Open Access. Powered by Scholars. Published by Universities.®
- Discipline
- Institution
- Publication
- Publication Type
Articles 1 - 3 of 3
Full-Text Articles in Survival Analysis
Survival Analysis Of Microarray Data With Microarray Measurement Subject To Measurement Error, Juan Xiong
Survival Analysis Of Microarray Data With Microarray Measurement Subject To Measurement Error, Juan Xiong
Electronic Thesis and Dissertation Repository
Microarray technology is essentially a measurement tool for measuring expressions of genes, and this measurement is subject to measurement error. Gene expressions could be employed as predictors for patient survival, and the measurement error involved in the gene expression is often ignored in the analysis of microarray data in the literature. Efforts are needed to establish statistical method for analyzing microarray data without ignoring the error in gene expression. A typical microarray data set has a large number of genes far exceeding the sample size. Proper selection of survival relevant genes contributes to an accurate prediction model. We study the …
Linear Regression Of Censored Length-Biased Lifetimes, Ying Qing Chen, Yan Wang
Linear Regression Of Censored Length-Biased Lifetimes, Ying Qing Chen, Yan Wang
UW Biostatistics Working Paper Series
Length-biased lifetimes may be collected in observational studies or sample surveys due to biased sampling scheme. In this article, we use a linear regression model, namely, the accelerated failure time model, for the population lifetime distributions in regression analysis of the length-biased lifetimes. It is discovered that the associated regression parameters are invariant under the length-biased sampling scheme. According to this discovery, we propose the quasi partial score estimating equations to estimate the population regression parameters. The proposed methodologies are evaluated and demonstrated by simulation studies and an application to actual data set.
On The Accelerated Failure Time Model For Current Status And Interval Censored Data, Lu Tian, Tianxi Cai
On The Accelerated Failure Time Model For Current Status And Interval Censored Data, Lu Tian, Tianxi Cai
Harvard University Biostatistics Working Paper Series
This paper introduces a novel approach to making inference about the regression parameters in the accelerated failure time (AFT) model for current status and interval censored data. The estimator is constructed by inverting a Wald type test for testing a null proportional hazards model. A numerically efficient Markov chain Monte Carlo (MCMC) based resampling method is proposed to simultaneously obtain the point estimator and a consistent estimator of its variance-covariance matrix. We illustrate our approach with interval censored data sets from two clinical studies. Extensive numerical studies are conducted to evaluate the finite sample performance of the new estimators.