Parameter estimation in regression for long-term survival rate from censored data

DOI10.1016/S0378-3758(01)00084-2zbMath0989.62067MaRDI QIDQ5955597

Sundarraman Subramanian

Publication date: 2 July 2002

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

zbMATH Keywords

Newton-Raphson methodmartingalecounting processconditional Kaplan-Meier estimatorCox proportional hazardscumulative hazard function

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Asymptotic distribution theory in statistics (62E20) Applications of statistics to biology and medical sciences; meta analysis (62P10) Censored data models (62N01) Estimation in survival analysis and censored data (62N02)

Related Items (8)

Survival-rate regression using kernel conditional Kaplan-Meier estimators ⋮ Multiple imputations and the missing censoring indicator model ⋮ Semiparametric transformation models and the missing information principle ⋮ Generalized time-dependent conditional linear models under left truncation and right censoring ⋮ A goodness-of-fit test for generalised conditional linear models under left truncation and right censoring ⋮ Semiparametric Estimation in Transformation Models with Cure Fraction ⋮ Regression analysis for long-term survival rate via empirical likelihood ⋮ Parameter Estimation in Regression for Long-Term Survival Rate from Left-Truncated and Right-Censored Data

Cites Work

• Cox's regression model for counting processes: A large sample study
• Strong consistency of a nonparametric estimator of the survival function with doubly censored data
• Uniform consistency of the kernel conditional Kaplan-Meier estimate
• Regression analysis for long-term survival rate
• Survival Analysis with Median Regression Models
• Statistical models based on counting processes
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