Elucidating analytic bias due to informative cohort entry in cancer clinico-genomic datasets Journal Article
| Authors: | Kehl, K. L.; Uno, H.; Gusev, A.; Groha, S.; Brown, S.; Lavery, J. A.; Schrag, D.; Panageas, K. S. |
| Article Title: | Elucidating analytic bias due to informative cohort entry in cancer clinico-genomic datasets |
| Abstract: | BACKGROUND: Oncologists 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. METHODS: We 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. RESULTS: Scenario #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. CONCLUSIONS: Approaches 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. ©2023 American Association for Cancer Research. |
| Keywords: | neoplasm; neoplasms; genomics; bias; causality; humans; human; statistical bias |
| Journal Title: | Cancer Epidemiology Biomarkers and Prevention |
| Volume: | 32 |
| Issue: | 3 |
| ISSN: | 1055-9965 |
| Publisher: | American Association for Cancer Research |
| Date Published: | 2023-03-01 |
| Start Page: | 344 |
| End Page: | 352 |
| Language: | English |
| DOI: | 10.1158/1055-9965.Epi-22-0875 |
| PUBMED: | 36626408 |
| PROVIDER: | scopus |
| PMCID: | PMC9992002 |
| DOI/URL: | |
| Notes: | The MSK Cancer Center Support Grant (P30 CA008748) is acknowledged in the PDF -- Source: Scopus |
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