ArticleJCO clinical cancer informatics2023
Estimating Treatment Effect of Adjuvant Chemotherapy in Elderly Patients With Stage III Colon Cancer Using Bayesian Networks.
Article in JCO clinical cancer informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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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.
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Who cites it
3 citing papers in PubMed, 4 citations in OpenAlex.
- Added value of adjuvant chemotherapy in patients with node-positive pT1-2 colon cancer: a national SNAPSHOT analysis.The oncologist · 2026Article
- Tablet Acceptability in Older Outpatients Undergoing Cancer Chemotherapy.Geriatrics (Basel, Switzerland) · 2026Article
- Bayesian networks as prognostic models in oncology: a systematic review and recommendations for clinical practice.BMJ oncology · 2026Article
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeWhile adjuvant therapy with capecitabine and oxaliplatin (CAPOX) has been proven to be effective in stage III colon cancer, capecitabine monotherapy (CapMono) might be equally effective in elderly patients. Unfortunately, the elderly are under-represented in clinical trials and patients included may not be representative of the routine care population. Observational data might alleviate this problem but is sensitive to biases such as confounding by indication. Here, we build causal models using Bayesian Networks (BNs), identify confounders, and estimate the effect of adjuvant chemotherapy using survival analyses.
methodsPatients 70 years and older were selected from the Netherlands Cancer Registry (N = 982). We developed several BNs using constraint-based, score-based, and hybrid algorithms while precluding noncausal relations. In addition, we created models using a limited set of recurrence and survival nodes. Potential confounders were identified through the resulting graphs. Several Cox models were fitted correcting for confounders and for propensity scores.
resultsWhen comparing adjuvant treatment with surgery only, pathological lymph node classification, physical status, and age were identified as potential confounders. Adjuvant treatment was significantly associated with survival in all Cox models, with hazard ratios between 0.39 and 0.45; CIs overlapped. BNs investigating CAPOX versus CapMono did not find any association between the treatment choice and survival and thus no confounders. Analyses using Cox models did not identify significant association either.
conclusionWe were able to successfully leverage BN structure learning algorithms in conjunction with clinical knowledge to create causal models. While confounders differed depending on the algorithm and included nodes, results were not contradictory. We found a strong effect of adjuvant therapy on survival in our cohort. Additional oxaliplatin did not have a marked effect and should be avoided in elderly patients.
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