Missing Data Essentials Part 2: Statistical Approaches to Missing Data in Cardiovascular Studies
Clicks: 51
ID: 313465
2026
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Popular Article
15.0
/100
51 views
6 readers
AI Quality Assessment
Not analyzed
Readership in this journal
PopularRanked #7 of 46 articles by views in european journal of cardiovascular nursing : journal of the working group on cardiovascular nursing of the european society of cardiology
Most read
Least read
Bar heights use a square-root scale.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
Missing data are common in cardiovascular nursing and allied health research. Although conventional methods of handling missing data may appear straightforward, they have significant limitations and can introduce bias. In contrast, principled methods, including multiple imputation with and without chained equations and full information maximum likelihood estimation, offer more robust ways to mitigate bias and make full use of available information. These methods of handling missingness can be applied to missing independent and dependent variables, and this methods paper provides worked examples to illustrate each approach.
| Reference Key |
openalex_W7161381205
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Christopher S. Lee, Shirin O. Hiatt, N F Dieckmann, Jill Doyle, Quin E. Denfeld |
| Journal | european journal of cardiovascular nursing : journal of the working group on cardiovascular nursing of the european society of cardiology |
| Year | 2026 |
| DOI |
10.1093/eurjcn/zvag128
|
| URL | |
| Keywords | Keywords not found |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.