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Cited by 63Statistical Methods and InferenceStatistical Methods in EpidemiologyAdvanced Causal Inference Techniques

Model Selection of the Effect of Binary Exposures over the Life Course

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Smith, Andrew D. A. C., Heron, Jon, Mishra, Gita, Gilthorpe, Mark S., Ben-Shlomo, Yoav, Tilling, Kate·2015·Epidemiology
DOI 10.1097/ede.0000000000000348PMC PMC4521897
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Abstract

Epidemiologists are often interested in examining the effect on a later-life outcome of an exposure measured repeatedly over the life course. When different hypotheses for this effect are proposed by competing theories, it is important to identify those most supported by observed data as a first step toward estimating causal associations. One method is to compare goodness-of-fit of hypothesized models with a saturated model, but it is unclear how to judge the "best" out of two hypothesized models that both pass criteria for a good fit. We developed a new method using the least absolute shrinkage and selection operator to identify which of a small set of hypothesized models explains most of the observed outcome variation. We analyzed a cohort study with repeated measures of socioeconomic position (exposure) through childhood, early- and mid-adulthood, and body mass index (outcome) measured in mid-adulthood. We confirmed previous findings regarding support or lack of support for the following hypotheses: accumulation (number of times exposed), three critical periods (only exposure in childhood, early- or mid-adulthood), and social mobility (transition from low to high socioeconomic position). Simulations showed that our least absolute shrinkage and selection operator approach identified the most suitable hypothesized model with high probability in moderately sized samples, but with lower probability for hypotheses involving change in exposure or highly correlated exposures. Identifying a single, simple hypothesis that represents the specified knowledge of the life course association allows more precise definition of the causal effect of interest.

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  • Whether using a triangulation approach or not, if the timing of exposure is central to the research question then that needs to be specifically included in the hypotheses tested and the comparisons made.

    “Whether using a triangulation approach or not, if the timing of exposure is central to the research question then that needs to be specifically included in the hypotheses tested and the comparisons made.49,50”

    From Triangulation in aetiological epidemiology (2017) · cites this paper as [49]

  • Timing of exposure, such as whether an exposure has an effect only during a critical period, or has different magnitudes of effect at different times in the life course (sensitive periods), are important to consider.

    “Timing of exposure, such as whether or not an exposure has an effect only during a critical period, or has different magnitudes of effect at different times in the life course (sensitive periods) are also important to consider.49,50”

    From Triangulation in aetiological epidemiology (2017) · cites this paper as [49]

  • The structured approach for binary exposures was developed by the authors.

    “We developed a structured approach for binary exposures,13 based on least angle regression (LARS),14 that demonstrated more accuracy in simulation than other methods.”

    From A structured approach to hypotheses involving continuous exposures over the life course (2016) · cites this paper as [13]

  • The structured approach for binary exposures demonstrated more accuracy in simulation than other methods.

    “We developed a structured approach for binary exposures,13 based on least angle regression (LARS),14 that demonstrated more accuracy in simulation than other methods.”

    From A structured approach to hypotheses involving continuous exposures over the life course (2016) · cites this paper as [13]

  • LARS can suffer from low power if some variables are highly correlated.

    “LARS can suffer from low power if some variables are highly correlated,13 therefore we centred C and Δ3 to reduce their correlation with their product.”

    From A structured approach to hypotheses involving continuous exposures over the life course (2016) · cites this paper as [13]

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Authors
Smith, A. D. A. C., Heron, J., Mishra, G., Gilthorpe, M. S., Ben-Shlomo, Y., & Tilling, K.
Affiliations
  • University of Leeds (GB)
  • University of Bristol (GB)
  • The University of Queensland (AU)
Corresponding author
Smith, Andrew D. A. C.
ORCID
Venue
Epidemiology
Publisher
Ovid Technologies (Wolters Kluwer Health)
Published
2015-09
Volume
26
Issue
5
Pages
719-726
ISSN
1044-3983 (ISSN-L)
DOI
10.1097/ede.0000000000000348
PMC
PMC4521897
OpenAlex
W1653151873
Cited by
63
References
31
Citations by year
2026: 5 · 2025: 3 · 2024: 7 · 2023: 6 · 2022: 7 · 2021: 8 · 2020: 4 · 2019: 5 · 2018: 5 · 2017: 4 · 2016: 8 · 2015: 1
FWCI
5.34
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Epidemiology (publishedVersion, cc-by)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
  • Explore Bristol Research (publishedVersion)
  • White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (acceptedVersion)
  • UWE Research Repository (UWE Bristol) (submittedVersion, cc-by)
Funders
  • University College London
  • University of Bristol
  • Medical Research Council (MR/L01629X/1)
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
  • Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
Keywords
model selection, life course exposure, binary exposure, LASSO, socioeconomic position, body mass index, social mobility, critical period, repeated measures, causal inference, hypothesis testing
MeSH terms
Adult, Child, Preschool, Computer Simulation, Female, Humans, Male, Middle Aged, Models, Theoretical, Regression Analysis, Socioeconomic Factors, Cohort Studies, Epidemiologic Research Design, Causality, Body Mass Index

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