This paper is in the following e-collection/theme issue:

Methods and Instruments in Medical Informatics (194) Advanced Data Analytics in eHealth (211) Tools, Programs and Algorithms (382) Secondary Use of Clinical Data for Research and Surveillance (475) Data Science (358) Electronic Health Records (1322) Clinical Information and Decision Making (1928) Big Data (308)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/64354, first published .
Imputation and Missing Indicators for Handling Missing Longitudinal Data: Data Simulation Analysis Based on Electronic Health Record Data

Imputation and Missing Indicators for Handling Missing Longitudinal Data: Data Simulation Analysis Based on Electronic Health Record Data

Imputation and Missing Indicators for Handling Missing Longitudinal Data: Data Simulation Analysis Based on Electronic Health Record Data

Authors of this article:

Molly Ehrig1 Author Orcid Image ;   Garrett S Bullock1 Author Orcid Image ;   Xiaoyan Iris Leng1 Author Orcid Image ;   Nicholas M Pajewski1 Author Orcid Image ;   Jaime Lynn Speiser1 Author Orcid Image
Article Authors Cited by (2) Tweetations Metrics

Journals

  1. Ren M, Guo H, Guo Y, Guo W, Zhu L. The risk prediction models for cognitive frailty in the older people in China: a systematic review and meta-analysis. BMC Geriatrics 2025;25(1) View
  2. MacRae C, Mercer S, Owen R, Penfold R, Arakelyan S, Dibben C, Pearce J, Lawson A, Lone N, Modig K, Guthrie B. Household size and its role in the association between multimorbidity and health and social care outcomes in older adults in Wales: retrospective cohort study. BMJ Medicine 2025;4(1):e001317 View