Performance of Cox Proportional Hazards and Accelerated Failure Time Models in the Tuberculosis/HIV Co-Infected Survival Data

Mr. Ogungbola Opeyemi Oyekola; Dr. A. A. Akomolafe; Dr. A. Z. Musa

Performance of Cox Proportional Hazards and Accelerated Failure Time Models in the Tuberculosis/HIV Co-Infected Survival Data

Mr. Ogungbola Opeyemi Oyekola *

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Dr. A. A. Akomolafe

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Dr. A. Z. Musa

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Performance of Cox Proportional Hazards and Accelerated Failure Time Models in the Tuberculosis/HIV Co-Infected Survival Data

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Abstract

Cox model and accelerated failure time models are widely used in modelling of survival data for various diseases. This research compares the performance of Cox proportional hazards models and accelerated failure time (AFT) models using TB/HIV co-infected survival data. The tools used are AFT model plot, the log-likelihood test, Akaike Information Criterion (AIC), Log rank test for comparing all survival variables. The research established that AFT model provides a better description of the dataset as compared with Cox PH models because it allows prediction of Hazard function, survival functions as well as time ratio. Moreover, Cox proportional hazard model does not fit appropriately when compared with AFT model; thereby provide less appropriate description of the survival data. Hence, it is better for researchers of TB/HIV coinfection to consider AFT model even if the proportionality assumption of the Cox model is satisfied.

References

25 Cites in Article

Reference Format

N Ata,M Sozer (2007). Cox Regression Models with Non proportional Hazards applied to Lung Cancer survival data.
A Ayman (2012). Semi-Parametric Hazard Ratio applied to Engineering Insurance System.
Dermot Maher (2007). Planning to improve global health: the next decade of tuberculosis control.
Ralf Bender,Thomas Augustin,Maria Blettner (2005). Generating survival times to simulate Cox proportional hazards models.
D Cox (1972). Regression mode0ls and lifetables.
J David (2014). Data Generation for the Cox Proportional Hazards Model with Time-Dependent Covariates: A Method for Medical Researchers.
Bradley Efron (1977). The Efficiency of Cox's Likelihood Function for Censored Data.
A Kazeem,A Abiodun,R Ipinyomi (2015). Semi-Parametric Non-Proportional Hazard Model With Time Varying Covariate.
Le Tt,Lim Hj (2009). Dynamics of the HIV Epidemics among Male Injecting Drug Users Using Agent-Based Modeling.
S Lindsay (2004). Cox Regression Model.
L Leemis,L Shih,K Reynertson (1989). Variate Generation for Accelerated Life and Proportional Hazards Models with Time Dependent Covariates.
R Maller,X Zhou (1996). Survival Analysis with Long-Term Survivors.
M Monica (2011). Bayesian Approaches to Correcting Bias in Epidemiological Data.
Jens Nielsen,Oliver Linton (1995). Kernel Estimation in a Nonparametric Marker Dependent Hazard Model.
O Ogungbola,A Akomolafe,Z Musa (2018). Accelerated failure time models with application to data on TB/HIV co-infected patients in Nigeria.
Marcello Pagano,Kimberlee Gauvreau,Heather Mattie (1993). Principles of Biostatistics.
I Persson (2002). Essays on the assumption of Proportional Hazards in Cox Regression.
Jin Lin,D Ying,Z (2003). Rank-based Inference for Accelerated Failure Time Models.
L John,D Andrew,J Patricia,E Cyprus,Tamara Hbn (2006). Modelling Survival in Acute Severe Illness Cox versus AFT models.
L Johnson,R Strawderman (2009). Induced smoothing for the semiparametric accelerated failure time model: asymptotics and extensions to clustered data.
M Thomas (1993). Survival Models, Part 2: Proportional Hazard Models.
L Wei,D Lin,L Weissfeld (1989). Regression Analysis of Multivariate Incomplete Failure Time Data by Modeling Marginal Distributions.
H Yuanxin (2013). Survival Analysis of Cardiovascular Diseases.
Thomas Scheike (2004). Time-Varying Effects in Survival Analysis.
C Sy Han (2013). Statistical methods and computing for Semi-parametric and Accelerated Failure Time Model with induced Smoothing.

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Mr. Ogungbola Opeyemi Oyekola, Dr. A. A. Akomolafe, Dr. A. Z. Musa. 2021. "Performance of Cox Proportional Hazards and Accelerated Failure Time Models in the Tuberculosis/HIV Co-Infected Survival Data". Global Journal of Medical Research - F: Diseases GJMR-F Volume 21 (GJMR Volume 21 Issue F2).

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GJMR Volume 21 Issue F2

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Journal Specifications

Crossref Journal DOI 10.17406/gjmr

Print ISSN 0975-5888

e-ISSN 2249-4618

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GJMR-F Classification NLMC Code: WF 200

Submission ReceivedFebruary 9, 2021
Peer Review Double Blind
Handling Editor
Accepted March 1, 2021
Published March 15, 2021

Version of record

v1.2

Issue date

April 22, 2021

Language

English

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