Cutpoint determination methods in competing risks subdistribution model

In the analysis involving clinical and psychological data, by transforming a continuous predictor variable into a categorical variable, usually binary, a more interpretable model can be established. Thus, we consider the problem of obtaining a threshold value of a continuous covariate given a com...

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Main Authors:	Noor Akma Ibrahim, Abdul Kudus, Isa Daud, Mohd. Rizam Abu Bakar
Format:	Article
Published:	Penerbit ukm 2009
Online Access:	http://journalarticle.ukm.my/1926/ http://journalarticle.ukm.my/1926/

id	ukm-1926
recordtype	eprints
spelling	ukm-19262011-06-20T03:32:11Z http://journalarticle.ukm.my/1926/ Cutpoint determination methods in competing risks subdistribution model Noor Akma Ibrahim, Abdul Kudus, Isa Daud, Mohd. Rizam Abu Bakar, In the analysis involving clinical and psychological data, by transforming a continuous predictor variable into a categorical variable, usually binary, a more interpretable model can be established. Thus, we consider the problem of obtaining a threshold value of a continuous covariate given a competing risk survival time response using a binary partitioning algorithm as a way to optimally partition data into two disjoint sets. Five cutpoint determination methods are developed based on regression of competing risks subdistribution. Simulation results show that the deviance method has the desired properties. Permutation test is used to assess the level of significance and bootstrap confidence interval is obtained for the optimal cutpoint. The deviance method is applied to determine cutpoint of age for a real dataset Penerbit ukm 2009-07 Article PeerReviewed Noor Akma Ibrahim, and Abdul Kudus, and Isa Daud, and Mohd. Rizam Abu Bakar, (2009) Cutpoint determination methods in competing risks subdistribution model. Journal of Quality Measurement and Analysis, 5 (1). pp. 103-117. ISSN 1823-5670 http://www.ukm.my/~ppsmfst/jqma/index.html
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Kebangasaan Malaysia
building	UKM Institutional Repository
collection	Online Access
description	In the analysis involving clinical and psychological data, by transforming a continuous predictor variable into a categorical variable, usually binary, a more interpretable model can be established. Thus, we consider the problem of obtaining a threshold value of a continuous covariate given a competing risk survival time response using a binary partitioning algorithm as a way to optimally partition data into two disjoint sets. Five cutpoint determination methods are developed based on regression of competing risks subdistribution. Simulation results show that the deviance method has the desired properties. Permutation test is used to assess the level of significance and bootstrap confidence interval is obtained for the optimal cutpoint. The deviance method is applied to determine cutpoint of age for a real dataset
format	Article
author	Noor Akma Ibrahim, Abdul Kudus, Isa Daud, Mohd. Rizam Abu Bakar,
spellingShingle	Noor Akma Ibrahim, Abdul Kudus, Isa Daud, Mohd. Rizam Abu Bakar, Cutpoint determination methods in competing risks subdistribution model
author_facet	Noor Akma Ibrahim, Abdul Kudus, Isa Daud, Mohd. Rizam Abu Bakar,
author_sort	Noor Akma Ibrahim,
title	Cutpoint determination methods in competing risks subdistribution model
title_short	Cutpoint determination methods in competing risks subdistribution model
title_full	Cutpoint determination methods in competing risks subdistribution model
title_fullStr	Cutpoint determination methods in competing risks subdistribution model
title_full_unstemmed	Cutpoint determination methods in competing risks subdistribution model
title_sort	cutpoint determination methods in competing risks subdistribution model
publisher	Penerbit ukm
publishDate	2009
url	http://journalarticle.ukm.my/1926/ http://journalarticle.ukm.my/1926/
first_indexed	2023-09-18T19:34:43Z
last_indexed	2023-09-18T19:34:43Z
_version_	1777405194060955648

Cutpoint determination methods in competing risks subdistribution model

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