Long term survival analysis: the curability of breast cancer.
ABSTRACT Methods of survival analysis for long-term follow-up studies are illustrated by a study of mortality in 3878 breast cancer patients in Edinburgh followed for up to 20 years. The problems of life tables, advantages of hazard plots and difficulties in statistical modelling are demonstrated by studying the relationship between survival and both clinical stage and initial menopausal status at diagnosis. To assess the 'curability' of breast cancer, mortality by year of follow-up is compared with expected mortality using Scottish age-specific death rates. Techniques for analysing such relative survival data include age-corrected life tables, ratio of observed to expected deaths and excess death rates. Finally, an additive hazard model is developed to incorporate covariates in the analysis of relative survival and curability.
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ABSTRACT: Background:The ACCENT database, with individual patient data for 20 898 patients from 18 colon cancer clinical trials, was used to support Food and Drug Administration (FDA) approval of 3-year disease-free survival as a surrogate for 5-year overall survival. We hypothesised substantive differences in survival estimation with log-normal modelling rather than standard Kaplan-Meier or Cox approaches.Methods:Time to relapse, disease-free survival, and overall survival were estimated using Kaplan-Meier, Cox, and log-normal approaches for male subjects aged 60-65 years, with stage III colon cancer, treated with 5-fluorouracil-based chemotherapy regimens (with 5FU), or with surgery alone (without 5FU).Results:Absolute differences between Cox and log-normal estimates with (without) 5FU varied by end point. The log-normal model had 5.8 (6.3)% higher estimated 3-year time to relapse than the Cox model; 4.8 (5.1)% higher 3-year disease-free survival; and 3.2 (2.2)% higher 5-year overall survival. Model checking indicated greater data support for the log-normal than the Cox model, with Cox and Kaplan-Meier estimates being more similar. All three model types indicate consistent evidence of treatment benefit on both 3-year disease-free survival and 5-year overall survival; patients allocated to 5FU had 5.0-6.7% higher 3-year disease-free survival and 5.3-6.8% higher 5-year overall survival.Conclusion:Substantive absolute differences between estimates of 3-year disease-free survival and 5-year overall survival with log-normal and Cox models were large enough to be clinically relevant, and warrant further consideration.British Journal of Cancer advance online publication, 5 February 2013; doi:10.1038/bjc.2013.34 www.bjcancer.com.British Journal of Cancer 02/2013; · 5.08 Impact Factor
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ABSTRACT: The detection of disseminated tumor cells in bone marrow is a common phenomenon seen in 30-40% of primary breast cancer patients. The presence of disseminated tumor cells at diagnosis as well as the persistence of disseminated tumor cells is strongly associated with poor clinical outcome. Since bone marrow biopsies are not well tolerated by many patients, the evaluation of circulating tumor cells in the blood might become a desired alternative. Circulating tumor cells are routinely detected, depending on stage of the disease and methodology, in 10-80% of breast cancer patients. Recent studies have shown a prognostic potential of circulating tumor cells in both primary and metastatic settings. The evaluation of circulating tumor cells may become one of the crucial markers for prediction of survival and therapy monitoring, and its characterization might enable specific targeting of minimal residual, and metastatic disease.Biomarkers in Medicine 02/2012; 6(1):109-18. · 3.22 Impact Factor
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ABSTRACT: Survival data stand out as a special statistical field. This paper tries to describe what survival data is and what makes it so special. Survival data concerns times to some events. A key point is the successive observation of time, which on the one hand leads to sometimes not being observed so that all that is known is that they exceed some given times (censoring), and on the other hand implies that predictions regarding the future course should be conditional on the present status (truncation). In the simplest case, this condition is that the individual is alive. The successive conditioning makes the hazard function, which describes the probability of an event happening during a short interval given that the individual is alive today, the most relevant concept. Here we discuss parametric as well as non-parametric methods. Examples are presented in a way that can be followed without the help of computers.Indian journal of surgical oncology. 09/2012; 3(3):208-14.