Constructing Causal Life-Course Models: Comparative Study of Data-Driven and Theory-Driven Approaches.

Life-course epidemiology relies on specifying complex (causal) models that describe how variables interplay over time. Traditionally, such models have been constructed by perusing existing theory and previous studies. By comparing data-driven and theory-driven models, we investigated whether data-driven causal discovery algorithms can help in this process.

Exploratory data structure comparisons: three new visual tools based on principal component analysis.

Datasets are sometimes divided into distinct subsets, e.g. due to multi-center sampling, or to variations in instruments, questionnaire item ordering or mode of administration, and the data analyst then needs to assess whether a joint analysis is meaningful.

What Is the Causal Interpretation of Sibling Comparison Designs?

Sibling comparison designs have long been used to assess causal effects of exposures for which randomized studies are impossible and measurement of all relevant confounding is unobtainable. The idea is to utilize the fact that siblings often share a lot of unobserved variables. Therefore, it is proposed that in certain cases, comparing siblings is equivalent to comparing exchangeable individuals, which is the foundation for causal inference based on randomized controlled trials (RCTs).

Evaluation of adding the community reinforcement approach to motivational enhancement therapy for adults aged 60 years and older with DSM-5 alcohol use disorder: a randomized controlled trial.

Aim: To examine whether adding the Community Reinforcement Approach for Seniors (CRA-S) to Motivational Enhancement Therapy (MET) increases the probability of treatment success in people aged ≥ 60 years with alcohol use disorder (AUD).

Design: A single blind multi-centre multi-national randomized (1 : 1) controlled trial.

Setting: Out-patient settings (municipal alcohol treatment clinics in Denmark, specialized addiction care facilities in Germany and a primary care clinic in the United States).

Relation Between Infant Microbiota and Autism?: Results from a National Cohort Sibling Design Study.

Background: Hypotheses concerning adverse effects of changes in microbiota have received much recent attention, but unobserved confounding makes them difficult to test. We investigated whether surrogate markers for potential adverse microbiota change in infancy affected autism risk, addressing unobserved confounding using a sibling study design.

Methods: This is a population-based, prospective cohort study including all singleton live births in Denmark from 1997 to 2010.

Investigating the effects of cesarean delivery and antibiotic use in early childhood on risk of later attention deficit hyperactivity disorder.

Background: Increasing attention deficit hyperactivity disorder (ADHD) incidence has been proposed to be caused by factors influencing microbiota in early life. We investigated the potential causality between ADHD and two surrogate markers for changes in children's microbiota: birth delivery mode and early childhood antibiotic use.

Method: This population-based, prospective cohort study linked nationwide registers of data for native Danish singleton live births in Denmark from 1997 to 2010.