Literature DB >> 1925154

Statistical analysis of repeated events forming renewal processes.

O O Aalen1, E Husebye.   

Abstract

For each of several individuals a sequence of repeated events, forming a renewal process, is observed up to some censoring time. The object is to estimate the average interevent time over the population of individuals as well as the variation of interevent times within and between individuals. Medical motivation comes from gastroenterology, and concerns the occurrence of certain cyclic movements in the small bowel during the fasting state. Two statistical models are considered: one is the standard variance component model adapted to censored data, and the other is a recent intensity based model with a random proportionality factor representing interindividual variation. These models are applied to the motility data, and their advantages are discussed. The intensity based model allows simple empirical Bayes estimation of the expected interevent times for an individual in the presence of censoring.

Mesh:

Year:  1991        PMID: 1925154     DOI: 10.1002/sim.4780100806

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  19 in total

1.  Dynamic random effects models for times between repeated events.

Authors:  D Y Fong; K F Lam; J F Lawless; Y W Lee
Journal:  Lifetime Data Anal       Date:  2001-12       Impact factor: 1.588

2.  Marginal regression of gaps between recurrent events.

Authors:  Yijian Huang; Ying Qing Chen
Journal:  Lifetime Data Anal       Date:  2003-09       Impact factor: 1.588

3.  Estimating marginal effects in accelerated failure time models for serial sojourn times among repeated events.

Authors:  Shu-Hui Chang
Journal:  Lifetime Data Anal       Date:  2004-06       Impact factor: 1.588

4.  Product-limit estimators of the gap time distribution of a renewal process under different sampling patterns.

Authors:  Richard D Gill; Niels Keiding
Journal:  Lifetime Data Anal       Date:  2010-03-23       Impact factor: 1.588

5.  Nonparametric Methods in Reliability.

Authors:  Myles Hollander; Edsel A Peña
Journal:  Stat Sci       Date:  2004-11       Impact factor: 2.901

6.  Dynamic Modelling and Statistical Analysis of Event Times.

Authors:  Edsel A Peña
Journal:  Stat Sci       Date:  2006-11       Impact factor: 2.901

7.  Conditional GEE for recurrent event gap times.

Authors:  David Y Clement; Robert L Strawderman
Journal:  Biostatistics       Date:  2009-03-18       Impact factor: 5.899

8.  Nonparametric modeling of the gap time in recurrent event data.

Authors:  Pang Du
Journal:  Lifetime Data Anal       Date:  2009-01-03       Impact factor: 1.588

9.  Nonparametric Estimation of a Recurrent Survival Function.

Authors:  Mei-Cheng Wang; Shu-Hui Chang
Journal:  J Am Stat Assoc       Date:  1999-03-01       Impact factor: 5.033

10.  Bayesian regression model for recurrent event data with event-varying covariate effects and event effect.

Authors:  Li-An Lin; Sheng Luo; Barry R Davis
Journal:  J Appl Stat       Date:  2017-08-26       Impact factor: 1.404

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