Cure Models as a Useful Statistical Tool for Analyzing Survival

Fred Hutchinson Cancer Research Center, Seattle, WA 98117, USA.
Clinical Cancer Research (Impact Factor: 8.72). 06/2012; 18(14):3731-6. DOI: 10.1158/1078-0432.CCR-11-2859
Source: PubMed


Cure models are a popular topic within statistical literature but are not as widely known in the clinical literature. Many patients with cancer can be long-term survivors of their disease, and cure models can be a useful tool to analyze and describe cancer survival data. The goal of this article is to review what a cure model is, explain when cure models can be used, and use cure models to describe multiple myeloma survival trends. Multiple myeloma is generally considered an incurable disease, and this article shows that by using cure models, rather than the standard Cox proportional hazards model, we can evaluate whether there is evidence that therapies at the University of Arkansas for Medical Sciences induce a proportion of patients to be long-term survivors.

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    • "However, they have not received much attention in the medical field until recently. When a long-term effect is expected, cure rate models can be a useful tool to design [34, 35] or to analyze and describe time-to-event data363738 . It is now recognized that clinical trial designs and analyses need to be tailored to the emerging early evidence and increasing knowledge from new therapies [35, 39]. "
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    ABSTRACT: Background A new class of immuno-oncology agents has recently been shown to induce long-term survival in a proportion of treated patients. This phenomenon poses unique challenges for the prediction of analysis time in event-driven studies. If the phenomenon of long-term survival is not accounted for properly, the accuracy of the prediction based on the existing methods may be substantially compromised. Methods Parametric mixture cure rate models with the best fit to empirical clinical trial data were proposed to predict analysis times in immuno-oncology studies during the course of the study. The proposed prediction procedure also accounts for the mechanism of action introduced by cancer immunotherapies, such as delayed and long-term survival effects. Results The proposed methodology was retrospectively applied to a randomized phase III immuno-oncology clinical trial. Among various parametric mixture cure rate models, the Weibull cure rate model was found to be the best-fitting model for this study. The unique survival kinetics of cancer immunotherapy was captured in the longitudinal predictions of the final analysis times. Conclusions Parametric mixture cure rate models, along with estimated long-term survival rates, probabilities of study incompletion, and expected statistical powers over time, provide immuno-oncology clinical trial researchers with a useful tool for continuous event monitoring and prediction of analysis times, such that informed decisions with quantifiable risks can be made for better resource and logistic planning.
    Preview · Article · Dec 2016 · BMC Medical Research Methodology
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    • "The advantage of the cure rate model, according to Othus et al.[26], among others authors, is that it allows associate covariates in both parts of the model. "
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    ABSTRACT: In this paper, we introduce a methodology based on the zero-inflated cure rate model to detect fraudsters in bank loan applications. Our approach enables us to accommodate three different types of loan applicants, i.e., fraudsters, those who are susceptible to default and finally, those who are not susceptible to default. An advantage of our approach is to accommodate zero-inflated times, which is not possible in the standard cure rate model. To illustrate the proposed method, a real dataset of loan survival times is fitted by the zero-inflated Weibull cure rate model. The parameter estimation is reached by maximum likelihood estimation procedure and Monte Carlo simulations are carried out to check its finite sample performance.
    Full-text · Conference Paper · Sep 2015
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    • "This situation is common in oncology studies and Othus et al. (2012) discuss the preference of cure models over Cox model for analyzing such data (Yin and Ibrahim, 2005; Othus et al., 2012b). No one can be cured of death, therefore term " long survivors " is sometimes used instead of " cured patients " as this long survivorship causes a K-M curve with a plateau tail (Othus et al., 2012a). Since metastatic breast cancer is considered to be incurable, exploring the factors associated with the long term from primary to metastatic breast cancer would be of great value for clinicians and oncology researchers. "
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    ABSTRACT: Breast cancer is a fatal disease and the most frequently diagnosed cancer in women with an increasing pattern worldwide. The burden is mostly attributed to metastatic cancers that occur in one-third of patients and the treatments are palliative. It is of great interest to determine factors affecting time from cancer diagnosis to secondary metastasis. Cure rate models assume a Poisson distribution for the number of unobservable metastatic-component cells that are completely deleted from the non-metastasis patient body but some may remain and result in metastasis. Time to metastasis is defined as a function of the number of these cells and the time for each cell to develop a detectable sign of metastasis. Covariates are introduced to the model via the rate of metastatic-component cells. We used non-mixture cure rate models with Weibull and log-logistic distributions in a Bayesian setting to assess the relationship between metastasis free survival and covariates. The median of metastasis free survival was 76.9 months. Various models showed that from covariates in the study, lymph node involvement ratio and being progesterone receptor positive were significant, with an adverse and a beneficial effect on metastasis free survival, respectively. The estimated fraction of patients cured from metastasis was almost 48%. The Weibull model had a slightly better performance than log-logistic. Cure rate models are popular in survival studies and outperform other models under certain conditions. We explored the prognostic factors of metastatic breast cancer from a different viewpoint. In this study, metastasis sites were analyzed all together. Conducting similar studies in a larger sample of cancer patients as well as evaluating the prognostic value of covariates in metastasis to each site separately are recommended.
    Full-text · Article · Dec 2014 · Asian Pacific journal of cancer prevention: APJCP
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