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

Salvatore Fasola, Laura Montalbano, Giovanna Cilluffo, Benjamin Cuer, Velia Malizia, Giuliana Ferrante, Isabella Annesi-Maesano and Stefania La Grutta
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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

IJERPH, 2021, vol. 18, issue 23, 1-15

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.

Keywords: children; exposome; genome; health; statistical methods; twin data (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
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