One current interest in medical research is the comparison of treatments in the analysis of survival times of patients. This is particularly problematic, especially for censored data, and when these data consists of several groups, where each group has distinct properties and characteristics but belong to the same distribution. There are various modeling schemes that have been contemplated to overcome these complexities inherent in the data. One such possibility is the Bayesian approach which integrates prior knowledge in analysis. In this paper, we focus on the use of Bayesian lognormal mixture model (MLNM) with related Dirichlet process (DP) prior distribution for estimating patient survival. The advances in the Bayesian paradigm have considerably bolstered the development and application of mixture modelling methodology in the field of survival analysis. The proposed MLN model is compared with the conventional parametric lognormal and the nonparametric Kaplan Meier (K-M) models used to estimate survival to establish model robustness. A simulation study that investigates the impact of censoring on these models is also described. Real data from past research is used to show the resulting Dirichlet process mixture model’s robustness in the comparison of censored treatment. The results indicate that the proposed lognormal mixtures provide a better fit to complex data. Further, the MLN models are able to estimate various survival distributions and therefore appropriate to compare treatments. Clinicians will find these models useful especially when confronted with the obstacle of choosing a suitable therapy for a disease.
| Published in | International Journal of Statistical Distributions and Applications (Volume 5, Issue 2) |
| DOI | 10.11648/j.ijsd.20190502.13 |
| Page(s) | 38-45 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2019. Published by Science Publishing Group |
Bayesian, Lognormal, Mixture Models, Treatment Comparison, Win BUGS
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APA Style
Henry Ondicho Nyambega, George Otieno Orwa. (2019). On Bayesian Estimation of Dirichlet Process Lognormal Mixture Models and Comparison of Treatments in Censoring. International Journal of Statistical Distributions and Applications, 5(2), 38-45. https://doi.org/10.11648/j.ijsd.20190502.13
ACS Style
Henry Ondicho Nyambega; George Otieno Orwa. On Bayesian Estimation of Dirichlet Process Lognormal Mixture Models and Comparison of Treatments in Censoring. Int. J. Stat. Distrib. Appl. 2019, 5(2), 38-45. doi: 10.11648/j.ijsd.20190502.13
AMA Style
Henry Ondicho Nyambega, George Otieno Orwa. On Bayesian Estimation of Dirichlet Process Lognormal Mixture Models and Comparison of Treatments in Censoring. Int J Stat Distrib Appl. 2019;5(2):38-45. doi: 10.11648/j.ijsd.20190502.13
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Download@article{10.11648/j.ijsd.20190502.13,
author = {Henry Ondicho Nyambega and George Otieno Orwa},
title = {On Bayesian Estimation of Dirichlet Process Lognormal Mixture Models and Comparison of Treatments in Censoring},
journal = {International Journal of Statistical Distributions and Applications},
volume = {5},
number = {2},
pages = {38-45},
doi = {10.11648/j.ijsd.20190502.13},
url = {https://doi.org/10.11648/j.ijsd.20190502.13},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijsd.20190502.13},
abstract = {One current interest in medical research is the comparison of treatments in the analysis of survival times of patients. This is particularly problematic, especially for censored data, and when these data consists of several groups, where each group has distinct properties and characteristics but belong to the same distribution. There are various modeling schemes that have been contemplated to overcome these complexities inherent in the data. One such possibility is the Bayesian approach which integrates prior knowledge in analysis. In this paper, we focus on the use of Bayesian lognormal mixture model (MLNM) with related Dirichlet process (DP) prior distribution for estimating patient survival. The advances in the Bayesian paradigm have considerably bolstered the development and application of mixture modelling methodology in the field of survival analysis. The proposed MLN model is compared with the conventional parametric lognormal and the nonparametric Kaplan Meier (K-M) models used to estimate survival to establish model robustness. A simulation study that investigates the impact of censoring on these models is also described. Real data from past research is used to show the resulting Dirichlet process mixture model’s robustness in the comparison of censored treatment. The results indicate that the proposed lognormal mixtures provide a better fit to complex data. Further, the MLN models are able to estimate various survival distributions and therefore appropriate to compare treatments. Clinicians will find these models useful especially when confronted with the obstacle of choosing a suitable therapy for a disease.},
year = {2019}
}
TY - JOUR T1 - On Bayesian Estimation of Dirichlet Process Lognormal Mixture Models and Comparison of Treatments in Censoring AU - Henry Ondicho Nyambega AU - George Otieno Orwa Y1 - 2019/07/30 PY - 2019 N1 - https://doi.org/10.11648/j.ijsd.20190502.13 DO - 10.11648/j.ijsd.20190502.13 T2 - International Journal of Statistical Distributions and Applications JF - International Journal of Statistical Distributions and Applications JO - International Journal of Statistical Distributions and Applications SP - 38 EP - 45 PB - Science Publishing Group SN - 2472-3509 UR - https://doi.org/10.11648/j.ijsd.20190502.13 AB - One current interest in medical research is the comparison of treatments in the analysis of survival times of patients. This is particularly problematic, especially for censored data, and when these data consists of several groups, where each group has distinct properties and characteristics but belong to the same distribution. There are various modeling schemes that have been contemplated to overcome these complexities inherent in the data. One such possibility is the Bayesian approach which integrates prior knowledge in analysis. In this paper, we focus on the use of Bayesian lognormal mixture model (MLNM) with related Dirichlet process (DP) prior distribution for estimating patient survival. The advances in the Bayesian paradigm have considerably bolstered the development and application of mixture modelling methodology in the field of survival analysis. The proposed MLN model is compared with the conventional parametric lognormal and the nonparametric Kaplan Meier (K-M) models used to estimate survival to establish model robustness. A simulation study that investigates the impact of censoring on these models is also described. Real data from past research is used to show the resulting Dirichlet process mixture model’s robustness in the comparison of censored treatment. The results indicate that the proposed lognormal mixtures provide a better fit to complex data. Further, the MLN models are able to estimate various survival distributions and therefore appropriate to compare treatments. Clinicians will find these models useful especially when confronted with the obstacle of choosing a suitable therapy for a disease. VL - 5 IS - 2 ER -
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