The DOI matches its Crossref record; 82 of the 82 references it cites were found in a registry or an index.
Advances in Analysis of Longitudinal Data
Open accessAbstract
In this review, we explore recent developments in the area of linear and nonlinear generalized mixed-effects regression models and various alternatives, including generalized estimating equations for analysis of longitudinal data. Methods are described for continuous and normally distributed as well as categorical (binary, ordinal, nominal) and count (Poisson) variables. Extensions of the model to three and four levels of clustering, multivariate outcomes, and incorporation of design weights are also described. Linear and nonlinear models are illustrated using an example involving a study of the relationship between mood and smoking.
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
“To accommodate missing data, mixed models, which involve randomly deleting missing observations without dropping participants, will be used where appropriate in conducting primary analyses [70].”
From Acceptance and Commitment Therapy preceded by an experimental Attention Bias Modification procedure in recurrent depression: Study protocol for a randomized controlled trial (2018) · cites this paper as [70]
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
Hierarchical Linear Models: Applications and Data Analysis Methods.
Longford, N. T., Bryk, A. S., & Raudenbush, S. W. (1993) · Contemporary Sociology A Journal of Reviews · doi:10.2307/2075823 · Cited by 18,773
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
Statistical Analysis With Missing Data
Lahiff, M., Little, R. J. A., & Rubin, D. B. (1989) · Journal of the American Statistical Association · doi:10.2307/2289883 · Cited by 17,385
Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence
Singer, J. D., & Willett, J. B. (2003) · doi:10.1093/acprof:oso/9780195152968.001.0001 · Cited by 10,647
RUBIN, D. B. (1976) · Biometrika · doi:10.1093/biomet/63.3.581 · Cited by 9,925
Random-Effects Models for Longitudinal Data
Laird, N. M., & Ware, J. H. (1982) · Biometrics · doi:10.2307/2529876 · Cited by 8,923
Statistical Analysis with Missing Data
Little, R. J. A., & Rubin, D. B. (2002) · Wiley Series in Probability and Statistics · doi:10.1002/9781119013563 · Cited by 8,473
Longitudinal Data Analysis for Discrete and Continuous Outcomes
Zeger, S. L., & Liang, K. (1986) · Biometrics · doi:10.2307/2531248 · Cited by 7,781
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
Research works printed but not linked: 7
- Bock RD, 1983, Modern Advances in Psychometric Research, 103
- Bock RD, 1983, Statistical and Methodological Advances in Psychiatric Research, 59
- 10.1037/0022-006X.65.1.130 · DOI
- Gibbons RD, 1981, Trend in correlated proportions
- Gibbons RD, 1988, Psychopharmacol. Bull., 24, 438
- Hedeker D, 1989, Random regression models with autocorrelated errors
- Hedeker D, 2008, SuperMix—a program for mixed-effects regression models
Cited by
Sorted by citations
Items 401–452 of 452
Doran, N. (2013) · American Journal on Addictions · doi:10.1111/j.1521-0391.2013.12094.x
Sequential Monte Carlo Methods in Random Intercept Models for Longitudinal Data
Alvares, D., Armero, C., Forte, A., & Chopin, N. (2017) · Springer proceedings in mathematics & statistics · doi:10.1007/978-3-319-54084-9_1
Marquart, L. (2016) · The University of Queensland · doi:10.14264/uql.2016.1083
Bundesgesundheitsblatt Gesundheitsforschung | Gesundheitsschutz
Zimmermann, T. P. (2013)
The Association between Distribution of Influenza-Like Illness and Climate in Each Zone of Thailand
Wechphanich, S., Supapong, S., & Rattananupong, T. (2017) · NRCT Data Center · doi:10.14456/jhr.2017.46
Woldu, H. G., Heckman, T. G., Handel, A., & Shen, Y. (2018) · Journal of Applied Statistics · doi:10.1080/02664763.2018.1470231
Arovah, N. I. (1905) · Queensland University of Technology · doi:10.5204/thesis.eprints.118193
