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AN EMPIRICAL STUDY OF ODDS RATIO ESTIMATION EMPLOYING BINOMIAL-NORMAL AND NORMAL-NORMAL MODELS
Keywords:
Bayesian inference, Binomial-normal, heterogeneity, imbalanced, MCMC, normal-normal, sample size, odds ratio.DOI:
https://doi.org/10.17654/0973514322021Abstract
Meta-analysis is the process of merging summary data from related but independent studies. The main aim of meta-analysis is to assess the level of heterogeneity between studies. The degree of heterogeneity in a meta-analysis influences the difficulty of making inferences. This may be obtained by calculating the between-study variance, but the interpretation is then bound to a certain treatment effect metric. The objective of this paper is to calculate the odds ratio in clinical trials between treatment and control groups. In addition, this work focuses on the sample size of each study and the number of zeros that appear in the study arms, which are regarded dataset characteristics and contribute to the significance of this analysis. The emphasis is on analytical approaches for estimating the parameters of interest. MCMC has been used to conduct a comparative study on individual, overall odds ratio and heterogeneity for eighteen datasets using the Binomial-normal and normal-normal models through Bayesian. All of the datasets point estimates and interval estimates have been computed, and a comparative study between the balanced and imbalanced groups has been performed.
Received: January 20, 2022
Revised: July 1, 2022
Accepted: July 18, 2022
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