The DOI matches its Crossref record.
An overview of methods for the analysis of longitudinal data
Abstract
This paper reviews statistical methods for the analysis of discrete and continuous longitudinal data. The relative merits of longitudinal and cross‐sectional studies are discussed. Three approaches, marginal, transition and random effects models, are presented with emphasis on the distinct interpretations of their coefficients in the discrete data case. We review generalized estimating equations for inferences about marginal models. The ideas are illustrated with analyses of a 2 × 2 crossover trial with binary responses and a randomized longitudinal study with a count outcome.
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.
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
Cited by
Attrition in longitudinal studies
Twisk, J., & de Vente, W. (2002) · Journal of Clinical Epidemiology · doi:10.1016/s0895-4356(01)00476-0 · Cited by 527
The prevalence of potentially traumatic events in childhood and adolescence
Costello, E. J., Erkanli, A., Fairbank, J. A., & Angold, A. (2002) · Journal of Traumatic Stress · doi:10.1023/a:1014851823163 · Cited by 511
Handling drop‐out in longitudinal studies
Hogan, J. W., Roy, J., & Korkontzelou, C. (2004) · Statistics in Medicine · doi:10.1002/sim.1728 · Cited by 404
Wang, R., Aung, M. S. H., Abdullah, S., Brian, R., Campbell, A. T., Choudhury, T., Hauser, M., Kane, J., Merrill, M., Scherer, E. A., et al. (2016) · Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing · doi:10.1145/2971648.2971740 · Cited by 257
Predicting Improvement in Gait After Stroke: A Longitudinal Prospective Study
Kollen, B., van de Port, I., Lindeman, E., Twisk, J., & Kwakkel, G. (2005) · Stroke · doi:10.1161/01.str.0000190839.29234.50 · Cited by 269
DISENTANGLING THE RISKS: PARENT CRIMINAL JUSTICE INVOLVEMENT AND CHILDREN'S EXPOSURE TO FAMILY RISKS
PHILLIPS, S. D., ERKANLI, A., KEELER, G. P., COSTELLO, E. J., & ANGOLD, A. (2006) · Criminology & Public Policy · doi:10.1111/j.1745-9133.2006.00404.x · Cited by 203
Copy Mean: A New Method to Impute Intermittent Missing Values in Longitudinal Studies
Genolini, C., Écochard, R., & Jacqmin-Gadda, H. (2013) · Open Journal of Statistics · doi:10.4236/ojs.2013.34a004 · Cited by 83
Investigating the pharmacodynamics of ketamine in children
HERD, D. W., ANDERSON, B. J., KEENE, N. A., & HOLFORD, N. H. G. (2008) · Pediatric Anesthesia · doi:10.1111/j.1460-9592.2007.02384.x · Cited by 103
Symptom management for cancer patients: a trial comparing two multimodal interventions
Sikorskii, A., Given, C. W., Given, B., Jeon, S., Decker, V., Decker, D., Champion, V., & McCorkle, R. (2007) · Journal of Pain and Symptom Management · doi:10.1016/j.jpainsymman.2006.11.018 · Cited by 96
CopyMean: A new method to predict monotone missing values in longitudinal studies
Genolini, C., Lacombe, A., Écochard, R., & Subtil, F. (2016) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2016.04.010 · Cited by 52
Long-term progression of Alzheimer's disease in patients under antidementia drugs
Gillette‐Guyonnet, S., Andrieu, S., Nourhashemi, F., Gardette, V., Coley, N., Cantet, C., Gauthier, S., Ousset, P., Vellas, B., & REAL.FR study group (2011) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2011.02.009 · Cited by 64
Umbricht, A., DeFulio, A., Winstanley, E. L., Tompkins, D. A., Peirce, J., Mintzer, M. Z., Strain, E. C., & Bigelow, G. E. (2014) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2014.03.033 · Cited by 57
Delacourt, C., Bertille, N., Salomon, L. J., Benachi, A., Henry, E., Massardier, J., Mottet, N., Rosenblatt, J., Sartor, A., Thong‐Vanh, C., et al. (2019) · Ultrasound in Obstetrics & Gynecology · doi:10.1002/uog.20130 · Cited by 38
Costello, E. J., & Angold, A. (2016) · Developmental Psychopathology · doi:10.1002/9781119125556.devpsy103 · Cited by 13
Metadata
- Authors
- Zeger, S. L., & Liang, K.
- Venue
- Statistics in Medicine
- Publisher
- Wiley
- Published
- 1992-01
- Volume
- 11
- Issue
- 14-15
- Pages
- 1825-1839
- ISSN
- 0277-6715 (ISSN-L)
- 1097-0258
- OpenAlex
- W2171042621
- Cited by
- 678
- References
- 0
- FWCI
- 11.48
- Type
- journal article
- Language
- en
- Open access
- closed
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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.