Directed Acyclic Graphs in Oncology Research: Applications and Illustrated Example
Bibliographic record
Abstract
Directed acyclic graphs (DAG) are increasingly recognized as powerful tools in research and can be used for elucidating complex causal relationships inherent in cancer development, progression, and treatment outcomes. In oncology, in which multifactorial risk factors are the norm, the ability to visualize and interrogate these relationships is critical. In observational studies, DAGs offer a rigorous framework for identifying confounders, mediators, and colliders, allowing researchers to estimate causal effects. This study presents the development and application of a DAG in a real-world observational study involving skin cancer. We outline practical steps for constructing DAGs and discuss how to use them to select variables that help control for confounding. Our approach provides a valuable guide for oncologists, epidemiologists, and other cancer researchers aiming to enhance the transparency and validity of causal claims across a wide range of oncologic contexts-from prevention and early detection to survivorship and health disparities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".