ReviewCancers2022
A Causal Framework for Making Individualized Treatment Decisions in Oncology.
Review in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed.
- Advancing clinical trials for rare renal cell carcinoma subtypes: Consensus statements from the International Kidney Cancer Symposium North America 2025 think tank.Urologic oncology · 2026Article
- Machine learning methods for estimating personalized treatment effects-insights on validity from two large trials.American journal of epidemiology · 2026Article
- Review
- Individualized Triplet Chemotherapy Decision-Making in Metastatic Colorectal Cancer: A Machine-Learning-Driven Study.Cancers · 2025Article
- Reproducibility of statistically significant phase III oncology trials: An In Silico meta-epidemiological analysis.European journal of cancer (Oxford, England : 1990) · 2025Article
- Survival-inferred fragility of statistical significance in phase III oncology trials.NPJ precision oncology · 2025Article
- Incomplete Toxicity Reporting and Use of Toxicity-Minimizing Language in Phase III Oncology Trials.JCO oncology practice · 2025Article
- Real-world practitioner perceptions of CARTITUDE-4 results for patients with previously treated multiple myeloma.EJHaem · 2024Article
- Evidenced-Based Prior for Estimating the Treatment Effect of Phase III Randomized Trials in Oncology.JCO precision oncology · 2024Article
- Towards Treatment Effect Interpretability: A Bayesian Re-analysis of 194,129 Patient Outcomes Across 230 Oncology Trials.medRxiv : the preprint server for health sciences · 2024Article
- Risk-benefit trade-offs and precision utilities in phase I-II clinical trials.Clinical trials (London, England) · 2024Article
- Differential Treatment Effects of Subgroup Analyses in Phase 3 Oncology Trials From 2004 to 2020.JAMA network open · 2024Article
- Locoregional Therapies in Immunologically "Cold" Tumors: Opportunities and Clinical Trial Design Considerations.Journal of vascular and interventional radiology : JVIR · 2024Review
- Pathways for non-manufacturers to drive generic drug repurposing for cancer in the U.S.Frontiers in pharmacology · 2024Article
- Prevalence and implications of significance testing for baseline covariate imbalance in randomised cancer clinical trials: The Table 1 Fallacy.European journal of cancer (Oxford, England : 1990) · 2023Article
- Review
- Renal Cell Carcinoma of Variant Histology: Biology and Therapies.Hematology/oncology clinics of North America · 2023Review
- Interpreting Randomized Controlled Trials.Cancers · 2023Review
- Obesity and renal cell carcinoma: Biological mechanisms and perspectives.Seminars in cancer biology · 2023Review
- Less is More? First Impressions From COSMIC-313.Cancer investigation · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
We discuss how causal diagrams can be used by clinicians to make better individualized treatment decisions. Causal diagrams can distinguish between settings where clinical decisions can rely on a conventional additive regression model fit to data from a historical randomized clinical trial (RCT) to estimate treatment effects and settings where a different approach is needed. This may be because a new patient does not meet the RCT's entry criteria, or a treatment's effect is modified by biomarkers or other variables that act as mediators between treatment and outcome. In some settings, the problem can be addressed simply by including treatment-covariate interaction terms in the statistical regression model used to analyze the RCT dataset. However, if the RCT entry criteria exclude a new patient seen in the clinic, it may be necessary to combine the RCT data with external data from other RCTs, single-arm trials, or preclinical experiments evaluating biological treatment effects. For example, external data may show that treatment effects differ between histological subgroups not recorded in an RCT. A causal diagram may be used to decide whether external observational or experimental data should be obtained and combined with RCT data to compute statistical estimates for making individualized treatment decisions. We use adjuvant treatment of renal cell carcinoma as our motivating example to illustrate how to construct causal diagrams and apply them to guide clinical decisions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.