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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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“We adjusted for baseline by maintaining the baseline variable as a covariate in the model as recommended in the litterature (Twisk, 2013; Vickers & Altman, 2001).”
MethodIslam, M. M., Yen, L. E., Valderas, J. M., & McRae, I. (2014) · BMC Public Health · doi:10.1186/1471-2458-14-1008 · Cited by 43
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Age-related change in peak oxygen uptake and change of cardiovascular risk factors. The HUNT Study
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“…of subjects who were only present at measurements in the first years may have limited the overall significance of the Time × Group interaction effect, although GEE analysis is expected to statistically overcome this limitation (Twisk, 2013) and including these data increased statistical power.”
Backgroundvan Roekel, E. H., Duchateau, J., Bours, M. J. L., van Delden, L., Breedveld-Peters, J. J. L., Koole, J. L., Kenkhuis, M., van den Brandt, P. A., Jansen, R. L. H., Kant, I., et al. (2020) · Quality of Life Research · doi:10.1007/s11136-020-02566-7 · Cited by 40
“To explore the temporal direction of associations, time-lag analyses were performed in which longitudinal associations of LPA with HRQoL outcomes at a later time point were modelled [32].”
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Yingklang, M., Sengthong, C., Haonon, O., Dangtakot, R., Pinlaor, P., Sota, C., & Pinlaor, S. (2018) · PLoS ONE · doi:10.1371/journal.pone.0198599 · Cited by 38
The depressive price of being a sandwich-generation caregiver: can organizations and managers help?
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den Boeft, M., Twisk, J. W. R., Terluin, B., Penninx, B. W. J. H., van Marwijk, H., Numans, M. E., van der Wouden, J. C., & van der Horst, H. E. (2016) · BMC Health Services Research · doi:10.1186/s12913-016-1332-7 · Cited by 39
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“Twisk (2013) discussed trade-offs in replacing missing data with estimated values vs. utilizing all available data in multilevel model assessment.”
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“Repeated measures taken from the same subjects are likely to be correlated (Twisk, 2003; Raudenbush, 2009).”
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“…effects on lexical scores were analyzed by the Generalized Estimating Equation (GEE) procedure, which estimates coefficients as they vary across participant repeats, and controls for participant effect across individual measurements, as each measurement is nested within a participant (Twisk, 2013).”
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“By including all drop-out predictors in the regression analyses as covariates, and by analysing the total data-set including missing values, we aimed to account for selective drop-out as much as possible (Twisk, 2013).”
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“Although a linear mixed model can handle missing data, assuming it is missing at random, the differential rate of death may introduce bias [27].”
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Self-citationUnderlying mechanisms of improving physical activity behavior after rehabilitation
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“The GEE approach allows estimation of regression coefficients that reflect the longitudinal relation between a predictor variable and an outcome variable [20].”
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“For example, by nature, the assumption of independence of measurements is violated in within-person research (Twisk 2013).”
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“We utilized generalized estimation equations (GEE) to analyze attendance and methamphetamine abstinence during treatment (Twisk 2003).”
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Gootjes, D. V., Posthumus, A. G., Jaddoe, V. W. V., & Steegers, E. A. P. (2021) · BMJ Open · doi:10.1136/bmjopen-2021-049075 · Cited by 24
Holstila, A., Mänty, M., Rahkonen, O., Lahelma, E. T., & Lahti, J. M. T. (2016) · Scandinavian Journal of Medicine and Science in Sports · doi:10.1111/sms.12758 · Cited by 26
“Linear mixed models are more suitable for analyzing repeated measurements than traditional regression models, because they take into account that repeated measurements from individuals are not independent (Twisk, 2013).”
BackgroundBlom, E. E., Aadland, E., Skrove, G. K., Solbraa, A. K., & Oldervoll, L. M. (2020) · Quality of Life Research · doi:10.1007/s11136-020-02554-x · Cited by 26
Martucci, G., Volpes, R., Panarello, G., Tuzzolino, F., Carlo, D. D., Ricotta, C., Gruttadauria, S., Conaldi, P. G., Luca, A., Amrein, K., et al. (2020) · Clinical Nutrition · doi:10.1016/j.clnu.2020.10.027 · Cited by 22
Bystad, E. W., Stefansson, V. T. N., Eriksen, B. O., & Melsom, T. (2023) · Kidney International Reports · doi:10.1016/j.ekir.2023.06.022 · Cited by 22
“The interactions between independent variables and time were taken to represent the independent variables’ associations with the GFR change rate.(37) All 1551 participants were included in the analysis regardless of the number of follow-up GFRs because the linear mixed regression accounts for missing values.”
Method
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
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