This paper introduces a novel semiparametric Bayesian approach for bivariate Meta-regression. The method extends traditional binomial models to trinomial distributions, accounting for positive, neutral, and negative treatment effects. Using a conditional Dirichlet process, we develop a model to compare treatment and control groups across multiple clinical centers. This approach addresses the challenges posed by confounding factors in such studies. The primary objective is to assess treatment efficacy by modeling response outcomes as trinomial distributions. We employ Gibbs sampling and the Metropolis-Hastings algorithm for posterior computation. These methods generate estimates of treatment effects while incorporating auxiliary variables that may influence outcomes. Simulations across various scenarios demonstrate the model’s effectiveness. We also establish credible intervals to evaluate hypotheses related to treatment effects. Furthermore, we apply the methodology to real-world data on economic activity in Iran from 2009 to 2021. This application highlights the practical utility of our approach in Meta-analytic contexts. Our research contributes to the growing body of literature on Bayesian methods in Meta-analysis. It provides valuable insights for improving clinical study evaluations.

In this paper, we will introduce a Bayesian semiparametric model concerned with both constant and coefficients. In Meta-Analysis or Meta-regression, we usually use a parametric family. However, lately the increasing tendency to use Bayesian nonparametric and semiparametric models, entered this area too. On the other hand, although we have some works on Bayesian nonparametric or semiparametric models, they just focus on intercept and do not pay much attention to regressor coefficient(s). We also would check the efficiency of the proposed model via simulation and give an illustrating example.

Triphala is a medicinal plant that can improve anthropometric parameters. Although Triphala is widely used, especially in India and Thailand, its efficacy is still controversial. Consequently, the purpose of this Meta-analysis and Meta-regression analysis was to assess the safety of Triphala and its effects on anthropometric parameters. A comprehensive review and Meta-analysis of randomized controlled trials assessing the safety of Triphala and its effects on anthropometric parameters were conducted by searching PubMed, ScienceDirect, Web of Science, and ThaiLIS databases. Two authors independently conducted the study selection and data extraction and evaluated the quality of the studies. The clinical therapeutic effects and adverse events of Triphala were evaluated and aggregated using a random-effects model. The chi-square and I2 tests were used to assess heterogeneity between the studies. Seven trials with a total of 458 patients were included. The Triphala-treated groups demonstrated a considerable decrease in body weight (BW) (weighted mean difference [WMD]: −, 2. 99 kg,95% CI: −, 5. 31, −, 0. 67,P = 0. 012,I2 = 94. 4%), body mass index (BMI) (WMD: −, 0. 79 kg/m2,95% CI: −, 1. 52, −, 0. 07,P = 0. 032,I2 = 90. 4%), and waist circumference (WC) (WMD: −, 1. 86 cm,95% CI: −, 3. 10, −, 0. 62,P = 0. 003,I2 = 88. 8%). During the treatment period, there were no reports of serious adverse events related to Triphala. However, there was no association between the dose or duration of treatment and any of the recorded outcomes. This Meta-analysis revealed that Triphala significantly improved BW, BMI, and WC. Nevertheless, substantial, well-designed randomized controlled studies are necessary to confirm this finding.