Abstract
Life course epidemiology (LCE) is a subfield of epidemiology that emphasizes the timing of events over and above cumulative exposure. This implies that the data at hand are mostly observational, longitudinal, and with repeated measures. Describing the data, formulating life course hypotheses and testing them requires specific conceptual and statistical tools. In this chapter, we provide an overview of some statistical methods based on four LCE papers both methodological and subject matter based. Although not exhaustive, this selection covers both sides of the theory building process. First, purely descriptive methods requiring few or no hypotheses are presented. At the other end, we describe the framework to formally formulate life course hypotheses. With them come several statistical methods to measure how well they hold up against real-life data. Along the way, many difficulties such as missing data, heterogeneous data sources, and hypothesis hierarchy are discussed with tools and approaches available to handle them.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abbott A (1983) Sequences of social events: concepts and methods for the analysis of order in social processes. Hist Methods 16(4):129–147. https://doi.org/10.1080/01615440.1983.10594107
Abbott A, Forrest J (1986) Optimal matching methods for historical sequences. J Interdiscip Hist 16(3):471. https://doi.org/10.2307/204500
Ahrens W, Pigeot I (eds) (2014) Handbook of epidemiology, 2nd edn. Springer Reference
Alwan H, Pruijm M, Ponte B, Ackermann D, Guessous I, Ehret G, Staessen JA, Asayama K, Vuistiner P, Younes SE, Paccaud F, Wuerzner G, Pechere-Bertschi A, Mohaupt M, Vogt B, Martin P-Y, Burnier M, Bochud M (2014) Epidemiology of masked and white-coat hypertension: the family-based SKIPOGH study. PLoS One 9(3):e92522. https://doi.org/10.1371/journal.pone.0092522
Ben-Shlomo Y, Cooper R, Kuh D (2016) The last two decades of life course epidemiology, and its relevance for research on ageing. Int J Epidemiol 45(4):973–988. https://doi.org/10.1093/ije/dyw096
Berger E, Castagné R, Chadeau-Hyam M, Bochud M, d’Errico A, Gandini M, Karimi M, Kivimäki M, Krogh V, Marmot M, Panico S, Preisig M, Ricceri F, Sacerdote C, Steptoe A, Stringhini S, Tumino R, Vineis P, Delpierre C, Kelly-Irving M (2019) Multi-cohort study identifies social determinants of systemic inflammation over the life course. Nat Commun 10(1):773. https://doi.org/10.1038/s41467-019-08732-x
Carmeli C, Steen J, Petrovic D, Lepage B, Delpierre C, Kelly-Irving M, Bochud M, Kivimäki M, Vineis P, Stringhini S (2020) Mechanisms of life-course socioeconomic inequalities in adult systemic inflammation: findings from two cohort studies. Soc Sci Med 245:112685. https://doi.org/10.1016/j.socscimed.2019.112685
Carpenter JR, Kenward MG (2013) Multiple imputation and its application: carpenter/multiple imputation and its application. John Wiley & Sons. https://doi.org/10.1002/9781119942283
De Stavola BL, Daniel RM (2012) Commentary: marginal structural models the way forward for life-course epidemiology? Epidemiology 23(2):233–237. https://doi.org/10.1097/EDE.0b013e318245847e
De Stavola BL, Daniel RM (2016) Commentary: incorporating concepts and methods from causal inference into life course epidemiology. Int J Epidemiol 45(4):1006–1010. https://doi.org/10.1093/ije/dyw103
Eisenberg-Guyot J, Peckham T, Andrea SB, Oddo V, Seixas N, Hajat A (2020) Life-course trajectories of employment quality and health in the U.S.: a multichannel sequence analysis. Soc Sci Med 264:113327. https://doi.org/10.1016/j.socscimed.2020.113327
Everitt B, Howell DC (eds) (2005) Encyclopedia of statistics in behavioral science. John Wiley & Sons
Finkelstein MM (1991) Use of “time windows” to investigate lung cancer latency intervals at an Ontario steel plant. Am J Ind Med 19(2):229–235. https://doi.org/10.1002/ajim.4700190210
Firmann M, Mayor V, Vidal PM, Bochud M, Pécoud A, Hayoz D, Paccaud F, Preisig M, Song KS, Yuan X, Danoff TM, Stirnadel HA, Waterworth D, Mooser V, Waeber G, Vollenweider P (2008) The CoLaus study: a population-based study to investigate the epidemiology and genetic determinants of cardiovascular risk factors and metabolic syndrome. BMC Cardiovasc Disord 8(1):6. https://doi.org/10.1186/1471-2261-8-6
Gabadinho A, Ritschard G, Müller NS, Studer M (2011) Analyzing and Visualizing State Sequences in R with TraMineR. J Stat Softw 40(4). https://doi.org/10.18637/jss.v040.i04
Halpin B (2017) SADI: sequence analysis tools for Stata. Stata J 17(3):546–572
Jones BL, Nagin DS, Roeder K (2001) A SAS procedure based on mixture models for estimating developmental trajectories. Sociol Methods Res 29(3):374–393. https://doi.org/10.1177/0049124101029003005
Klijn SL, Weijenberg MP, Lemmens P, van den Brandt PA, Lima Passos V (2017) Introducing the fit-criteria assessment plot – a visualisation tool to assist class enumeration in group-based trajectory modelling. Stat Methods Med Res 26(5):2424–2436. https://doi.org/10.1177/0962280215598665
