Competing risk multistate censored data modeling by propensity score matching method

Atanu Bhattacharjee; Gajendra K. Vishwakarama; Abhipsa Tripathy; Bhrigu Kumar Rajbongshi

doi:10.1038/s41598-024-54149-y

Competing risk multistate censored data modeling by propensity score matching method

Atanu Bhattacharjee, Gajendra K. Vishwakarama (Lead / Corresponding author), Abhipsa Tripathy, Bhrigu Kumar Rajbongshi

Population Health and Genomics

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Abstract

The potential contribution of the paper is the use of the propensity score matching method for updating censored observations within the context of multi-state model featuring two competing risks.The competing risks are modelled using cause-specific Cox proportional hazard model.The simulation findings demonstrate that updating censored observations tends to lead to reduced bias and mean squared error for all estimated parameters in the risk of cause-specific Cox model.The results for a chemoradiotherapy real dataset are consistent with the simulation results.

Original language	English
Article number	4368
Number of pages	11
Journal	Scientific Reports
Volume	14
DOIs	https://doi.org/10.1038/s41598-024-54149-y
Publication status	Published - 22 Feb 2024

Keywords

Censoring
Competing risk
Multistate model
Non parametric estimation
Propensity Score

ASJC Scopus subject areas

General

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10.1038/s41598-024-54149-yLicence: CC BY

Final published versionFinal published version, 2.34 MBLicence: CC BY

Cite this

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title = "Competing risk multistate censored data modeling by propensity score matching method",

abstract = "The potential contribution of the paper is the use of the propensity score matching method for updating censored observations within the context of multi-state model featuring two competing risks.The competing risks are modelled using cause-specific Cox proportional hazard model.The simulation findings demonstrate that updating censored observations tends to lead to reduced bias and mean squared error for all estimated parameters in the risk of cause-specific Cox model.The results for a chemoradiotherapy real dataset are consistent with the simulation results.",

keywords = "Censoring, Competing risk, Multistate model, Non parametric estimation, Propensity Score",

author = "Atanu Bhattacharjee and Vishwakarama, {Gajendra K.} and Abhipsa Tripathy and Rajbongshi, {Bhrigu Kumar}",

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AU - Tripathy, Abhipsa

AU - Rajbongshi, Bhrigu Kumar

N1 - Publisher Copyright: © The Author(s) 2024.

PY - 2024/2/22

Y1 - 2024/2/22

N2 - The potential contribution of the paper is the use of the propensity score matching method for updating censored observations within the context of multi-state model featuring two competing risks.The competing risks are modelled using cause-specific Cox proportional hazard model.The simulation findings demonstrate that updating censored observations tends to lead to reduced bias and mean squared error for all estimated parameters in the risk of cause-specific Cox model.The results for a chemoradiotherapy real dataset are consistent with the simulation results.

AB - The potential contribution of the paper is the use of the propensity score matching method for updating censored observations within the context of multi-state model featuring two competing risks.The competing risks are modelled using cause-specific Cox proportional hazard model.The simulation findings demonstrate that updating censored observations tends to lead to reduced bias and mean squared error for all estimated parameters in the risk of cause-specific Cox model.The results for a chemoradiotherapy real dataset are consistent with the simulation results.

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KW - Multistate model

KW - Non parametric estimation

KW - Propensity Score

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Competing risk multistate censored data modeling by propensity score matching method

Abstract

Keywords

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