The Co-Twin Control Design: Implementation and Methodological Considerations

Establishing causal relationships in observational studies is an important step in research and policy decision making. The association between an exposure and an outcome can be confounded by multiple factors, often making it hard to draw causal conclusions. The co-twin control design (CTCD) is a powerful approach that allows for the investigation of causal effects while controlling for genetic and shared environmental confounding factors. This article introduces the CTCD and offers an overview of analysis methods for binary and continuous outcome and exposure variables. Tools for data simulation are provided, along with practical guidance and accompanying scripts for implementing the CTCD in R, SPSS, and Stata. While the CTCD offers valuable insights into causal inference, it depends on several assumptions that are important when interpreting CTCD results. By presenting a broad overview of the CTCD, this article aims to equip researchers with actionable recommendations and a comprehensive understanding of the design's strengths and limitations.

Continuity of Genetic Risk for Aggressive Behavior Across the Life-Course

We test whether genetic influences that explain individual differences in aggression in early life also explain individual differences across the life-course. In two cohorts from The Netherlands (N = 13,471) and Australia (N = 5628), polygenic scores (PGSs) were computed based on a genome-wide meta-analysis of childhood/adolescence aggression. In a novel analytic approach, we ran a mixed effects model for each age (Netherlands: 12-70 years, Australia: 16-73 years), with observations at the focus age weighted as 1, and decaying weights for ages further away. We call this approach a 'rolling weights' model. In The Netherlands, the estimated effect of the PGS was relatively similar from age 12 to age 41, and decreased from age 41-70. In Australia, there was a peak in the effect of the PGS around age 40 years. These results are a first indication from a molecular genetics perspective that genetic influences on aggressive behavior that are expressed in childhood continue to play a role later in life.

Family member incarceration and physical health problems: A longitudinal study among Australian households

This study examines the relationship between poor physical health and exposure to family member incarceration. Longitudinal data (2001–2015) from an Australian nationally representative household-based panel study was used (177,312 observations within 26,572 respondents). Hybrid random-effects models showed a strong correlation between poor physical health and family member imprisonment. However, this strong association can be explained for a large part by differences between individuals, since the association of physical health with within-individual changes in family member imprisonment was considerably lower. Nevertheless, the within-individual analyses showed that male sample members were significantly more likely to experience physical health problems in years in which they experienced family member imprisonment, compared to years in which they did not. This association was not found among females. Moreover, no effect of parental imprisonment on the physical health of young sample members was found.

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Imprisonment of a family member is strongly related to poor physical health.

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This association can be explained for a large part by time-constant confounders.

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Males have poorer health in years in which family members are imprisoned.

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Females do not have poorer health in years in which family members are imprisoned.

Big Five Personality Traits of Cybercrime Victims

The prevalence of cybercrime has increased rapidly over the last decades and has become part of the everyday life of citizens. It is, therefore, of great importance to gain more knowledge on the factors related to an increased or decreased likelihood of becoming a cybercrime victim. The current study adds to the existing body of knowledge using a large representative sample of Dutch individuals (N = 3,648) to study the relationship between cybercrime victimization and the key traits from the Big Five model of personality (i.e., extraversion, agreeableness, conscientiousness, emotional stability, and openness to experience). First, multinomial logistic regression analyses were used to examine the associations between the personality traits and three victim groups, that is, cybercrime victims versus nonvictims, traditional crime victims versus nonvictims, and cybercrime victims versus traditional crime victims. Next, logistic regression analyses were performed to predict victimization of cyber-dependent crimes (i.e., hacking and virus infection) and cyber-enabled crimes (i.e., online intimidation, online consumer fraud, and theft from bank account). The analyses show that personality traits are not specifically associated with cybercrime victimization, but rather with victimization in general. Only those with higher scores on emotional stability were less likely to become a victim of cybercrime than traditional crime. Furthermore, the results indicate that there are little differences between personality traits related to victimization of cyber-enabled and cyber-dependent crimes. Only individuals with higher scores on openness to experience have higher odds of becoming a victim of cyber-enabled crimes.