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Full-Text Articles in Survival Analysis
Estimating A Survival Distribution With Current Status Data And High-Dimensional Covariates, Mark J. Van Der Laan, Aad Van Der Vaart
Estimating A Survival Distribution With Current Status Data And High-Dimensional Covariates, Mark J. Van Der Laan, Aad Van Der Vaart
U.C. Berkeley Division of Biostatistics Working Paper Series
We consider the inverse problem of estimating a survival distribution when the survival times are only observed to be in one of the intervals of a random bisection of the time axis. We are particularly interested in the case that high-dimensional and/or time-dependent covariates are available, and/or the survival events and censoring times are only conditionally independent given the covariate process. The method of estimation consists of regularizing the survival distribution by taking the primitive function or smoothing, estimating the regularized parameter by using estimating equations, and finally recovering an estimator for the parameter of interest.
Linear Life Expectancy Regression With Censored Data, Ying Qing Chen, Su-Chun Cheng
Linear Life Expectancy Regression With Censored Data, Ying Qing Chen, Su-Chun Cheng
U.C. Berkeley Division of Biostatistics Working Paper Series
Life expectancy, i.e., mean residual life function, has been of important practical and scientific interests to characterise the distribution of residual life. Regression models are often needed to model the association between life expectancy and its covariates. In this article, we consider a linear mean residual life model and further developed some inference procedures in presence of censoring. The new model and proposed inference procedure will be demonstrated by numerical examples and application to the well-known Stanford heart transplant data. Additional semiparametric efficiency calculation and information bound are also considered.
A Semiparametric Model Selection Criterion With Applications To The Marginal Structural Model, M. Alan Brookhart, Mark J. Van Der Laan
A Semiparametric Model Selection Criterion With Applications To The Marginal Structural Model, M. Alan Brookhart, Mark J. Van Der Laan
U.C. Berkeley Division of Biostatistics Working Paper Series
Estimators for the parameter of interest in semiparametric models often depend on a guessed model for the nuisance parameter. The choice of the model for the nuisance parameter can affect both the finite sample bias and efficiency of the resulting estimator of the parameter of interest. In this paper we propose a finite sample criterion based on cross validation that can be used to select a nuisance parameter model from a list of candidate models. We show that expected value of this criterion is minimized by the nuisance parameter model that yields the estimator of the parameter of interest with …