Literature DB >> 34366547

Semiparametric Regression Analysis of Panel Count Data: A Practical Review.

Sy Han Chiou1, Chiung-Yu Huang2, Gongjun Xu3, Jun Yan4.   

Abstract

Panel count data arise in many applications when the event history of a recurrent event process is only examined at a sequence of discrete time points. In spite of the recent methodological developments, the availability of their software implementations has been rather limited. Focusing on a practical setting where the effects of some time-independent covariates on the recurrent events are of primary interest, we review semiparametric regression modelling approaches for panel count data that have been implemented in R package spef. The methods are grouped into two categories depending on whether the examination times are associated with the recurrent event process after conditioning on covariates. The reviewed methods are illustrated with a subset of the data from a skin cancer clinical trial.

Entities:  

Keywords:  Counting process; estimating equation; frailty; maximum likelihood; recurrent event

Year:  2018        PMID: 34366547      PMCID: PMC8340851          DOI: 10.1111/insr.12271

Source DB:  PubMed          Journal:  Int Stat Rev        ISSN: 0306-7734            Impact factor:   2.217


  2 in total

1.  Nonparametric inference for panel count data with competing risks.

Authors:  E P Sreedevi; P G Sankaran
Journal:  J Appl Stat       Date:  2020-07-21       Impact factor: 1.416

2.  A Robust Functional EM Algorithm for Incomplete Panel Count Data.

Authors:  Alexander Moreno; Zhenke Wu; Jamie Yap; Cho Lam; David W Wetter; Inbal Nahum-Shani; Walter Dempsey; James M Rehg
Journal:  Adv Neural Inf Process Syst       Date:  2020-12
  2 in total

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