Causal diagrams for research about childhood-onset disabilities.

Marginal Structural Models, Doubly Robust Estimation, and Bias Analysis in Perinatal and Paediatric Epidemiology

Directed acyclic graphs: A tool to identify confounders in orthodontic research, Part II.

BackgroundCompositional data (CD) comprise the parts of some whole, for which all parts sum to that whole; the whole may vary across individual units of analysis or remain fixed. Such...

Adjusting for Selection Effects in Epidemiologic Studies

Although some studies document that posttraumatic stress disorder (PTSD) increases suicide risk, other studies have produced the paradoxical finding that PTSD decreases suicide risk. We sought to understand methodologic biases that may explain these paradoxical findings through the use of directed acyclic graphs (DAGs). DAGs are causal diagrams that visually encode a researcher's assumptions about data generating mechanisms and assumed causal relations among variables. DAGs can connect theories to data and guide statistical choices made in study design and analysis. In this article, we describe DAGs and explain how they can be used to identify biases that may arise from inappropriate analytic decisions and data limitations. We define a particular form of bias, collider bias, that is a likely explanation for why studies have found a supposedly protective association of PTSD with suicide. This protective association is interpreted by some researchers as evidence that PTSD reduces the risk of suicide. Collider bias may occur through inappropriate adjustment for a psychiatric comorbidity, such as adjustment for variables that are affected by PTSD and share common causes with suicide. We recommend that researchers collect longitudinal measurements of psychiatric comorbidities, which would help establish the temporal ordering of variables and avoid the biases discussed in this article. Furthermore, researchers could use DAGs to explore how results may be impacted by design and analytic decisions prior to execution. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

When is a confounder not a confounder?

BackgroundCausal inference methods are increasingly popular in health research, with directed acyclic graphs (DAGs) being notably prominent. Theoretically, DAGs are powerful tools for minimising bias in quantitative analysis, however their...

Compartmental model diagrams have been used for nearly a century to depict causal relationships in infectious disease epidemiology. Causal directed acyclic graphs (DAGs) have been used more broadly in epidemiology since the 1990s to guide analyses of a variety of public health problems. Using an example from chronic disease epidemiology, the effect of type 2 diabetes on dementia incidence, we illustrate how compartmental model diagrams can represent the same concepts as causal DAGs, including causation, mediation, confounding, and collider bias. We show how to use compartmental model diagrams to explicitly depict interaction and feedback cycles. While DAGs imply a set of conditional independencies, they do not define conditional distributions parametrically. Compartmental model diagrams parametrically (or semiparametrically) describe state changes based on known biological processes or mechanisms. Compartmental model diagrams are part of a long-term tradition of causal thinking in epidemiology and can parametrically express the same concepts as DAGs, as well as explicitly depict feedback cycles and interactions. As causal inference efforts in epidemiology increasingly draw on simulations and quantitative sensitivity analyses, compartmental model diagrams may be of use to a wider audience. Recognizing simple links between these two common approaches to representing causal processes may facilitate communication between researchers from different traditions.

Background/Objectives: Directed acyclic graphs (DAGs) inform the epidemiologic statistical modeling confounders to determine close to true causal relationships in a study context. They inform the inclusion of the predictive model variables that affect the causal relationship. Non-small cell lung cancer (NSCLC) is frequently diagnosed, aggressive, and the second leading cause of cancer deaths in the United States. Determining factors affecting both the guideline-concordant treatment receipt and survival outcomes for early-stage lung cancer will help inform future statistical models aiming to achieve a close to true causal relationship. Methods: Peer-reviewed original research published during 2002–2023 was identified through PubMed, Embase, Web of Sciences, Clinical trials registry, and the gray literature. DAGitty version 3.1, an online software program, developed implied DAGs and integrated DAG graphics. The evidence synthesis for constructing directed acyclic graphs (ESC-DAGs) protocol was utilized to guide DAG development. The conceptual models utilized were Andersen and Aday for factors affecting treatment receipt and Shi and Steven for survival outcome factors. Results: A total of 36 studies were included in the DAG synthesis out of 9421 retrieved across databases. Eight studies served in the synthesis of treatment receipt DAG, while 28 studies were used for the survival outcomes DAG. There were 10 causal paths and 13 covariates for treatment receipt and 2 causal pathways and 32 covariates for survival outcomes. Conclusions: There are very few studies reporting on factors affecting early-stage NSCLC guideline-concordant care receipt compared to factors affecting its survival outcomes in the past two decades of original research. Future investigations can utilize data extracted in the current study to develop a meta-analysis informing effect size.

Aim Central venous catheters (CVCs) are essential for long-term therapies but carry a high risk of central line-associated bloodstream infections (CLABSIs), which significantly impact patient outcomes and healthcare costs. This study aimed to develop a causal model for CLABSI using expert knowledge to guide future clinical trials and prevention strategies. Methods We constructed a directed acyclic graph (DAG) informed by literature and expert knowledge elicitation. A multidisciplinary team of clinicians, including infectious disease and vascular access experts, participated in interviews and workshops to refine the DAG, resulting in a final model with 30 variables representing CLABSI development. Findings The expert-elicited DAG identified two main pathways, patient-related and CVC-related, each contributing to CLABSI risk. Variables and relationships in the DAG highlighted key patient characteristics, CVC management practices, and overlapping factors influencing infection. This model serves as a novel framework to understand CLABSI causation and supports trial design by identifying confounding factors, causal pathways, and meaningful endpoints. Conclusions/implications Our causal DAG provides a structured representation of CLABSI risk factors, which may support the design of clinical trials examining interventions to reduce CVC-related infections. By clarifying causal mechanisms, the DAG can enhance the specificity of endpoints and improve the rigor of prevention strategies.

