Journal ArticleDOI
Some approaches to the analysis of recurrent event data
Reads0
Chats0
TLDR
Frailty models are shown to be a special case of a random effects generalization of generalized linear models, whereas marginal models for multivariate failure time data are more closely related to the generalized estimating equation approach to longitudinal generalizedlinear models.Abstract:
Methodological research in biostatistics has been dominated over the last twenty years by further development of Cox's regression model for life tables and of Nelder and Wedderburn's formulation of generalized linear models. In both of these areas the need to address the problems introduced by subject level heterogeneity has provided a major motivation, and the analysis of data concerning recurrent events has been widely discussed within both frameworks. This paper reviews this work, drawing together the parallel development of 'marginal' and 'conditional' approaches in survival analysis and in generalized linear models. Frailty models are shown to be a special case of a random effects generalization of generalized linear models, whereas marginal models for multivariate failure time data are more closely related to the generalized estimating equation approach to longitudinal generalized linear models. Computational methods for inference are discussed, including the Bayesian Markov chain Monte Carlo approach.read more
Citations
More filters
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
Survival analysis for recurrent event data: an application to childhood infectious diseases
TL;DR: The aim of the paper is to determine which models are appropriate for recurrent event data using the key components of the Cox proportional hazards approach, and concludes that PWP-GT and TT-R are useful models for analysing recurrent eventData.
Journal ArticleDOI
Frailty modeling for spatially correlated survival data, with application to infant mortality in Minnesota.
TL;DR: The main substantive goal here is to explain the pattern of infant mortality using important covariates while accounting for possible (spatially correlated) differences in hazard among the counties, using the GIS ArcView to map resulting fitted hazard rates, to help search for possible lingering spatial correlation.
Journal ArticleDOI
Duration Models for Repeated Events
TL;DR: In this article, a class of duration models for analyzing repeated events is presented, and applied to the analysis of international conflict data, where the authors illustrate their methods through an application to widely used data on international conflict.
Journal ArticleDOI
Repeated events survival models: the conditional frailty model.
TL;DR: A robust strategy for the estimation of effects in medical treatments, social conditions, individual behaviours, and public policy programs in repeated events survival models under three common conditions: heterogeneity across individuals, dependence across the number of events, and both heterogeneity and event dependence is recommended.
References
More filters
Book ChapterDOI
Nonparametric Estimation from Incomplete Observations
Edward L. Kaplan,Paul Meier +1 more
TL;DR: In this article, the product-limit (PL) estimator was proposed to estimate the proportion of items in the population whose lifetimes would exceed t (in the absence of such losses), without making any assumption about the form of the function P(t).
Book ChapterDOI
Regression Models and Life-Tables
TL;DR: The analysis of censored failure times is considered in this paper, where the hazard function is taken to be a function of the explanatory variables and unknown regression coefficients multiplied by an arbitrary and unknown function of time.
Book
Generalized Linear Models
Peter McCullagh,John A. Nelder +1 more
TL;DR: In this paper, a generalization of the analysis of variance is given for these models using log- likelihoods, illustrated by examples relating to four distributions; the Normal, Binomial (probit analysis, etc.), Poisson (contingency tables), and gamma (variance components).
Journal ArticleDOI
Longitudinal data analysis using generalized linear models
Kung Yee Liang,Scott L. Zeger +1 more
TL;DR: In this article, an extension of generalized linear models to the analysis of longitudinal data is proposed, which gives consistent estimates of the regression parameters and of their variance under mild assumptions about the time dependence.
Journal ArticleDOI
Generalized Linear Models
TL;DR: In this paper, the authors used iterative weighted linear regression to obtain maximum likelihood estimates of the parameters with observations distributed according to some exponential family and systematic effects that can be made linear by a suitable transformation.