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Cure Fraction (cure + fraction)

Distribution by Scientific Domains
Mathematics and Statistics50%

Selected Abstracts

Improved survival time: What can survival cure models tell us about population-based survival improvements in late-stage colorectal, ovarian, and testicular cancer?,

CANCER, Issue 10 2008
Lan Huang PhD
Abstract BACKGROUND The objective of the current study was to investigate the long-term impact of treatment advances on the survival of patients with late-stage ovarian, colorectal (American Joint Committee on Cancer stage III, men), and testicular cancers by estimating the increase in the percentage cured from their disease and the change in survival time of uncured patients. METHODS Cause-specific survival data from 1973 to 2000 were obtained from the Surveillance, Epidemiology, and End Results Program. Survival cure models were fit and were used to estimate the gain in life expectancy (GLE) attributed to an increase in the fraction of cured patients and to prolonged survival among noncured patients. RESULTS Treatment improvement for ovarian cancer resulted in a total GLE of 2 years, and 80% of that GLE was because of an extension of survival time in uncured patients (from 0.9 years to 2.1 years) rather than an increased cure fraction (from 12% to 14%). In contrast, the cure rate rose from 29% to 47% for colorectal cancer, representing 82% of a 2.8-year GLE, and from 23% to 81% for testicular cancer, representing 100% of a 24-year GLE. CONCLUSIONS The current results suggested that treatment benefits for testicular and colorectal cancer in men with late-stage disease primarily are the result of increases in cure fraction, whereas survival gains for ovarian cancer occur despite persisting disease. Cure models, in combination with population-level data, provide insight into how treatment advances are changing survival and ultimately impacting mortality. Survival patterns reflect the underlying biology of response to cancer treatment and suggest promising directions for future research. Cancer 2008. Published 2008 by the American Cancer Society. [source]

Mixture cure survival models with dependent censoring

JOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES B (STATISTICAL METHODOLOGY), Issue 3 2007
Yi Li
Summary., The paper is motivated by cure detection among the prostate cancer patients in the National Institutes of Health surveillance epidemiology and end results programme, wherein the main end point (e.g. deaths from prostate cancer) and the censoring causes (e.g. deaths from heart diseases) may be dependent. Although many researchers have studied the mixture survival model to analyse survival data with non-negligible cure fractions, none has studied the mixture cure model in the presence of dependent censoring. To account for such dependence, we propose a more general cure model that allows for dependent censoring. We derive the cure models from the perspective of competing risks and model the dependence between the censoring time and the survival time by using a class of Archimedean copula models. Within this framework, we consider the parameter estimation, the cure detection and the two-sample comparison of latency distributions in the presence of dependent censoring when a proportion of patients is deemed cured. Large sample results by using martingale theory are obtained. We examine the finite sample performance of the proposed methods via simulation and apply them to analyse the surveillance epidemiology and end results prostate cancer data. [source]

Joint Models for Multivariate Longitudinal and Multivariate Survival Data

BIOMETRICS, Issue 2 2006
Yueh-Yun Chi
Summary Joint modeling of longitudinal and survival data is becoming increasingly essential in most cancer and AIDS clinical trials. We propose a likelihood approach to extend both longitudinal and survival components to be multidimensional. A multivariate mixed effects model is presented to explicitly capture two different sources of dependence among longitudinal measures over time as well as dependence between different variables. For the survival component of the joint model, we introduce a shared frailty, which is assumed to have a positive stable distribution, to induce correlation between failure times. The proposed marginal univariate survival model, which accommodates both zero and nonzero cure fractions for the time to event, is then applied to each marginal survival function. The proposed multivariate survival model has a proportional hazards structure for the population hazard, conditionally as well as marginally, when the baseline covariates are specified through a specific mechanism. In addition, the model is capable of dealing with survival functions with different cure rate structures. The methodology is specifically applied to the International Breast Cancer Study Group (IBCSG) trial to investigate the relationship between quality of life, disease-free survival, and overall survival. [source]