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Full-Text Articles in Survival Analysis
Designed Extension Of Survival Studies: Application To Clinical Trials With Unrecognized Heterogeneity, Yi Li, Mei-Chiung Shih, Rebecca A. Betensky
Designed Extension Of Survival Studies: Application To Clinical Trials With Unrecognized Heterogeneity, Yi Li, Mei-Chiung Shih, Rebecca A. Betensky
Harvard University Biostatistics Working Paper Series
It is well known that unrecognized heterogeneity among patients, such as is conferred by genetic subtype, can undermine the power of randomized trial, designed under the assumption of homogeneity, to detect a truly beneficial treatment. We consider the conditional power approach to allow for recovery of power under unexplained heterogeneity. While Proschan and Hunsberger (1995) confined the application of conditional power design to normally distributed observations, we consider more general and difficult settings in which the data are in the framework of continuous time and are subject to censoring. In particular, we derive a procedure appropriate for the analysis of …
Mixture Cure Survival Models With Dependent Censoring, Yi Li, Ram C. Tiwari, Subharup Guha
Mixture Cure Survival Models With Dependent Censoring, Yi Li, Ram C. Tiwari, Subharup Guha
Harvard University Biostatistics Working Paper Series
A number of authors have studies the mixture survival model to analyze survival data with nonnegligible cure fractions. A key assumption made by these authors is the independence between the survival time and the censoring time. To our knowledge, no one has studies the mixture cure model in the presence of dependent censoring. To account for such dependence, we propose a more general cure model which allows for dependent censoring. In particular, we derive the cure models from the perspective of competing risks and model the dependence between the censoring time and the survival time using a class of Archimedean …
Semiparametric Normal Transformation Models For Spatially Correlated Survival Data, Yi Li, Xihong Lin
Semiparametric Normal Transformation Models For Spatially Correlated Survival Data, Yi Li, Xihong Lin
Harvard University Biostatistics Working Paper Series
There is an emerging interest in modeling spatially correlated survival data in biomedical and epidemiological studies. In this paper, we propose a new class of semiparametric normal transformation models for right censored spatially correlated survival data. This class of models assumes that survival outcomes marginally follow a Cox proportional hazard model with unspecified baseline hazard, and their joint distribution is obtained by transforming survival outcomes to normal random variables, whose joint distribution is assumed to be multivariate normal with a spatial correlation structure. A key feature of the class of semiparametric normal transformation models is that it provides a rich …
Inference On Survival Data With Covariate Measurement Error - An Imputation-Based Approach, Yi Li, Louise Ryan
Inference On Survival Data With Covariate Measurement Error - An Imputation-Based Approach, Yi Li, Louise Ryan
Harvard University Biostatistics Working Paper Series
We propose a new method for fitting proportional hazards models with error-prone covariates. Regression coefficients are estimated by solving an estimating equation that is the average of the partial likelihood scores based on imputed true covariates. For the purpose of imputation, a linear spline model is assumed on the baseline hazard. We discuss consistency and asymptotic normality of the resulting estimators, and propose a stochastic approximation scheme to obtain the estimates. The algorithm is easy to implement, and reduces to the ordinary Cox partial likelihood approach when the measurement error has a degenerative distribution. Simulations indicate high efficiency and robustness. …
Robust Inferences For Covariate Effects On Survival Time With Censored Linear Regression Models, Larry Leon, Tianxi Cai, L. J. Wei
Robust Inferences For Covariate Effects On Survival Time With Censored Linear Regression Models, Larry Leon, Tianxi Cai, L. J. Wei
Harvard University Biostatistics Working Paper Series
Various inference procedures for linear regression models with censored failure times have been studied extensively. Recent developments on efficient algorithms to implement these procedures enhance the practical usage of such models in survival analysis. In this article, we present robust inferences for certain covariate effects on the failure time in the presence of "nuisance" confounders under a semiparametric, partial linear regression setting. Specifically, the estimation procedures for the regression coefficients of interest are derived from a working linear model and are valid even when the function of the confounders in the model is not correctly specified. The new proposals are …