Author(s)

Mercy K. Peter¹, Levi Mbugua², Anthony Wanjoya³

¹Department of Mathematics, Pan African University Institute for Basic Sciences, Technology and Innovation, Nairobi, Kenya.
²Department of Statistics and Actuarial science, Technical University of Kenya, Nairobi, Kenya.
³Department of Statistics and Actuarial science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya.

The effect of treatment on patient’s outcome can easily be determined through the impact of the treatment on biological events. Observing the treatment for patients for a certain period of time can help in determining whether there is any change in the biomarker of the patient. It is important to study how the biomarker changes due to treatment and whether for different individuals located in separate centers can be clustered together since they might have different distributions. The study is motivated by a Bayesian non-parametric mixture model, which is more flexible when compared to the Bayesian Parametric models and is capable of borrowing information across different centers allowing them to be grouped together. To this end, this research modeled Biological markers taking into consideration the Surrogate markers. The study employed the nested Dirichlet process prior, which is easily peaceable on different distributions for several centers, with centers from the same Dirichlet process component clustered automatically together. The study sampled from the posterior by use of Markov chain Monte carol algorithm. The model is illustrated using a simulation study to see how it performs on simulated data. Clearly, from the simulation study it was clear that, the model was capable of clustering data into different clusters.

Bayesian Non-Parametric, Nested Dirichlet Process, Biomarker, Clustering, Surrogate Markers, Dirichlet Process, Markov Chain Monte Carlo

Peter, M. , Mbugua, L. and Wanjoya, A. (2019) Bayesian Non-Parametric Mixture Model with Application to Modeling Biological Markers. Journal of Data Analysis and Information Processing, 7, 141-152. doi: 10.4236/jdaip.2019.74009.