Emergence of Bayesian Network as Data Imputation Technique in Clinical Trials

With data becoming the new oil of the world market, data is essential to key decision-makers and businesses to drive growth in all sections of society. Whether it is economy, computer science or medicine, with the advent of information technology in all fields, handling missing data becomes one of the pivotal tasks to ensure accurate. One of the roadblocks to data-driven decisions is missing data. Missing data affects statistical power of data and adds bias, among other ill effects of their presence. This chapter reviews existing mainstream methods of data handling at the same time looks upon Bayesian network, particularly in clinical trials (observational studies). We look at different studies that have used a vareity of methods to impute data in different medical dataset, stating the advantages and disadvantages of different method give a breeze through about data imputation methods and thier overview. Further solidifying how bayesian network is a model that is a promising method that can set a benchmark for other data imputation techniques.