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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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Journal ArticleDOI

Survival Analysis Part I: Basic concepts and first analyses

TL;DR: Multivariate survival analysis, a form of multiple regression, provides a way of doing this adjustment for patient-related factors that could potentially affect the survival time of a patient, and is the subject of the next paper in this series.
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A note on competing risks in survival data analysis

TL;DR: Two published data sets are illustrated and the resulting estimates are compared with those obtained using the Kaplan–Meier approach to demonstrate the importance of appropriately estimating the cumulative incidence of an event of interest in the presence of competing risk events.
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Clinical and biologic implications of recurrent genomic aberrations in myeloma

TL;DR: A stratification of patients into 3 distinct categories allowed for prognostication: poor prognosis group (t(4;14)(p16;q32), t(14; 16)(q32;q23), and - 17p13), intermediate prognosis (- 13q14), and good prognosis groups (all others).
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Predicting the retention of university students

TL;DR: This paper used survival analysis to model 8,867 undergraduate students at Oregon State University between 1991 and 1996 to predict a student's probability of leaving school based on demographic and academic variables.
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combining estimates of interest in prognostic modelling studies after multiple imputation: current practice and guidelines

TL;DR: The proposed simple guidelines for combining estimates after MI may lead to a wider and more appropriate use of MI in future prognostic modelling studies.