The Directed Acyclic Graph (DAG) is a graph representing causal pathways for informing the conduct of an observational study. The use of DAGs allows transparent communication of a causal model between researchers and can prevent over-adjustment biases when conducting causal inference, permitting greater confidence and transparency in reported causal estimates. In the era of ‘big data’ and increasing number of observational studies, the role of the DAG is becoming more important. Recent best-practice guidance for constructing a DAG with reference to the literature has been published in the ‘Evidence synthesis for constructing DAGs’ (ESC-DAG) protocol. We aimed to assess adherence to these principles for DAGs constructed within perioperative literature. Following registration on the International Prospective Register of Systematic Reviews (PROSPERO) and with adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting framework for systematic reviews, we searched the Excerpta Medica dataBASE (Embase), the Medical Literature Analysis and Retrieval System Online (MEDLINE) and Cochrane databases for perioperative observational research incorporating a DAG. Nineteen studies were included in the final synthesis. No studies demonstrated any evidence of following the mapping stage of the protocol. Fifteen (79%) fulfilled over half of the translation and integration one stages of the protocol. Adherence with one stage did not guarantee fulfilment of the other. Two studies (11%) undertook the integration two stage. Unmeasured variables were handled inconsistently between studies. Only three (16%) studies included unmeasured variables within their DAG and acknowledged their implication within the main text. Overall, DAGs that were constructed for use in perioperative observational literature did not consistently adhere to best practice, potentially limiting the benefits of subsequent causal inference. Further work should focus on exploring reasons for this deviation and increasing methodological transparency around DAG construction.

Pain states fluctuate over time and across situations. Similarly, there is variation in risk and protective factors and how they impact on these pain-related transitions. We are interested in whether such variations are more than random, and whether they can be accounted for by observed variables. The availability of large longitudinal datasets, such as UK Biobank (https://www.ukbiobank.ac.uk/), offers a unique opportunity to study these variations at scale. However, such datasets bring a high risk of bias (eg, confounding) and danger of over-interpretation. It is therefore important to be transparent about our causal thinking. Directed acyclic graphs (DAGs) are graphical representations of the hypothesized causal relationships between variables. They are used to identify the smallest set of variables that need to be adjusted to remove confounding bias in estimating the causal effect of an exposure on an outcome. However, use of DAGs in pain research is not common, despite their potential to guide study design and data analysis. In this article, we present a workflow for building a DAG using domain knowledge from 3 different sources: researchers (theory-based), people with lived experience (person-based), and the literature (evidence-based). We created a DAG for the putative effect of executive function on the maintenance of chronic high-impact pain. The resulting DAG provides a valuable framework for guiding future research on the role of executive functioning in pain, and it underscores the broader potential of using DAGs to improve causal inference in pain research.

PurposePersons with childhood-onset disabilities are among the most marginalized populations, often unemployed or underemployment in jobs providing neither adequate hours for financial self-sufficiency nor fulfillment through skill-utilization. The purpose of this paper is to examine the extent to which social capital in the form of strong ties with family and friends is associated with enhanced employment outcomes for persons with childhood-onset disabilities.Design/methodology/approachQuestioning the current theoretical consensus that strong social ties are unimportant to employment quality, the authors draw on disability research and opportunity, motivation and ability social capital theory to propose a model of the impact of strong ties with family and friends on paid-work-hours and skill-utilization as well as the potential moderating role of gender and disability severity. The authors then test this model using data from 1,380 people with childhood-onset disabilities and OLS regression analysis.FindingsAs theorized, family-of-origin-size is positively associated with hours worked. Family-of-origin-size is also associated with having more close friends and children. These strong ties, in turn, are positively associated with hours worked. The impact of having more children on hours worked and skill-utilization, however, is positive for men but non-significant for women.Originality/valueThis study breaks new ground by focusing on the association between strong ties with family and friends and employment quality for people with childhood-onset disabilities – a marginalized and understudied group. Findings further indicate the particular vulnerability of women with disabilities.

BackgroundTo estimate the causal relationship between two variables it is important to first establish the potential causal pathways and to consider causes of bias. Omission of key confounders from the...

Ecologists often rely on randomized control trials (RCTs) to quantify causal relationships in nature. Many of our foundational insights of ecological phenomena can be traced back to well-designed experiments, and RCTs continue to provide valuable insights today. Although RCTs are often regarded as the "gold standard" for causal inference, it is important to recognize that they too rely on a set of causal assumptions that must be justified and met by the researcher to draw valid causal conclusions. We use key ecological examples to show how biases such as confounding, overcontrol, and collider bias can occur in experimental setups. In tandem, we highlight how such biases can be removed through the application of the structural causal model (SCM) framework. The SCM framework visualizes the causal structure of a system or process under study using directed acyclic graphs (DAGs) and subsequently applies a set of graphical rules to remove bias from both observational and experimental data. We show how DAGs can be applied across ecological experimental studies to ensure proper study design and statistical analysis, leading to more accurate causal estimates drawn from experimental data. Although causal conclusions drawn from RCTs are often taken at face value, ecologists are increasingly becoming aware that experimental approaches must be carefully designed and analyzed to avoid potential biases. By applying DAGs as a visual and conceptual tool, experimental ecologists can increasingly meet the causal assumptions required for valid causal inference.