Metacognition, stress & recovery
Capobianco, L. (2017) · Research Explorer (The University of Manchester)
Palaniswamy, V. (2018) · Griffith Research Online · doi:10.25904/1912/1907
Roy, Y., Piponnier, J.-C., & Faubert, J. (2018) · Lecture notes in electrical engineering · doi:10.1007/978-981-13-1642-5_66
Kang, S. (2018) · Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign)
The modeling of trajectories in psychotic illness
Clark, S. R., Schubert, K. O., & Baune, B. T. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-813176-3.00002-x
Research Design and Statistical Method
Tabassum, N., & Singh, S. (2020) · Springer eBooks · doi:10.1007/978-3-030-48527-6_4
Cho, J., Chien, L.-C., & Holditch‐Davis, D. (2020) · The Journal of Perinatal & Neonatal Nursing · doi:10.1097/jpn.0000000000000519
Philibert, A., Fillion, M., Silva, J. D., Lena, T. S., & Mergler, D. (2021) · Research Square · doi:10.21203/rs.3.rs-952530/v1
Methodological issues to analyse real-world longitudinal occupational health data (Preprint)Preprint
Colin-Chevalier, R., Dutheil, F., Dewavrin, S., Cornet, T., Baker, J. S., & Pereira, B. (2021) · doi:10.2196/preprints.35068
Riordan, B. C., Moradi, S., Carey, K. B., Conner, T. S., Jang, K., Reid, K. E., & Scarf, D. (2018) · doi:10.2196/preprints.10164.a
Huang, Y. C., Kao, L., Liao, T.-H., Chiu, C. C., & Wen, H. (2023) · Schizophrenia Research · doi:10.1016/j.schres.2023.02.002
Multidimensional beta-binomial regression model: A joint analysis of patient-reported outcomes
Najera‐Zuloaga, J., Lee, D., Esteban, C., & Aróstegui, I. (2023) · Statistical Modelling · doi:10.1177/1471082x231151311
Bayesian Learning of Personalized Longitudinal Biomarker Trajectory
Zhou, S., Huang, X., Shen, C., & Kantarjian, H. M. (2023) · Annals of Data Science · doi:10.1007/s40745-023-00486-0
Longitudinal Studies in Pediatric Gastrointestinal Surgery: Theory and Practice
Stellato, R. K. (2023) · doi:10.33540/2051
Albert, W., Spielmann, H., Semmig‐Könze, S., Knosalla, C., Mulzer, J., Tigges‐Limmer, K., Kugler, C., & Richter, F. (2025) · Artificial Organs · doi:10.1111/aor.14996
Linear mixed effects double penalized L p -quantile regression model for longitudinal data
Chen, J., Wan, F., Qiu, H., & Yuan, Z. (2025) · Communication in Statistics- Theory and Methods · doi:10.1080/03610926.2025.2531411
The impact of screening-detected atrial fibrillation and associated outcomes on quality of life
Kongebro, E. K., Kronborg, C., Haugan, K., Graff, C., Højberg, S., Krieger, D. W., Brandes, A., Køber, L., Svendsen, J. H., & Diederichsen, S. Z. (2025) · Quality of Life Research · doi:10.1007/s11136-025-04047-1
Melkani, D., Harnwal, N., Desai, S., Patel, D., & Melkani, G. C. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.31.685789
Tinsel, I., Metzner, G., Schlett, C., Sehlbrede, M., Bischoff, M., Anger, R., Brame, J., König, D., Wurst, R., Fuchs, R., et al. (2021) · Figshare · doi:10.6084/m9.figshare.15145659
Tinsel, I., Metzner, G., Schlett, C., Sehlbrede, M., Bischoff, M., Anger, R., Brame, J., König, D., Wurst, R., Fuchs, R., et al. (2021) · Figshare · doi:10.6084/m9.figshare.15145668
Loidl, V., Hamacher, K., Lang, M., Laub, O., Schwettmann, L., & Grill, E. (2023) · Figshare · doi:10.6084/m9.figshare.24113188
Tinsel, I., Metzner, G., Schlett, C., Sehlbrede, M., Bischoff, M., Anger, R., Brame, J., König, D., Wurst, R., Fuchs, R., et al. (2021) · Figshare · doi:10.6084/m9.figshare.15145671
Rezaei, M., Forouzan, K., Eini‐Zinab, H., Omidvar, N., Jafaripour, S., & Rezazadeh, A. (2024) · Figshare · doi:10.6084/m9.figshare.26793741
Dzakpasu, F. Q. S., Owen, N. G., Carver, A., Sethi, P., Brakenridge, C. J., Salim, A., Urquhart, D. M., Cicuttini, F., & Dunstan, D. W. (2022) · Figshare · doi:10.6084/m9.figshare.21656100
Leeners, B., Kruger, T. H. C., Geraedts, K., Tronci, E., Mancini, T., Ille, F., Egli, M., Röblitz, S., Saleh, L., Spanaus, K., et al. (2017) · Zürcher Hochschule für Angewandte Wissenschaften digital collection (Zurich University of Applied Sciences) · doi:10.21256/zhaw-1585
