Triangulation for causal loop diagrams: constructing biopsychosocial models using group model building, literature review, and causal discovery
The complex nature of many health problems necessitates the use of systems thinking tools
like causal loop diagrams (CLDs) to visualize the underlying causal network and facilitate …
like causal loop diagrams (CLDs) to visualize the underlying causal network and facilitate …
Real‐world causal evidence for planned predictive enrichment in critical care trials: A scoping review
BS Kaas‐Hansen, A Granholm… - Acta …, 2024 - Wiley Online Library
Background Randomised clinical trials in critical care are prone to inconclusiveness due, in
part, to undue optimism about effect sizes and suboptimal accounting for heterogeneous …
part, to undue optimism about effect sizes and suboptimal accounting for heterogeneous …
Machine learning in causal inference for epidemiology
In causal inference, parametric models are usually employed to address causal questions
estimating the effect of interest. However, parametric models rely on the correct model …
estimating the effect of interest. However, parametric models rely on the correct model …
Machine-learning–based forecasting of dengue fever in Brazilian cities using epidemiologic and meteorological variables
K Roster, C Connaughton… - American Journal of …, 2022 - academic.oup.com
Dengue is a serious public health concern in Brazil and globally. In the absence of a
universal vaccine or specific treatments, prevention relies on vector control and disease …
universal vaccine or specific treatments, prevention relies on vector control and disease …
Mapping complex public health problems with causal loop diagrams
This paper presents causal loop diagrams (CLDs) as tools for studying complex public
health problems like health inequality. These problems often involve feedback loops—a …
health problems like health inequality. These problems often involve feedback loops—a …
Life course epidemiology
Life course epidemiology emerged in the 1990s as a response to the growing rift between
differing theoretical models of disease etiology. As such it attempted to synthesize existing …
differing theoretical models of disease etiology. As such it attempted to synthesize existing …
Constructing causal life-course models: Comparative study of data-driven and theory-driven approaches
AH Petersen, CT Ekstrøm, P Spirtes… - American Journal of …, 2023 - academic.oup.com
Life-course epidemiology relies on specifying complex (causal) models that describe how
variables interplay over time. Traditionally, such models have been constructed by perusing …
variables interplay over time. Traditionally, such models have been constructed by perusing …
Nighttime smartphone use, sleep quality, and mental health: investigating a complex relationship
Abstract Study Objectives This study investigated the complex relationship between
nighttime smartphone use, sleep, and mental health among adult populations in Denmark …
nighttime smartphone use, sleep, and mental health among adult populations in Denmark …
A longitudinal causal graph analysis investigating modifiable risk factors and obesity in a European cohort of children and adolescents
Childhood obesity is a complex disorder that appears to be influenced by an interacting
system of many factors. Taking this complexity into account, we aim to investigate the causal …
system of many factors. Taking this complexity into account, we aim to investigate the causal …
Invited commentary: where do the causal DAGS come from?
V Didelez - American Journal of Epidemiology, 2024 - academic.oup.com
How do we construct our causal directed acyclic graphs (DAGs)—for example, for life-course
modeling and analysis? In this commentary, I review how the data-driven construction of …
modeling and analysis? In this commentary, I review how the data-driven construction of …