Applied Longitudinal Data Analysis for Epidemiology: A Practical Guide

Twisk, Jos W. R.

doi:10.1017/cbo9781139342834

Cited by 594Statistical Methods and Bayesian InferenceStatistical Methods in EpidemiologyStatistical Methods in Clinical Trials

Applied Longitudinal Data Analysis for Epidemiology: A Practical Guide

Twisk, Jos W. R.·2013·Cambridge University Press eBooks

DOI 10.1017/cbo9781139342834

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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 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 could be detected.
“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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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
DOI: 10.1017/cbo9781139342834
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 · 2012: 2
FWCI: 6.66
Type: book
Language: en
Open access: closed
Licence: www.cambridge.org/core/terms (unspecified)
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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Applied Longitudinal Data Analysis for Epidemiology: A Practical Guide

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