The University of Melbourne Library Digital Repository
metadata only
A comparison of multiple imputation methods for missing data in longitudinal studies 01 Mathematical Sciences
BACKGROUND: Multiple imputation (MI) is now widely used to handle missing data in longitudinal studies. Several MI techniques have been proposed to impute incomplete longitudinal covariates, including standard fully conditional specification (FCS-Standard) and joint multivariate normal imputation (J
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Flexible Multiple Imputation of Missing Data in Time-Structured Longitudinal Designs#433174
- Flexible Multiple Imputation of Missing Data in Time-Structured Longitudinal Designs#323797
- Validity of MMRM-based hypothesis testing under missing-not-at-random mechanisms#1037369
- Benchmarking imputation strategies for missing time-series data in critical care using real-world-inspired scenarios#254286
- Single-Item Reliability for Intensive Longitudinal Data: A Three-Level Linear Mixed-Effect Autoregressive Modeling Approach#297823
Record · ID 1005314
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive