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Longitudinal Study Designs

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Handbook of Research Methods in Health Social Sciences

Abstract

Longitudinal study designs are implemented when one or more responses are measured repeatedly on the same individual or experimental unit. These designs often seek to characterize time trajectories for cohorts and individuals within cohorts. Three broad categories of longitudinal designs include (1) repeated measures or growth curve designs, where multiple responses for each individual are observed over time or space under the same intervention or other conditions; (2) crossover designs, where individual responses are measured over sequences of interventions so that individuals each “cross over” from one intervention to another; and (3) follow-up studies, where individuals in a cohort are followed until the time that they either have an “event” (e.g., death, depressive episode) or have not had an event but have no further follow-up information. Longitudinal designs may be either randomized where individuals are randomly assigned into different groups or observational where individuals from different well-defined groups are observed over time. In this chapter, I briefly discuss the nature of each of the three designs above and more deeply explore visualization and some analysis techniques for repeated measures design studies via examples of the analyses of two datasets. I conclude with discussion of recent topics of interest in the modeling of longitudinal data including models for intensive longitudinal data, latent class models, and joint modeling of survival and repeated measures data.

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References

  • Anderson SJ. Biostatistics: a computing approach. Boca Raton: Taylor & Francis Group, LLC; 2011.

    Google Scholar 

  • Baron RM, Kenny DA. The moderator-mediator variable distinction in social psychological research: conceptual, strategic and statistical considerations. J Pers Soc Psychol. 1986;51(6):1173–82.

    Article  Google Scholar 

  • Brown H, Prescott R. Applied mixed models in medicine. 2nd ed. West Sussex: Wiley; 2006.

    Book  Google Scholar 

  • Chen K, Lei J. Localized functional principal component analysis. J Am Stat Assoc. 2015;110(511):1266–75.

    Article  Google Scholar 

  • Choi J-I, Anderson SJ, Richards TJ, Thompson WK. Prediction of transplant-free survival in idiopathic pulmonary fibrosis patients using joint models for event times and mixed multivariate longitudinal data. J Appl Stat. 2014;41(10):2192–205.

    Article  Google Scholar 

  • Diggle PJ, Heagerty P, Liang K-Y and Zeger SL. Analysis of longitudinal data. 2nd ed. Oxford: Oxford University Press; 2002.

    Google Scholar 

  • Fitzmaurice G, Davidian M, Verbeke G, Molenberghs G, editors. Longitudinal data analysis. Chapman & Hall/Taylor & Francis Group: Boca Raton; 2009.

    Google Scholar 

  • Fleiss JL. The design and analysis of clinical experiments. New York: Wiley; 1986.

    Google Scholar 

  • Grizzle JE. The two–period change–over design and its use in clinical trials. Biometrics. 1965;21:467–80.

    Article  Google Scholar 

  • Grizzle JE, Allen DM. Analysis of growth and dose response curves. Biometrics. 1969;25(2):357–81.

    Article  Google Scholar 

  • Guo X, Carlin BP. Separate and joint modeling of longitudinal and event time data using standard computer packages. Am Stat. 2004;58:16–24.

    Article  Google Scholar 

  • Hardin JW, Hilbe JM. Generalized estimating equations. Boca Raton: Chapman & Hall/CRC; 2003.

    Google Scholar 

  • Harville D. Maximum likelihood estimation of variance components and related problems. J Am Stat Assoc. 1977;72:320–40.

    Article  Google Scholar 

  • Hastie T, Tibsharani R, Friedman J. The elements of statistical learning. 2nd ed. New York: Springer; 2009.

    Book  Google Scholar 

  • Hedeker D, Gibbons RD. Longitudinal data analysis. Hoboken: Wiley; 2006.

    Google Scholar 

  • Henderson R, Diggle P, Dobson A. Joint modelling of longitudinal measurements and event time data. Biostatistics. 2000;1:465–80.

    Article  Google Scholar 

  • Jennrich RI, Schlucter MD. Unbalanced repeated-measures models with structured covariance matrices. Biometrics. 1986;42:805–20.

    Article  Google Scholar 

  • Jones RH, Ackerson LM. Unequally spaced longitudinal data with serial correlation. Biometrika. 1990;77:721–31.

    Article  Google Scholar 

  • Kalbfleisch JD, Prentice RL. The statistical analysis of failure time data. 2nd ed. New York: Wiley; 2002.

    Book  Google Scholar 

  • Klein JP, Moeschberger ML. Survival analysis: techniques for censored and truncated data. 2nd ed. New York: Springer; 2003.

    Google Scholar 

  • Kraemer HC. Discovering, comparing, and combining moderators of treatment on outcome after randomized clinical trials: a parametric approach. Stat Med. 2013;32:19.

    Article  Google Scholar 

  • Laird NM, Ware JH. Random effects models for longitudinal data. Biometrics. 1982;38:963–74.

    Article  Google Scholar 

  • Lavori PW, Dawson R. A design for testing clinical strategies: biased adaptive within-subject randomization. J R Stat Soc A. 2000;163:29–38.

    Article  Google Scholar 

  • Lenze EJ, Mulsant BH, Blumberger DM, Karp JF, Newcomer JW, Anderson SJ, Dew MA, Butters M, Stack JA, Begley AE, Reynolds CF. Efficacy, safety, and tolerability of augmentation pharmacotherapy with aripiprazole for treatment-resistant depression in late life: a randomized placebo-controlled trial. Lancet. 2015;386:2404–12.

