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Influence diagnostics for the Cox proportional hazards regression model : method, simulation and applications

Kausar, Tehzeeb ; Akbar, Atif and Qasim, Muhammad LU (2023) In Journal of Statistical Computation and Simulation 93(10). p.1580-1600
Abstract

This article investigates the performance of several residuals for the Cox proportional hazards regression model to diagnose the influential observations. The standardized and adjusted forms of residuals are proposed for Cox proportional hazards regression model. In addition, Cook’s distance is proposed for both standardized and adjusted residuals. The assessment of different residuals for the identification of influential observations is made through the Monte Carlo simulation. A real dataset of bone marrow transplant Leukaemia is analyzed to show the benefit of the proposed methods. Simulation and application results show that the standardized and adjusted residuals based on the Cox–Snell method perform best for the detection of... (More)

This article investigates the performance of several residuals for the Cox proportional hazards regression model to diagnose the influential observations. The standardized and adjusted forms of residuals are proposed for Cox proportional hazards regression model. In addition, Cook’s distance is proposed for both standardized and adjusted residuals. The assessment of different residuals for the identification of influential observations is made through the Monte Carlo simulation. A real dataset of bone marrow transplant Leukaemia is analyzed to show the benefit of the proposed methods. Simulation and application results show that the standardized and adjusted residuals based on the Cox–Snell method perform best for the detection of influential points. Furthermore, the standardized, and adjusted Martingale and deviance residuals work better when the sample size is large.

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Please use this url to cite or link to this publication:
author
; and
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Adjusted residuals, Cook’s distance, Cox proportional hazards model, Cox–Snell residual, Influential observations, Standardized residuals
in
Journal of Statistical Computation and Simulation
volume
93
issue
10
pages
21 pages
publisher
Taylor & Francis
external identifiers
  • scopus:85143727455
ISSN
0094-9655
DOI
10.1080/00949655.2022.2145608
language
English
LU publication?
no
additional info
Publisher Copyright: © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
id
6458c0fc-c49c-4f2f-a2f6-63dc3572d730
date added to LUP
2025-03-24 17:24:14
date last changed
2025-04-04 14:20:29
@article{6458c0fc-c49c-4f2f-a2f6-63dc3572d730,
  abstract     = {{<p>This article investigates the performance of several residuals for the Cox proportional hazards regression model to diagnose the influential observations. The standardized and adjusted forms of residuals are proposed for Cox proportional hazards regression model. In addition, Cook’s distance is proposed for both standardized and adjusted residuals. The assessment of different residuals for the identification of influential observations is made through the Monte Carlo simulation. A real dataset of bone marrow transplant Leukaemia is analyzed to show the benefit of the proposed methods. Simulation and application results show that the standardized and adjusted residuals based on the Cox–Snell method perform best for the detection of influential points. Furthermore, the standardized, and adjusted Martingale and deviance residuals work better when the sample size is large.</p>}},
  author       = {{Kausar, Tehzeeb and Akbar, Atif and Qasim, Muhammad}},
  issn         = {{0094-9655}},
  keywords     = {{Adjusted residuals; Cook’s distance; Cox proportional hazards model; Cox–Snell residual; Influential observations; Standardized residuals}},
  language     = {{eng}},
  number       = {{10}},
  pages        = {{1580--1600}},
  publisher    = {{Taylor & Francis}},
  series       = {{Journal of Statistical Computation and Simulation}},
  title        = {{Influence diagnostics for the Cox proportional hazards regression model : method, simulation and applications}},
  url          = {{http://dx.doi.org/10.1080/00949655.2022.2145608}},
  doi          = {{10.1080/00949655.2022.2145608}},
  volume       = {{93}},
  year         = {{2023}},
}