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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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van Waart, H., Stuiver, M. M., van Harten, W. H., Geleijn, E., Kieffer, J. M., Buffart, L. M., de Maaker‐Berkhof, M., Boven, E., Schrama, J. G., Geenen, M. M., et al. (2015) · Journal of Clinical Oncology · doi:10.1200/jco.2014.59.1081 · Cited by 645
Different ways to estimate treatment effects in randomised controlled trials
J, T., L, B., T, H., J, R., M, W., & M, H. (2018) · Contemporary Clinical Trials Communications · doi:10.1016/j.conctc.2018.03.008 · Cited by 532
“This adjustment was performed by adding a random intercept to the model [13].”
MethodSelf-citationAre loneliness and social isolation associated with cognitive decline?
Lara, E., Caballero, F. F., Rico‐Uribe, L. A., Olaya, B., Haro, J. M., Ayuso‐Mateos, J. L., & Miret, M. (2019) · International Journal of Geriatric Psychiatry · doi:10.1002/gps.5174 · Cited by 423
Understanding and interpreting confidence and credible intervals around effect estimates
Hespanhol, L., Vallio, C. S., Costa, L. M., & Saragiotto, B. T. (2019) · Brazilian Journal of Physical Therapy · doi:10.1016/j.bjpt.2018.12.006 · Cited by 390
Toledo, E., Hu, F. B., Estruch, R., Buil-Cosiales, P., Corella, D., Salas-Salvadó, J., Covas, M. I., Arós, F., Gómez-Gracia, E., Fiol, M., et al. (2013) · BMC Medicine · doi:10.1186/1741-7015-11-207 · Cited by 289
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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