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Applied Longitudinal Data Analysis for Epidemiology: A Practical Guide
Abstract
This book discusses the most important techniques available for longitudinal data analysis, from simple techniques such as the paired t-test and summary statistics, to more sophisticated ones such as generalized estimating of equations and mixed model analysis. A distinction is made between longitudinal analysis with continuous, dichotomous and categorical outcome variables. The emphasis of the discussion lies in the interpretation and comparison of the results of the different techniques. The second edition includes new chapters on the role of the time variable and presents new features of longitudinal data analysis. Explanations have been clarified where necessary and several chapters have been completely rewritten. The analysis of data from experimental studies and the problem of missing data in longitudinal studies are discussed. Finally, an extensive overview and comparison of different software packages is provided. This practical guide is essential for non-statisticians and researchers working with longitudinal data from epidemiological and clinical studies.
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“Post-hoc power calculations based on the inclusion rates of this study with three time points indicated that differences of medium size (d = .59) between study groups over time at a significance level of p = .05 with a power of .80 (Twisk, 2013) could be detected.”
From Online psychosocial group intervention for parents: Positive effects on anxiety and depression. (2021) · cites this paper as Twisk, 2013
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“Secondly, the within-subject interpretation implies a change within one subject of 1-unit intermediate variable is associated with a change of β units in the primary outcome measure (Twisk, 2013).”
el Bouhaddani, S., Veling, W., Schaefer, B., Doreleijers, T., & Domburgh, L. (2018) · Early Intervention in Psychiatry · doi:10.1111/eip.12755 · Cited by 4
“6%, indicating that observations from participants in one intervention group were no more similar than observations from other intervention groups and therefore multilevel analyses were not needed (Twisk, 2013).”
BackgroundResultInfluentialFactors influencing the length of hospital stay of patients with substance use disorders
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Peynenburg, V. A., Sapkota, R. P., Lozinski, T., Sundström, C., Wilhelms, A., Titov, N., Dear, B. F., & Hadjistavropoulos, H. D. (2023) · JMIR Mental Health · doi:10.2196/44722 · Cited by 3
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Mixed Model Analysis in Longitudinal Studies
Twisk, J. W. R. (2019) · Cambridge University Press eBooks · doi:10.1017/9781108635660.010 · Cited by 6
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Vreeker, A., Horsfall, M., Eikelenboom, M., Beerthuizen, A., Bergink, V., Boks, M. P., Hartman, C. A., de Koning, R. M., de Leeuw, M., Maciejewski, D. F., et al. (2024) · BMC Psychiatry · doi:10.1186/s12888-024-05555-z · Cited by 3
Ulke, C., Jürgen Kayser, A., Tenke, C. E., Mergl, R., Sander, C., Panier, L. Y. X., Alvarenga, J. E., D, M. F. M., McGrath, P. J., Deldin, P. J., et al. (2024) · Psychiatry Research · doi:10.1016/j.psychres.2024.116165 · Cited by 3
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Progression of GBA1 severe and risk variants: a longitudinal mixed model analysis
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“As individuals with PD were enrolled at different time points (Twisk, 2013) due to the dynamic cohort study design, the disease progression could be distinguished from cohort or period effects.”
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Healthy Aging in Colombia 2018 and Its Variation in Relation to Social Conditions
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Explaining Differences between Groups
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“Logistic generalized estimating equations (GEE) with exchangeable correlation structure ITT analysis will be used to examine the effects on dichotomous outcomes, since GEE is reported to be a better estimator of effects in dichotomous outcomes than LMM [66].”
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“…DMFT score that will continue unchanged over the follow-up period (parallel lines for the intake quintiles) whereas a significant interaction with time in Model 3 indicates that the effect of starch intake on DMFT score varies over time (divergent or convergent lines for the intake quintiles) [26].”
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Twisk, J. W. R. (2014) · Springer eBooks · doi:10.1007/978-3-642-55345-5_10 · Cited by 3
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“We also included APOE ε4 carrier status as a covariate using linear conditional fixed effects regression (Twisk, 2013).”
Wilborn, D., Kottner, J., Hillmann, K., Xu, S., Konietschke, F., & Blume‐Peytavi, U. (2021) · Dermatology Research and Practice · doi:10.1155/2021/4163705 · Cited by 6
Yirga, A. A., Melesse, S. F., Mwambi, H. G., et al. (2022) · BMC Infectious Diseases · doi:10.1186/s12879-021-06942-7 · Cited by 2
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“LME analyses allow the modelling of change over time for correlated data in longitudinal studies (Singer & Willett, 2003; Twisk, 2013).”
Occupational stress and body composition of hospital workers: a follow-up study
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Multivariate Mixed Model Analysis
Twisk, J. W. R. (2019) · Cambridge University Press eBooks · doi:10.1017/9781108635660.011 · Cited by 4
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“Dies gilt als Standardvorgehen bei der Modellierung von Längsschnittdaten (Twisk 2013).”
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Clinical signs in the jaw and neck region following whiplash trauma—A 2‐year follow‐up
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Self-citationMeta-Analysis on Individual Participant Data
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Self-citationGLMM and GLMMTree for Modelling Poverty in Indonesia
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“While we used the most appropriate analysis technique to handle missing data (Twisk, 2013), the substantial drop-out might have affected the results.”
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“They also account for the correlations that occur among repeated measurements from the same individual [27, 28].”
Metadata
- Authors
- Twisk, J. W. R.
- Affiliations
- Vrije Universiteit Amsterdam (NL)
- Corresponding author
- Twisk, Jos W. R.
- ORCID
- Twisk, Jos W. R.
- Venue
- Cambridge University Press eBooks
- Publisher
- Cambridge University Press
- Published
- 2013-05-05
- OpenAlex
- W4251962509
- Cited by
- 593
- References
- 0
- Citations by year
- 2026: 45 · 2025: 34 · 2024: 47 · 2023: 39 · 2022: 48 · 2021: 48 · 2020: 55 · 2019: 66 · 2018: 36 · 2017: 36 · 2016: 33 · 2015: 33 · 2014: 14 · 2013: 8
- FWCI
- 6.66
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Cambridge University Press eBooks (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Keywords
- longitudinal analysis, generalized estimating equations, mixed models, paired t-test, summary statistics, time variable, experimental study, missing data, statistical software comparison, epidemiology
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 ›
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