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

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

## 3. Results

#### 3.1. ACE Models

#### 3.2. GLMMs

_{3}were significantly associated with developmental delays at 8, 12, 18, 24, 30, and 36 months (RR = 1.014 to 1.173). Moreover, they found that postnatal exposures to PM

_{2.5}were significantly associated with personal-social developmental delays up to 18 months (RR = 1.052 to 1.120). Finally, model (3) was used by Jackson et al. [24] to confirm the results obtained through a different method.

_{3}was significantly associated with communication delays (RR = 1.025) between 8 and 36 months.

#### 3.3. GLMs with Fixed Pair Effects

_{1}(OR = 5.57) in twins aged 9–16 years. In children up to 18 years, Li et al. [28] found an increased risk of ever being obese associated with untreated infection in the first year of life compared with no infection (OR = 1.55). Finally, in children aged 3–10 years, Slob et al. [30] found that antibiotic use in the first 2 years of life was consistently associated with an increased risk of asthma in two cohorts (OR = 1.54 and OR = 2.00), while inconsistent results were found in the two cohorts for eczema (OR = 0.99 and OR = 1.67).

#### 3.4. Within-Pair Difference Analyses

#### 3.5. Paired-Sample Tests

#### 3.6. GEE Models

_{10}and NO

_{x}during pregnancy, and during the first/ninth year of life. In children aged 18–22 months, Boghossian et al. [36] found that antenatal corticosteroids (ANS) were associated with a lower risk of neurodevelopmental impairment (NDI) or death among non-small for gestational age infants (RR = 0.89), and a higher risk among small for gestational age infants (RR = 1.62). Moreover, ANS were associated with a higher risk of NDI/death among infants of mothers with diabetes (RR = 1.55). In children aged 3–7 years, Johnson et al. [38] found positive associations between BMI and the frequency of maternal non-verbal encouragements/discouragements and temporary discouragements during a laboratory meal. Using GEE models, Yeung et al. [26] and Slob et al. [30] found similar results than in their conditional analyses (GLMMs and conditional logistic regression, respectively).

#### 3.7. GLMs with Cluster-Robust Standard Errors

_{1}($\mathsf{\beta}=-0.16$) and abnormal FEV

_{1}(OR = 1.27). In children aged 9–12 years, Castellheim et al. [39] found that lifetime general anesthesia was significantly associated with ADHD scores ($\mathsf{\beta}=1.02$). Finally, Leong et al. [31] found that antibiotic use was associated with increasing BMI ($\mathsf{\beta}=0.018$) and obesity (OR = 1.09) in children aged 4 years.

#### 3.8. GLMs

#### 3.9. Independent-Sample Tests

## 4. Discussion

## 5. Conclusions

## Supplementary Materials

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Flow diagram showing the study selection process. * Reasons for exclusion: reviews (n = 2), abstracts (n = 11), statistical analyses included singletons and twins together (n = 1), different outcomes (n = 3), twin mothers (n = 1), adult twins (n = 1), and no statistical analyses (n = 1).

**Figure 2.**Summary of recommendations regarding which types of models researchers should favor under different conditions (research needs, data structure, and desired parameter meaning).

^{1}Conditional logistic regression is recommended.

^{2}Accommodated for through the use of dummy variables.

^{3}Either the outcome or the exposure mustable t be binary.

^{4}Only in models with identity link function and in log-linear models.

Class of Methods | Strengths | Limitations | R Libraries |
---|---|---|---|

ACE models | - -
- Confounders can be included
- -
- Optimal inference
- -
- Shared confounders are adjusted for by design
- -
- Genetic contribution can be estimated
| - -
- Require adaptations for binary outcomes and repeated measurements
| umx lavaan OpenMx |

Generalized linear mixed models (GLMMs) | - -
- Suitable for binary outcomes
- -
- Confounders can be included
- -
- Optimal inference
- -
- Shared confounders are adjusted for by design
- -
- Suitable for repeated measurements
| - -
- Genetic contribution cannot be estimated
| lme4 nlme MASS |

GLMs with fixed pair effects | - -
- Suitable for binary outcomes
- -
- Individual-level confounders can be included
- -
- Shared confounders are adjusted for by design
| - -
- Shared confounders cannot be included
- -
- Estimators may be sub-optimal
- -
- Unsuitable for repeated measurements
- -
- Genetic contribution cannot be estimated
| stats |

Within-pair difference analyses | - -
- Individual-level confounders can be included
- -
- Optimal inference
- -
- Shared confounders are adjusted for by design
| - -
- Unsuitable for binary outcomes
- -
- Shared confounders cannot be included
- -
- Unsuitable for repeated measurements
- -
- Genetic contribution cannot be estimated
| stats |

Paired-sample tests | - -
- Optimal inference
- -
- Shared confounders are adjusted for by design
| - -
- Require a binary exposure
- -
- Require adaptations for binary outcomes
- -
- Confounders cannot be included
- -
- Unsuitable for repeated measurements
- -
- Genetic contribution cannot be estimated
| stats |

Generalized estimating equations (GEE) models | - -
- Suitable for binary outcomes
- -
- Confounders can be included
- -
- Optimal inference
| - -
- Shared confounders are not adjusted for by design
- -
- Require adaptations for repeated measurements
- -
- Genetic contribution cannot be estimated
| gee geepack |

Generalized linear models (GLMs) with cluster-robust standard errors | - -
- Suitable for binary outcomes
- -
- Confounders can be included
- -
- Optimal standard error estimators
| - -
- Sub-optimal effect-size estimators
- -
- Shared confounders are not adjusted for by design
- -
- Unsuitable for repeated measurements
- -
- Genetic contribution cannot be estimated
| sandwich |

Generalized linear models (GLMs) | - -
- Suitable for binary outcomes
- -
- Confounders can be included
| - -
- Sub-optimal inference
- -
- Shared confounders are not adjusted for by design
- -
- Unsuitable for repeated measurements
- -
- Genetic contribution cannot be estimated
| stats |

Independent-sample tests | - -
- Suitable for binary outcomes
| - -
- Require a binary exposure
- -
- Confounders cannot be included
- -
- Sub-optimal inference
- -
- Shared confounders are not adjusted for by design
- -
- Unsuitable for repeated measurements
- -
- Genetic contribution cannot be estimated
| stats |

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## Share and Cite

**MDPI and ACS Style**

Fasola, S.; Montalbano, L.; Cilluffo, G.; Cuer, B.; Malizia, V.; Ferrante, G.; Annesi-Maesano, I.; La Grutta, S.
A Critical Review of Statistical Methods for Twin Studies Relating Exposure to Early Life Health Conditions. *Int. J. Environ. Res. Public Health* **2021**, *18*, 12696.
https://doi.org/10.3390/ijerph182312696

**AMA Style**

Fasola S, Montalbano L, Cilluffo G, Cuer B, Malizia V, Ferrante G, Annesi-Maesano I, La Grutta S.
A Critical Review of Statistical Methods for Twin Studies Relating Exposure to Early Life Health Conditions. *International Journal of Environmental Research and Public Health*. 2021; 18(23):12696.
https://doi.org/10.3390/ijerph182312696

**Chicago/Turabian Style**

Fasola, Salvatore, Laura Montalbano, Giovanna Cilluffo, Benjamin Cuer, Velia Malizia, Giuliana Ferrante, Isabella Annesi-Maesano, and Stefania La Grutta.
2021. "A Critical Review of Statistical Methods for Twin Studies Relating Exposure to Early Life Health Conditions" *International Journal of Environmental Research and Public Health* 18, no. 23: 12696.
https://doi.org/10.3390/ijerph182312696