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4. Modelling Survival Data in Medical Research

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This article is published in Journal of The Royal Statistical Society Series A-statistics in Society.The article was published on 1995-01-01. It has received 1092 citations till now. The article focuses on the topics: Medical research.

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Mineral Metabolism, Mortality, and Morbidity in Maintenance Hemodialysis

TL;DR: Hyperphosphatemia and hyperparathyroidism were significantly associated with all-cause, cardiovascular, and fracture-related hospitalization, and the population attributable risk percentage for disorders of mineral metabolism was 17.5%, owing largely to the high prevalence of hyperph phosphatemia.
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Overall Survival and Updated Results for Sunitinib Compared With Interferon Alfa in Patients With Metastatic Renal Cell Carcinoma

TL;DR: Sunitinib demonstrates longer overall survival compared with IFN-alpha plus improvement in response and progression-free survival in the first-line treatment of patients with metastatic RCC, highlighting an improved prognosis in patients with RCC in the era of targeted therapy.
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Survival and Prognostic Stratification of 670 Patients With Advanced Renal Cell Carcinoma

TL;DR: Five prognostic factors for predicting survival were identified and used to categorize patients with metastatic RCC into three risk groups, for which the median survival times were separated by 6 months or more.
References
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Estimation of Population Based Colorectal Cancer Survival Analysis Using Cox Proportional Hazards Model

TL;DR: From the results, it was concluded that the predictor variables could significantly predict the survival of colorectal cancer patients using Cox proportional model and the data met Cox Proportional Hazards Assumptions.
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A semi-mechanistic red blood cell survival model provides some insight into red blood cell destruction mechanisms.

TL;DR: It is shown that the proposed model in combination with informative RBC survival data is able to provide a deeper insight into RBC destruction mechanisms, and three mechanisms were estimable based on the available data of twelve subjects, including random destruction, senescence and destruction due to delayed failure.
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The Poisson-exponential regression model under different latent activation schemes

TL;DR: In this article, a new family of survival distributions is presented by considering that the latent number of failure causes follows a Poisson distribution and the time for these causes to be activated follows an exponential distribution.