A Latent‐Class Mixture Model for Incomplete Longitudinal Gau…
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- DOI 10.1111/j.1541-0420.2007.00837.x
- PMID https://pubmed.ncbi.nlm.nih.gov/17608789
- OpenAlex ID W2037836788
- Type Article
- Language EN
- Published 30 Jun 2007
- Volume 64 (1)
- Pages 96–105
- Source Biometrics
- Publisher Oxford University Press
- FWCI 7.88
- Countries 2
- Institutions 3
- Added 24 Jun 2016
- Updated 27 Oct 2026
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A Latent‐Class Mixture Model for Incomplete Longitudinal Gaussian Data
Article 2007 EN
In the analyses of incomplete longitudinal clinical trial data, there has been a shift, away from simple methods that are valid only if the data are missing completely at random, to more principled ignorable analyses, which are valid under the less restrictive missing at random assumption. The availability of the necessary standard statistical software nowadays allows for such analyses in practice…
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1 SDGsAuthors & Affiliations
4 authors| # | Author | Institutions | ORCID |
|---|---|---|---|
| 1 | Caroline Beunckens Corresponding | Hasselt University (BE) | — |
| 2 | Geert Molenberghs | Hasselt University (BE) | |
| 3 | Geert N. Verbeke | KU Leuven (BE) | |
| 4 | Craig H. Mallinckrodt | Eli Lilly (United States) (US) |
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