Abstract
Sensitive periods are times during development when life experiences can have a greater impact on outcomes than at other periods during the life course. However, a dearth of sophisticated methods for studying time-dependent exposure-outcome relationships means that sensitive periods are often overlooked in research studies in favor of more simplistic and easier-to-use hypotheses such as ever being exposed, or the effect of an exposure accumulated over time. The structured life course modeling approach (SLCMA; pronounced “slick-mah”) allows researchers to model complex life course hypotheses, such as sensitive periods, to determine which hypothesis best explains the amount of variation between a repeated exposure and an outcome. The SLCMA makes use of the least angle regression (LARS) variable selection technique, a type of least absolute shrinkage and selection operator (LASSO) estimation procedure, to yield a parsimonious model for the exposure-outcome relationship of interest. The results of the LARS procedure are complemented with a post-selection inference method, called selective inference, which provides unbiased effect estimates, confidence intervals, and p-values for the final explanatory model. In this chapter, we provide a brief overview of the genesis of this sensitive period modeling approach and provide a didactic step-by-step user’s guide to implement the SLCMA in sensitive- period research. R code to complete the SLCMA is available on our GitHub page at: https://github.com/thedunnlab/SLCMA-pipeline.
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Acknowledgements
We are extremely grateful to all the families who took part in the Avon Longitudinal Study of Parents and Children (ALSPAC) study, the midwives for their help in recruiting them, and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists, and nurses. The UK Medical Research Council and the Wellcome Trust (Grant ref.: 102215/2/13/2) and the University of Bristol provide core support for ALSPAC. A comprehensive list of grants funding is available on the ALSPAC website (http://www.bristol.ac.uk/alspac/external/documents/grant-acknowledgements.pdf). This publication is the work of the authors, each of whom serves as guarantors for the contents of this paper. The authors thank Rebecca Lacey, PhD and Tochukwu Nweze for their suggestions on how to improve this manuscript as well as Susan T. Landry for her assistance in copy-editing the chapter.
Funding: The UK Medical Research Council and Wellcome (Grant ref.: 217065/Z/19/Z) and the University of Bristol provide core support for ALSPAC. This publication is the work of the authors and BJS, ADACS, and ECD will serve as guarantors for the contents of this paper. This work was supported by the National Institute of Mental Health of the National Institutes of Health [grant number R01MH113930 awarded to ECD]. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
Ethical Approval and Informed Consent: Ethical approval for the study was obtained from the ALSPAC Ethics and Law Committee and the Local Research Ethics Committees. Informed consent for the use of data collected via questionnaires and clinics was obtained from participants following the recommendations of the ALSPAC Ethics and Law Committee at the time.
Conflict of Interest: None declared.
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Smith, B.J., Smith, A.D.A.C., Dunn, E.C. (2021). Statistical Modeling of Sensitive Period Effects Using the Structured Life Course Modeling Approach (SLCMA). In: Andersen, S.L. (eds) Sensitive Periods of Brain Development and Preventive Interventions. Current Topics in Behavioral Neurosciences, vol 53. Springer, Cham. https://doi.org/10.1007/7854_2021_280
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DOI: https://doi.org/10.1007/7854_2021_280
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