2017
DOI: 10.1001/jamapsychiatry.2017.0502
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Causal Inference in Psychiatric Epidemiology

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Cited by 27 publications
(20 citation statements)
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“…To date, observational studies examining the impact of ACEs have used a range of multivariate statistical techniques to control for measured covariates that are associated with both ACEs and examined outcomes to provide less biased estimates of the direct effect of ACEs. The inclusion of covariates increases the likelihood that observed associations will not be the product of “backdoor paths” (D’Onofrio et al, 2016), where common causes partly or entirely explain the observed link and lead to a spurious association (Kendler, 2017). As such, measured covariates are included into multivariate statistical models to block backdoor paths and increase confidence in reported findings.…”
Section: Conceptual Models For the Relationship Between Aces Antisocmentioning
confidence: 99%
“…To date, observational studies examining the impact of ACEs have used a range of multivariate statistical techniques to control for measured covariates that are associated with both ACEs and examined outcomes to provide less biased estimates of the direct effect of ACEs. The inclusion of covariates increases the likelihood that observed associations will not be the product of “backdoor paths” (D’Onofrio et al, 2016), where common causes partly or entirely explain the observed link and lead to a spurious association (Kendler, 2017). As such, measured covariates are included into multivariate statistical models to block backdoor paths and increase confidence in reported findings.…”
Section: Conceptual Models For the Relationship Between Aces Antisocmentioning
confidence: 99%
“…Thus, our sample offers more refined family and individual-level data than samples drawn from larger medical registries that have been used to examine similar questions. A strength of this design is that causality can be ruled out when associations are attributable to familial confounding, and any within-family associations can be concluded as potentially causal (e.g., causality has not been ruled out, but neither has it been confirmed, as other non-familial confounders could still explain the association; Kendler, 2017). We hypothesized that SDP-ADHD behavior associations will be explained by familial confounds when assessed using a DSM-based symptom measure (Estabrook et al, 2015; Lindblad & Hjern, 2010; Obel et al, 2015; Skoglund et al, 2014; Thapar et al, 2009), but that a within-family, potentially causal effect will be found when ADHD behaviors are assessed using the SWAN, specifically for hyperactivity/impulsivity (Knopik, Marceau, Bidwell, et al, 2016).…”
Section: Present Studymentioning
confidence: 99%
“…In this study, we carried out a stringent test of these non-causal interpretations, capitalizing on design and analytical features with complementary strengths. To account for family-wide factors, we used a co-twin control design 20 to test whether adolescents with the same genotype and rearing environment—but different exposure to adolescent victimization—had a different risk for self-injurious thoughts and behaviors. To account for individual factors, we used propensity score matching 21 to test whether adolescents with a similar individual propensity to experience victimization—but different exposure to adolescent victimization—had a different risk for self-injurious thoughts and behaviors.…”
mentioning
confidence: 99%