through a simulation study based on real data. The primary goal is to provide clear methodological guidelines and improve the handling of missing data in life course research. In parallel, we develop the MICT-timing algorithm, which is an extension of the MICT algorithm. This innovative multiple imputation method improves the quality of imputation in trajectories subject to time-varying transition rates, a situation often encountered in life course data.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11135-024-02028-z.

摘要

未标注

生命历程范式强调不仅要研究特定时间点的情况，还要研究其在中长期生命历程中的演变。这些轨迹通常由分类数据表示。本文旨在全面回顾迄今为止在单变量分类数据背景下提出的多重插补方法，并通过基于真实数据的模拟研究评估其实际相关性。主要目标是提供明确的方法指南，改善生命历程研究中缺失数据的处理。同时，我们开发了MICT - 时间算法，它是MICT算法的扩展。这种创新的多重插补方法提高了受时变转换率影响的轨迹的插补质量，这是生命历程数据中经常遇到的情况。