Journal ArticleDOI
Accelerated failure time models for counting processes
TLDR
In this article, a natural extension of the conventional accelerated failure time model for survival data is presented to formulate the effects of covariates on the mean function of the counting process for recurrent events.Abstract:
SUMMARY We present a natural extension of the conventional accelerated failure time model for survival data to formulate the effects of covariates on the mean function of the counting process for recurrent events. A class of consistent and asymptotically normal rank estimators is developed for estimating the regression parameters of the proposed model. In addition, a Nelson-Aalen-type estimator for the mean function of the counting process is constructed, which is consistent and, properly normalised, converges weakly to a zeromean Gaussian process. We assess the finite-sample properties of the proposed estimators and the associated inference procedures through Monte Carlo simulation and provide an application to a well-known bladder cancer study.read more
Citations
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Book
The Statistical Analysis of Recurrent Events
TL;DR: Models and Frameworks for Analysis of Recurrent Events based on Counts and Rate Functions and Analysis of Gap Times are presented.
Journal ArticleDOI
Rank-based inference for the accelerated failure time model
TL;DR: In this paper, a broad class of rank-based monotone estimating functions is developed for the semiparametric accelerated failure time model with censored observations, which are shown to be consistent and asymptotically normal.
Journal ArticleDOI
Nonparametric analysis of recurrent events and death.
Debashis Ghosh,Danyu Lin +1 more
TL;DR: Nonparametric statistics for comparing two mean frequency functions and for combining data on recurrent events and death, together with consistent variance estimators, are developed and an application to a cancer clinical trial is provided.
Journal ArticleDOI
Nonparametric Estimation With Recurrent Event Data
TL;DR: In this article, the problem of nonparametric estimation for the distribution function governing the time to occurrence of a recurrent event in the presence of censoring is considered, and the authors derive Nelson-Aalen and Kaplan-Meier-type estimators and establish their respective finite-sample and asymptotic properties.
Journal ArticleDOI
On fitting Cox's proportional hazards models to survey data
TL;DR: In this article, the authors provide a formal justification of Binder's method and present an alternative approach which regards the survey population as a random sample from an infinite universe and accounts for this randomness in the statistical inference.
References
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Regression models and life tables (with discussion
TL;DR: The drum mallets disclosed in this article are adjustable, by the percussion player, as to balance, overall weight, head characteristics and tone production of the mallet, whereby the adjustment can be readily obtained.
Journal ArticleDOI
The Statistical Analysis of Failure Time Data.
Book
Analysis of Survival Data
David Cox,D. Oakes +1 more
TL;DR: In this article, the authors give a concise account of the analysis of survival data, focusing on new theory on the relationship between survival factors and identified explanatory variables and conclude with bibliographic notes and further results that can be used for student exercises.
Journal ArticleDOI
Cox's Regression Model for Counting Processes: A Large Sample Study
TL;DR: In this article, the Cox regression model for censored survival data is extended to a model where covariate processes have a proportional effect on the intensity process of a multivariate counting process, allowing for complicated censoring patterns and time dependent covariates.
Book
Empirical processes with applications to statistics
Galen R. Shorack,Jon A. Wellner +1 more
TL;DR: In this paper, a broad cross-section of the literature available on one-dimensional empirical processes is summarized, with emphasis on real random variable processes as well as a wide-ranging selection of applications in statistics.
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