2010
DOI: 10.1002/cncr.25370
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Predictors of competing mortality in early breast cancer

Abstract: BACKGROUND: Death in the absence of disease recurrence (competing mortality) is an important determinant of disease-free survival (DFS) in early breast cancer. The authors sought to identify predictors of this event using competing risks modeling. METHODS: A cohort study was made of 1231 consecutive women with stage I to II invasive breast cancer diagnosed between 1986 and 2004, treated with breast conservation therapy. Median follow-up was 82 months. The authors used a parametric competing risks regression mo… Show more

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Cited by 39 publications
(25 citation statements)
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“…The same applies to mortality, particularly in subgroups at high risk of non‐breast cancer death, which is not influenced by the intervention to the same degree as breast cancer‐specific mortality. Inclusion of all‐cause mortality in an endpoint can therefore distort the results. Finally, the incidence of the more and less important components should be comparable.…”
Section: Discussionmentioning
confidence: 99%
“…The same applies to mortality, particularly in subgroups at high risk of non‐breast cancer death, which is not influenced by the intervention to the same degree as breast cancer‐specific mortality. Inclusion of all‐cause mortality in an endpoint can therefore distort the results. Finally, the incidence of the more and less important components should be comparable.…”
Section: Discussionmentioning
confidence: 99%
“…Relative to the US population, survivors experience excess morbidity and mortality due to cardiac and vascular abnormalities and pulmonary complications (Choi et al, 2011; Mariotto et al, 2007; Oeffinger and Tonorezos, 2011; Siegel et al, 2012a; Valdivieso et al, 2012). This landscape highlights an opportunity to use PNI paradigms to understand cancer from a competing risk perspective in which multiple factors concurrently affect risks for morbidity and mortality (Mell et al, 2010; Schairer et al, 2004). Although not consistently observed (Zucca et al, 2012), age at diagnosis, general life expectancy trends, and long-term physiological sequelae of treatment exposure have converged to increase the prevalence of co-morbidity or multmorbidity 4 in a cancer context (Braithwaite et al, 2012; Land et al, 2012; Patnaik et al, 2011; Ritchie et al, 2011; Yood et al, 2012).…”
Section: Shifting Sands Of Survivorshipmentioning
confidence: 99%
“…General US life tables (6) have limited ability to predict life expectancy for cancer patients (7). Tools have been developed to calculate life expectancy adjusted for health status; however, most of these are specific to a cancer site, stage or particular treatment and are not derived from population-based data (714). Better prediction tools are needed to more precisely estimate the risk of death due to other health-related conditions (15).…”
Section: Introductionmentioning
confidence: 99%