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J Res Health Sci. 2024;24(2): e00615.
doi: 10.34172/jrhs.2024.150

Scopus ID: 85196387570
  Abstract View: 121
  PDF Download: 83

Original Article

Identification of Risk Factors for Relapse in Childhood Leukemia Using Penalized Semi-parametric Mixture Cure Competing Risks Model

Zahra Mehrbakhsh 1,2 ORCID logo, Leili Tapak 1,3 ORCID logo, Nasser Behnampour 4, Ghodratollah Roshanaei 1,3* ORCID logo

1 Department of Biostatistics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran
2 Student Research Committee, Hamadan University of Medical Sciences, Hamadan, Iran
3 Modeling of Noncommunicable Diseases Research Center, Hamadan University of Medical Sciences, Hamadan, Iran
4 Department of Biostatistics and Epidemiology, School of Health, Golestan University of Medical Sciences, Gorgan, Iran
*Corresponding Author: Ghodratollah Roshanaei, Email: gh.roshanaei@umsha.ac.ir

Abstract

Background: Leukemia is the most common childhood malignancy. Identifying prognostic factors of patient survival and relapse using more reliable statistical models instead of traditional variable selection methods such as stepwise regression is of great importance. The present study aimed to apply a penalized semi-parametric mixture cure model to identify the prognostic factors affecting short-term and long-term survival of childhood leukemia in the presence of competing risks. The outcome of interest in this study was time to relapse.

Study Design: A retrospective cohort study.

Methods: A total of 178 patients (0‒15 years old) with leukemia participated in this study (September 1997 to September 2016, followed up to June 2021) at Golestan University of Medical Sciences, Iran. Demographic, clinical, and laboratory data were collected, and then a penalized semi-parametric mixture cure competing risk model with smoothly clipped absolute deviation (SCAD) and least absolute shrinkage and selection operator (LASSO) regularizations was used to analyze the data.

Results: Important prognostic factors of relapse patients selected by the SCAD regularization method were platelets (150000‒400000 vs.>400000; odds ratio=0.31) in the cure part and type of leukemia (ALL vs. AML, hazard ratio (HR)=0.08), mediastinal tumor (yes vs. no, HR=16.28), splenomegaly (yes vs. no; HR=2.94), in the latency part. In addition, significant prognostic factors of death identified by the SCAD regularization method included white blood cells (<4000 vs.>11000, HR=0.25) and rheumatoid arthritis signs (yes vs. no, HR=5.75) in the latency part.

Conclusion: Several laboratory factors and clinical side effects were associated with relapse and death, which can be beneficial in treating the disease and predicting relapse and death time.


Please cite this article as follows: Mehrbakhsh Z, Tapak L, Behnampour N, Roshanaei G. Identification of risk factors for relapse in childhood leukemia using penalized semi-parametric mixture cure competing risks model. J Res Health Sci. 2024; 24(2):e00615. doi:10.34172/jrhs.2024.149
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Submitted: 05 Jan 2024
Revision: 07 Feb 2024
Accepted: 21 Apr 2024
ePublished: 01 Jun 2024
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