ArticleBritish journal of cancer2009
Prevalent cases in observational studies of cancer survival: do they bias hazard ratio estimates?
Article in British journal of cancer, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 4 of them syntheses that pooled it.
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Who cites it
47 citing papers in PubMed, 4 syntheses or guidelines pooled it.
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- A role for XRCC2 gene polymorphisms in breast cancer risk and survival.Journal of medical genetics · 2011Pooled it
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- Association between a germline OCA2 polymorphism at chromosome 15q13.1 and estrogen receptor-negative breast cancer survival.Journal of the National Cancer Institute · 2010Pooled it
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- Racial disparities in epithelial ovarian cancer survival: An examination of contributing factors in the Ovarian Cancer in Women of African Ancestry consortium.International journal of cancer · 2022Article
- Incorporating progesterone receptor expression into the PREDICT breast prognostic model.European journal of cancer (Oxford, England : 1990) · 2022Article
- Increased risk of being diagnosed with endometriosis in patients with Systemic lupus erythematosus: a population-based cohort study in Taiwan.Scientific reports · 2022Article
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- Independent effects of 15 commonly prescribed drugs on all-cause mortality among US elderly patients with type 2 diabetes mellitus.BMJ open diabetes research & care · 2020Article
- Mortality after breast cancer as a function of time since diagnosis by estrogen receptor status and age at diagnosis.International journal of cancer · 2019Article
- Body mass index and breast cancer survival: a Mendelian randomization analysis.International journal of epidemiology · 2017Article
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7 authors.
Funding
Abstract
Observational epidemiological studies often include prevalent cases recruited at various times past diagnosis. This left truncation can be dealt with in non-parametric (Kaplan-Meier) and semi-parametric (Cox) time-to-event analyses, theoretically generating an unbiased hazard ratio (HR) when the proportional hazards (PH) assumption holds. However, concern remains that inclusion of prevalent cases in survival analysis results inevitably in HR bias. We used data on three well-established breast cancer prognosticators - clinical stage, histopathological grade and oestrogen receptor (ER) status - from the SEARCH study, a population-based study including 4470 invasive breast cancer cases (incident and prevalent), to evaluate empirically the effectiveness of allowing for left truncation in limiting HR bias. We found that HRs of prognostic factors changed over time and used extended Cox models incorporating time-dependent covariates. When comparing Cox models restricted to subjects ascertained within six months of diagnosis (incident cases) to models based on the full data set allowing for left truncation, we found no difference in parameter estimates (P=0.90, 0.32 and 0.95, for stage, grade and ER status respectively). Our results show that use of prevalent cases in an observational epidemiological study of breast cancer does not bias the HR in a left truncation Cox survival analysis, provided the PH assumption holds true.
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