ArticleCancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology2023
Elucidating Analytic Bias Due to Informative Cohort Entry in Cancer Clinico-genomic Datasets.
Article in Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Learning Patient Similarity from Genomics for Precision Oncology.medRxiv : the preprint server for health sciences · 2025Article
- Biomarker Inference and the Timing of Next-Generation Sequencing in a Multi-Institutional, Cross-Cancer Clinicogenomic Data Set.JCO precision oncology · 2024Article
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8 authors.
Funding
Abstract
backgroundOncologists often order genomic testing to inform treatment for worsening cancer. The resulting correlation between genomic testing timing and prognosis, or "informative entry," can bias observational clinico-genomic research. The efficacy of existing approaches to this problem in clinico-genomic cohorts is poorly understood.
methodsWe simulated clinico-genomic cohorts followed from an index date to death. Subgroups in each cohort who underwent genomic testing before death were "observed." We varied data generation parameters under four scenarios: (i) independent testing and survival times; (ii) correlated testing and survival times for all patients; (iii) correlated testing and survival times for a subset of patients; and (iv) testing and mortality exclusively following progression events. We examined the behavior of conditional Kendall tau (Tc) statistics, Cox entry time coefficients, and biases in overall survival (OS) estimation and biomarker inference across scenarios.
resultsScenario #1 yielded null Tc and Cox entry time coefficients and unbiased OS inference. Scenario #2 yielded positive Tc, negative Cox entry time coefficients, underestimated OS, and biomarker associations biased toward the null. Scenario #3 yielded negative Tc, positive Cox entry time coefficients, and underestimated OS, but biomarker estimates were less biased. Scenario #4 yielded null Tc and Cox entry time coefficients, underestimated OS, and biased biomarker estimates. Transformation and copula modeling did not provide unbiased results.
conclusionsApproaches to informative clinico-genomic cohort entry, including Tc and Cox entry time statistics, are sensitive to heterogeneity in genotyping and survival time distributions. IMPACT: Novel methods are needed for unbiased inference using observational clinico-genomic data.
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