    Article  Google Scholar 

  • Liang KY, Zeger SL. Longitudinal data analysis using generalized linear models. Biometrika. 1986;73:13–22.

    Article  Google Scholar 

  • Little RJA, Rubin DB. Statistical analysis with missing data. 2nd ed. New York: Wiley; 2002.

    Book  Google Scholar 

  • McCullagh P, Nelder JA. Generalized linear models. London: Chapman and Hall; 1982.

    Google Scholar 

  • McCullagh P, Nelder JA. Generalized linear models. 2nd ed. London: Chapman and Hall; 1989.

    Book  Google Scholar 

  • Molengberghs G, Verbeke G. Models for discrete longitudinal data. New York: Springer; 2005.

    Google Scholar 

  • Muenz LR, Rubinstein LV. Markov models for covariate dependence of binary sequences. Biometrics. 1985;41:91–101.

    Article  Google Scholar 

  • Murphy SA. Optimal dynamic treatment regimes. J R Stat Soc B. 2003;65(2):331–66.

    Article  Google Scholar 

  • Murphy SA. An experimental design for the development of adaptive treatment strategies. Stat Med. 2005;24:1455–81.

    Article  Google Scholar 

  • Nagin DS. Analyzing developmental trajectories: a semiparametric, group-based approach. Psychol Methods. 1999;4(2):139–57.

    Article  Google Scholar 

  • Pearl J. Causality: models, reasoning and inference. Cambridge: Cambridge University Press; 2000.

    Google Scholar 

  • Potthoff R, Roy SN. A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika. 1964;51(3):313–26.

    Article  Google Scholar 

  • Rao CR. Some statistical methods for comparison of growth curves. Biometrics. 1958;14(1):17.

    Article  Google Scholar 

  • Rao CR. The theory of least squares when parameters are stochastic and its application to the analysis of growth curves. Biometrika. 1965;52(3/4):447–58.

    Article  Google Scholar 

  • Reynolds CF III, Butters MA, Lopez O, Pollock BG, et al. Maintenance treatment of depression in old age: a randomized, double-blind, placebo-controlled evaluation of the efficacy and safety of donepezil combined with antidepressant pharmacotherapy. Arch Gen Psychiatry. 2011;68(1):51–60.

    Article  Google Scholar 

  • Rizopoulos D. Dynamic predictions and prospective accuracy in joint models for longitudinal and time-to-event data. Biometrics. 2011;67:819–29.

    Article  Google Scholar 

  • Rizopoulos D. Joint models for longitudinal and time-to-event data, with applications in R. Boca Raton: Chapman and Hall/CRC; 2012.

    Book  Google Scholar 

  • Roeder, K, Lynch, KG and Nagin, DS. Modeling uncertainty in latent class membership: a case study in criminology. J Am Stat Assoc. 2011;94:766–776.

    Google Scholar 

  • Rosner B. Fundamentals of biostatistics. 7th ed. Boston: Brooks/Cole; 2010.

    Google Scholar 

  • Shiffman S, Dunbar MS, Kirchner TR, Li X, Tindle HA, Anderson SJ, Scholl SM, Ferguson SG. Cue reactivity in converted and native intermittent smokers. Nicotine Tob Res. 2015;17(1):119–23.

    Article  Google Scholar 

  • Song X, Davidian M, Tsiatis AA. A semiparametric likelihood approach to joint modeling of longitudinal and time-to-event data. Biometrics. 2002;58:742–53.

    Article  Google Scholar 

  • Stone AA, Shiffman S, Atienza AA, Nebeling L, editors. The science of real-time data capture. New York: Oxford University Press; 2007.

    Google Scholar 

  • Wallis TA, Schafer J, editors. Models for intensive longitudinal data. New York: Oxford Press; 2006.

    Google Scholar 

  • Ware JH. Linear models for the analysis of longitudinal studies. Am Stat. 1985;39(2):95–101.

    Google Scholar 

  • Zeger SL, Liang KY. Longitudinal data analysis for discrete and continuous outcomes. Biometrics. 1986;42:121–30.

    Article  Google Scholar 

  • Zipunnikov V, Greven S, Shou H, Caffo B, et al. Longitudinal high-dimensional principal components analysis with application to diffusion tensor imaging of multiple sclerosis. Ann Appl Stat. 2014;8(4):2175–202.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Biostatistics, University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA, USA

    Stewart J. Anderson

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  1. Stewart J. Anderson
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Correspondence to Stewart J. Anderson .

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Editors and Affiliations

  1. Health, Locked Bag 1797, CA.02.35, Western Sydney Univ, School of Science & Health, Locked Bag 1797, CA.02.35, Penrith, New South Wales, Australia

    Pranee Liamputtong

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Anderson, S.J. (2018). Longitudinal Study Designs. In: Liamputtong, P. (eds) Handbook of Research Methods in Health Social Sciences . Springer, Singapore. https://doi.org/10.1007/978-981-10-2779-6_70-1

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  • DOI: https://doi.org/10.1007/978-981-10-2779-6_70-1

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  • Print ISBN: 978-981-10-2779-6

  • Online ISBN: 978-981-10-2779-6

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