The DOI matches its Crossref record; 101 of the 102 references it cites were found in a registry or an index.
The analysis of multivariate longitudinal data: A review
Open accessAbstract
Longitudinal experiments often involve multiple outcomes measured repeatedly within a set of study participants. While many questions can be answered by modeling the various outcomes separately, some questions can only be answered in a joint analysis of all of them. In this article, we will present a review of the many approaches proposed in the statistical literature. Four main model families will be presented, discussed and compared. Focus will be on presenting advantages and disadvantages of the different models rather than on the mathematical or computational details.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“As noted in the review by Verbeke et al. (2014), copula models have been utilized in the multivariate longitudinal data literature although it is not widely used in biomedical applications; early contributions include Frees and Wang (2005), Lambert (1996), Lambert and Vandenhende (2002), and Meester and Mackay (1994).”
From Dependence modeling of multivariate longitudinal hybrid insurance data with dropout (2021) · cites this paper as Verbeke et al. (2014)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Longitudinal data analysis using generalized linear models
LIANG, K.-Y., & ZEGER, S. L. (1986) · Biometrika · doi:10.1093/biomet/73.1.13 · Cited by 18,550
Random-Effects Models for Longitudinal Data
Laird, N. M., & Ware, J. H. (1982) · Biometrics · doi:10.2307/2529876 · Cited by 8,923
Nelsen, R. B. (1999) · Lecture notes in statistics · doi:10.1007/978-1-4757-3076-0 · Cited by 7,777
Everitt, B. S. (2001) · Psychology Press eBooks · doi:10.4324/9781410605542-11 · Cited by 6,877
Goldstein, H. (2006) · Technometrics · doi:10.1198/tech.2006.s417 · Cited by 4,960
Approximate Inference in Generalized Linear Mixed Models
Breslow, N. E., & Clayton, D. G. (1993) · Journal of the American Statistical Association · doi:10.1080/01621459.1993.10594284 · Cited by 4,662
Fonctions de répartition à N dimensions et leurs marges
Sklar, M. (1959) · HAL (Le Centre pour la Communication Scientifique Directe) · Cited by 4,271
Linear Mixed Models for Longitudinal Data
Verbeke, G. (1997) · Lecture Notes in Statistics · doi:10.1007/978-1-4612-2294-1_3 · Cited by 3,506
Self-citationDiggle, P. J., Heagerty, P. J., Liang, K., & Zeger, S. L. (2002) · doi:10.1093/oso/9780198524847.001.0001 · Cited by 3,388
Courgeau, D., & Goldstein, H. (1997) · Population · doi:10.2307/1534624 · Cited by 2,596
Research works printed but not linked: 9
- Shock N, 1984, National Institutes of Health publication 84-2450
- O'Brien L, 2004, Appl Stat, 53, 177
- Sklar A, 1959, Publications de l'Institut de Statistique de l'Université de Paris, 8, 229
- Bryk A, 1992, Hierarchical linear models in social and behavioral research: applications and data analysis methods
- Fitzmaurice G, 2009, Handbooks of Modern Statistical Methods
- Arnold B, 1991, Sankhya: Ind J Stat Series B, 53, 233
- Fieuws S, 2006, Appl Stat, 55, 1
- Liu L, 2005, Biometrics, 3
- Blozis S, 2006, Longitudinal models in the behavioural and related sciences, 189
Cited by
Sorted by citations
Quantification of biological aging in young adults
Belsky, D. W., Caspi, A., Houts, R., Cohen, H. J., Corcoran, D. L., Danese, A., Harrington, H., Israel, S., Levine, M. E., Schaefer, J. D., et al. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1506264112 · Cited by 1,012
Group-based multi-trajectory modeling
Nagin, D., Jones, B., Passos, V. L., & Tremblay, R. (2018) · Statistical Methods in Medical Research · doi:10.1177/0962280216673085 · Cited by 824
On the unnecessary ubiquity of hierarchical linear modeling.
McNeish, D., Stapleton, L. M., & Silverman, R. D. (2016) · Psychological Methods · doi:10.1037/met0000078 · Cited by 787
Faria, R., Gomes, M., Epstein, D., & White, I. R. (2014) · PharmacoEconomics · doi:10.1007/s40273-014-0193-3 · Cited by 673
Measurement error in geometric morphometrics
Fruciano, C. (2016) · Development Genes and Evolution · doi:10.1007/s00427-016-0537-4 · Cited by 314
Metadata
- Authors
- Verbeke, G., Fieuws, S., Molenberghs, G., & Davidian, M.
- Affiliations
- Hasselt University (BE)
- KU Leuven (BE)
- North Carolina State University (US)
- Corresponding author
- Verbeke, Geert
- Venue
- Statistical Methods in Medical Research
- Publisher
- SAGE Publications
- Published
- 2012-04-20
- Volume
- 23
- Issue
- 1
- Pages
- 42-59
- ISSN
- 0962-2802 (ISSN-L)
- 1477-0334
- PMC
- PMC3404254
- OpenAlex
- W2004277545
- Cited by
- 290
- References
- 102
- Citations by year
- 2026: 8 · 2025: 25 · 2024: 17 · 2023: 19 · 2022: 30 · 2021: 43 · 2020: 34 · 2019: 26 · 2018: 19 · 2017: 19 · 2016: 20 · 2015: 14 · 2014: 13 · 2013: 3
- FWCI
- 9.16
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- Statistical Methods in Medical Research (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Document Server@UHasselt (UHasselt) (submittedVersion, other-oa)
- Lirias (submittedVersion)
- Funders
- National Cancer Institute (R01CA085848, P01CA142538)
- National Institute of Allergy and Infectious Diseases (R37AI031789)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Keywords
- multivariate longitudinal data, joint modeling, multiple outcomes, repeated measures, longitudinal experiment, model comparison
- MeSH terms
- Hearing, Humans, Longitudinal Studies, Models, Statistical, Multivariate Analysis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
