Abstract
Early-stage breast cancer (BC) is a curable disease with many patients dying of causes other than BC. The influence of non-BC death and other competing risks on the interpretation of Kaplan-Meier (KM)-based analyses for BC-specific outcomes are unknown. We searched the Oxford University website to identify all meta-analyses published by the Early Breast Cancer Trialists Collaborative Group (EBCTCG) between 2005 and 2018. The potential influence of competing risks was estimated using a validated multivariable linear model that predicts the difference between KM and cumulative incidence function (CIF) on estimates of BC-specific outcomes. The initial search identified 14 EBCTCG papers, 10 (71%) reported data on BC and competing events. Eight (80%) had a relative difference between KM and the competing risk adjusted estimates exceeding 10%. The median relative difference was 28.4% for local-recurrence; 16.8% for distant-recurrence, and 6.7% for BC-specific mortality. There was a 18.9% relative difference between KM and CIF adjusted analyses beyond 10 years. The use of KM-based methods when competing risks are present biases risk estimates in studies of early BC especially for uncommon outcomes such as local recurrence. The use of CIF to calculate BC-specific outcomes may be preferable in this setting.
Highlights
Www.nature.com/scientificreports result in a KM-based risk estimate of ~14%
We hypothesized that competing risk bias would be greater with longer follow-up and for analyses of less common endpoints and that over-estimation of breast cancer-specific events would result in biased estimates of treatment efficacy
As detailed in individual reports of analyses performed by the Early Breast Cancer Trialists Collaborative Group (EBCTCG), all estimates of recurrence and breast cancer-specific mortality are based on the complement of the Kaplan-Meier curve
Summary
Www.nature.com/scientificreports result in a KM-based risk estimate of ~14% (see Appendix A for survival table). A 10% relative difference in estimates of risk of disease-specific events between Kaplan-Meier and cumulative incidence function (CIF) has been identified as methodologically important[3]. This difference is common in the medical literature[9] with approximately one third of KM analyses overestimating the true risk of an outcome by more than 10%3. We hypothesized that competing risk bias would be greater with longer follow-up and for analyses of less common endpoints (e.g. local recurrence) and that over-estimation of breast cancer-specific events would result in biased estimates of treatment efficacy
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