Tinsel, I., Metzner, G., Schlett, C., Sehlbrede, M., Bischoff, M., Anger, R., Brame, J., König, D., Wurst, R., Fuchs, R., et al. (2021) · Figshare · doi:10.6084/m9.figshare.15145662
Philibert, A., Silva, J. D., Fillion, M., & Mergler, D. (2024) · Figshare · doi:10.6084/m9.figshare.25951786
Tinsel, I., Metzner, G., Schlett, C., Sehlbrede, M., Bischoff, M., Anger, R., Brame, J., König, D., Wurst, R., Fuchs, R., et al. (2021) · Figshare · doi:10.6084/m9.figshare.15145653
Tinsel, I., Metzner, G., Schlett, C., Sehlbrede, M., Bischoff, M., Anger, R., Brame, J., König, D., Wurst, R., Fuchs, R., et al. (2021) · Figshare · doi:10.6084/m9.figshare.15145656
Tinsel, I., Metzner, G., Schlett, C., Sehlbrede, M., Bischoff, M., Anger, R., Brame, J., König, D., Wurst, R., Fuchs, R., et al. (2021) · Figshare · doi:10.6084/m9.figshare.15145650
Liu, Q., Zhu, A. Y., Tang, M., Tran, T., & Rodriguez, V. J. (2026) · doi:10.2196/preprints.90413
Martínez, A. F., Ramírez‐Silva, C. I., & Fuentes–García, R. (2026) · Statistical Methods in Medical Research · doi:10.1177/09622802251414594
Stemerdink, N. C., Heemskerk, S. C. M., Hartman, E., Wesseling, M., Tieleman, P., Burdorf, A., & Haagsma, J. A. (2026) · Quality of Life Research · doi:10.1007/s11136-026-04295-9
Huynh, M., O'Connor, S., McManamen, M., Engstrom, A., Russo, J. E., Wang, J., Iles-Shih, M., Shoyer, J., Whiteside, L., & Zatzick, D. (2026) · Injury · doi:10.1016/j.injury.2026.113481
A Reproducible Statistical-Learning Framework for Sparse Time-Indexed Transaction Data
Cristescu, M. P., & Petrea, I. (2026) · Mathematics · doi:10.3390/math14193598
Egeland, K. M., Borge, R. H., Ehrhart, M. G., Aarons, G. A., Sklar, M., Braathu, N., Wentzel‐Larsen, T., Peters, N., Bækkelund, H., Endsjø, M., et al. (2026) · Implementation Science · doi:10.1186/s13012-026-01538-8
der Hoek, L. V., de Vette, S. P. M., Neh, H., MacRae, D. C., Langendijk, J. A., Sijtsema, N. M., van Ooijen, P. M. A., van der Schaaf, A., & van Dijk, L. V. (2026) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2026.111815
Obuaya, O., Luz, S. F., Taylor, R. S., & Weir, C. J. (2026) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms69103.2026.00159
Tosefsky, K. N., Schaffner, S. L., Lam, J. S. T., Wang, Y. N., Keymanesh, S., Kuan, A. J., Metcalfe‐Roach, A., Cirstea, M. S., Sacheli, M., Morelli, T. F., et al. (2026) · iScience · doi:10.1016/j.isci.2026.117981
Metadata
- Authors
- Gibbons, R. D., Hedeker, D., & DuToit, S.
- Affiliations
- University of Illinois Chicago (US)
- Venue
- Annual Review of Clinical Psychology
- Publisher
- Annual Reviews
- Published
- 2010-03-01
- Volume
- 6
- Issue
- 1
- Pages
- 79-107
- ISSN
- 1548-5943 (ISSN-L)
- 1548-5951
- PMC
- PMC2971698
- OpenAlex
- W2113413884
- Cited by
- 452
- References
- 75
- Citations by year
- 2026: 16 · 2025: 26 · 2024: 35 · 2023: 28 · 2022: 26 · 2021: 40 · 2020: 30 · 2019: 32 · 2018: 48 · 2017: 57 · 2016: 30 · 2015: 24 · 2014: 21 · 2013: 15 · 2012: 17
- FWCI
- 1.75
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Annual Review of Clinical Psychology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (n44mh32056-10-0-1)
- National Institute of Mental Health (R01 MH8012201, N44 MH032056, R01MH080122)
- National Cancer Institute (P01CA098262, 5P01CA98262)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Spatial and Panel Data Analysis
- Life Sciences › Agricultural and Biological Sciences › Agronomy and Crop Science › Agriculture, Soil, Plant Science
- Keywords
- longitudinal analysis, nonlinear mixed-effects models, generalized estimating equations, categorical outcomes, count data, Poisson regression, multilevel models, multivariate outcomes, design weights
- MeSH terms
- Humans, Longitudinal Studies, Models, Psychological, Psychology, Clinical
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