Kuh D (2003) Life course epidemiology. J Epidemiol Community Health 57(10):778–783. https://doi.org/10.1136/jech.57.10.778
Lange T, Vansteelandt S, Bekaert M (2012) A simple unified approach for estimating natural direct and indirect effects. Am J Epidemiol 176(3):190–195
Little RJ, Rubin DB (2019) Statistical analysis with missing data, vol 793. John Wiley & Sons
Madathil SA, Rousseau M-C, Wynant W, Schlecht NF, Netuveli G, Franco EL, Nicolau B (2016) Nonlinear association between betel quid chewing and oral cancer: implications for prevention. Oral Oncol 60:25–31. https://doi.org/10.1016/j.oraloncology.2016.06.011
Madathil S, Joseph L, Hardy R, Rousseau M-C, Nicolau B (2018) A Bayesian approach to investigate life course hypotheses involving continuous exposures. Int J Epidemiol 47(5):1623–1635. https://doi.org/10.1093/ije/dyy107
McElreath R (2020) Statistical rethinking: a Bayesian course with examples in R and Stan, 2nd edn. Taylor and Francis, CRC Press
Mishra G, Nitsch D, Black S, De Stavola B, Kuh D, Hardy R (2009) A structured approach to modelling the effects of binary exposure variables over the life course. Int J Epidemiol 38(2):528–537. https://doi.org/10.1093/ije/dyn229
Nagin DS (2005) Group-based modeling of development. Harvard University Press
Pearl J (2000) Causality: models, reasoning, and inference. Cambridge University Press
Pearl J, Glymour M, Jewell NP (2016) Causal inference in statistics: a primer. Wiley
Peters S, Turner MC, Bugge MD, Vienneau D, Vermeulen R (2020) International inventory of occupational exposure information: OMEGA-NET. Ann Work Expos Health 64(5):465–467. https://doi.org/10.1093/annweh/wxaa021
Proust-Lima C, Philipps V, Liquet B (2017) Estimation of extended mixed models using latent classes and latent processes: the R Package lcmm. J Stat Softw 78(2):10.18637/jss.v078.i02
Rousseeuw PJ (1987) Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 20:53–65. https://doi.org/10.1016/0377-0427(87)90125-7
Rubin DB (1974) Estimating causal effects of treatments in randomized and nonrandomized studies. J Educ Psychol 66(5):688–701. https://doi.org/10.1037/h0037350
Singer JD, Willett JB (2003) Applied longitudinal data analysis. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780195152968.001.0001
Smith ADAC, Hardy R, Heron J, Joinson CJ, Lawlor DA, Macdonald-Wallis C, Tilling K (2016) A structured approach to hypotheses involving continuous exposures over the life course. Int J Epidemiol:dyw164. https://doi.org/10.1093/ije/dyw164
Stan Development Team (2021) Stan modeling language users guide and reference manual 2.16. https://mc-stan.org/users/documentation/
Steen J, Loeys T, Moerkerke B, Vansteelandt S (2017) medflex: an R Package for Flexible Mediation Analysis using Natural Effect Models. J Stat Softw 76(11). https://doi.org/10.18637/jss.v076.i11
Studer M, Ritschard G (2016) What matters in differences between life trajectories: a comparative review of sequence dissimilarity measures. J R Stat Soc A Stat Soc 179(2):481–511. https://doi.org/10.1111/rssa.12125
Tu Y-K, Tilling K, Sterne JA, Gilthorpe MS (2013) A critical evaluation of statistical approaches to examining the role of growth trajectories in the developmental origins of health and disease. Int J Epidemiol 42(5):1327–1339. https://doi.org/10.1093/ije/dyt157
van Buuren S, Groothuis-Oudshoorn K (2011) Mice: multivariate imputation by chained equations in R. J Stat Softw 45(3):1–67. https://doi.org/10.18637/jss.v045.i03
VanderWeele TJ (2014) Commentary: resolutions of the birthweight paradox: competing explanations and analytical insights. Int J Epidemiol 43(5):1368–1373. https://doi.org/10.1093/ije/dyu162
Wagner M, Grodstein F, Leffondre K, Samieri C, Proust-Lima C (2021) Time-varying associations between an exposure history and a subsequent health outcome: a landmark approach to identify critical windows. BMC Med Res Methodol 21(1):266. https://doi.org/10.1186/s12874-021-01403-w
Zang E, Max JT (2020) Bayesian estimation and model selection in group-based trajectory models. Psychol Methods. https://doi.org/10.1037/met0000359
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 Springer Nature Switzerland AG
About this entry
Cite this entry
Le Guillou, A., Wild, P. (2023). Methods in Modeling Life Course. In: Wahrendorf, M., Chandola, T., Descatha, A. (eds) Handbook of Life Course Occupational Health. Handbook Series in Occupational Health Sciences. Springer, Cham. https://doi.org/10.1007/978-3-030-94023-2_13-1
Download citation
DOI: https://doi.org/10.1007/978-3-030-94023-2_13-1
Received:
Accepted:
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-94023-2
Online ISBN: 978-3-030-94023-2
eBook Packages: Springer Reference Behavioral Science and PsychologyReference Module Humanities and Social SciencesReference Module Business, Economics and Social Sciences