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A Critical Review of Statistical Methods for Twin Studies Relating Exposure to Early Life Health Conditions

Author

Listed:
  • Salvatore Fasola

    (Institute for Biomedical Research and Innovation, National Research Council, 90146 Palermo, Italy)

  • Laura Montalbano

    (Institute for Biomedical Research and Innovation, National Research Council, 90146 Palermo, Italy)

  • Giovanna Cilluffo

    (Institute for Biomedical Research and Innovation, National Research Council, 90146 Palermo, Italy)

  • Benjamin Cuer

    (Institute Desbrest of Epidemiology and Public Health, Inserm and University of Montpellier, 34093 Montpellier, France)

  • Velia Malizia

    (Institute for Biomedical Research and Innovation, National Research Council, 90146 Palermo, Italy)

  • Giuliana Ferrante

    (Department of Surgical Sciences, Dentistry, Gynecology and Pediatrics, Pediatric Division, University of Verona, 37134 Verona, Italy)

  • Isabella Annesi-Maesano

    (Institute Desbrest of Epidemiology and Public Health, Inserm and University of Montpellier, 34093 Montpellier, France)

  • Stefania La Grutta

    (Institute for Biomedical Research and Innovation, National Research Council, 90146 Palermo, Italy)

Abstract

When investigating disease etiology, twin data provide a unique opportunity to control for confounding and disentangling the role of the human genome and exposome. However, using appropriate statistical methods is fundamental for exploiting such potential. We aimed to critically review the statistical approaches used in twin studies relating exposure to early life health conditions. We searched PubMed, Scopus, Web of Science, and Embase (2011–2021). We identified 32 studies and nine classes of methods. Five were conditional approaches (within-pair analyses): additive-common-erratic (ACE) models (11 studies), generalized linear mixed models (GLMMs, five studies), generalized linear models (GLMs) with fixed pair effects (four studies), within-pair difference analyses (three studies), and paired-sample tests (two studies). Four were marginal approaches (unpaired analyses): generalized estimating equations (GEE) models (five studies), GLMs with cluster-robust standard errors (six studies), GLMs (one study), and independent-sample tests (one study). ACE models are suitable for assessing heritability but require adaptations for binary outcomes and repeated measurements. Conditional models can adjust by design for shared confounders, and GLMMs are suitable for repeated measurements. Marginal models may lead to invalid inference. By highlighting the strengths and limitations of commonly applied statistical methods, this review may be helpful for researchers using twin designs.

Suggested Citation

  • Salvatore Fasola & Laura Montalbano & Giovanna Cilluffo & Benjamin Cuer & Velia Malizia & Giuliana Ferrante & Isabella Annesi-Maesano & Stefania La Grutta, 2021. "A Critical Review of Statistical Methods for Twin Studies Relating Exposure to Early Life Health Conditions," IJERPH, MDPI, vol. 18(23), pages 1-15, December.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:23:p:12696-:d:693294
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    References listed on IDEAS

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    4. Petkovsek, Melissa A. & Boutwell, Brian B. & Beaver, Kevin M. & Barnes, J.C., 2014. "Prenatal smoking and genetic risk: Examining the childhood origins of externalizing behavioral problems," Social Science & Medicine, Elsevier, vol. 111(C), pages 17-24.
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