• Journal of the Korean Data and Information Science Society
• /
• v.22 no.3
• /
• pp.459-466
• /
• 2011

This article addresses robust Bayesian modeling for meta analysis which derives general conclusion by combining independently performed individual studies. Specifically, we propose hierarchical Bayesian models with unknown variances for meta analysis under priors which are scale mixtures of normal, and thus have tail heavier than that of the normal. For the numerical analysis, we use the Gibbs sampler for calculating Bayesian estimators and illustrate the proposed methods using actual data.

• Journal of the Korean Data and Information Science Society
• /
• v.11 no.2
• /
• pp.313-318
• /
• 2000

This article addresses aspects of combining information, with special attention to meta-analysis. In specific, we consider hierarchical Bayesian models for meta-analysis under priors which are scale mixtures of normal, and thus have tail heavier than that of the normal. Numerical methods of finding Bayes estimators under these heavy tailed prior are given, and are illustrated with an actual example.

• Proceedings of the Korean Statistical Society Conference
• /
• 2000.11a
• /
• pp.5-12
• /
• 2000

Meta-analysis refers to quantitative methods for combining results from independent studies in order to draw overall conclusions. We consider hierarchical models including selection models under a skewed heavy tailed error distribution and it is shown to be useful in such Bayesian meta-analysis. A general class of skewed elliptical distribution is reviewed and developed. These rich class of models combine the information of independent studies, allowing investigation of variability both between and within studies, and weight function. Here we investigate sensitivity of results to unobserved studies by considering a hierarchical selection model and use Markov chain Monte Carlo methods to develop inference for the parameters of interest.

• Genomics & Informatics
• /
• v.20 no.2
• /
• pp.16.1-16.12
• /
• 2022

Various methodologies for the genetic analysis of longitudinal data have been proposed and applied to data from large-scale genome-wide association studies (GWAS) to identify single nucleotide polymorphisms (SNPs) associated with traits of interest and to detect SNP-time interactions. We recently proposed a grid-based Bayesian mixed model for longitudinal genetic data and showed that our Bayesian method increased the statistical power compared to the corresponding univariate method and well detected SNP-time interactions. In this paper, we further analyze longitudinal obesity-related traits such as body mass index, hip circumference, waist circumference, and waist-hip ratio from Korea Association Resource data to evaluate the proposed Bayesian method. We first conducted GWAS analyses of cross-sectional traits and combined the results of GWAS analyses through a meta-analysis based on a trajectory model and a random-effects model. We then applied our Bayesian method to a subset of SNPs selected by meta-analysis to further discover SNPs associated with traits of interest and SNP-time interactions. The proposed Bayesian method identified several novel SNPs associated with longitudinal obesity-related traits, and almost 25% of the identified SNPs had significant p-values for SNP-time interactions.

• The Korean Journal of Applied Statistics
• /
• v.14 no.1
• /
• pp.161-175
• /
• 2001

Meta-analysis refers to quantitative methods for combining results from independent studies in order to draw overall conclusions. Hierarchical models including selection models are introduced and shown to be useful in such Bayesian meta-analysis. Semiparametric hierarchical models are proposed using the Dirichlet process prior. These rich class of models combine the information of independent studies, allowing investigation of variability both between and within studies, and weight function. Here we investigate sensitivity of results to unobserved studies by considering a hierachical selection model with including unknown weight function and use Markov chain Monte Carlo methods to develop inference for the parameters of interest. Using Bayesian method, this model is used on a meta-analysis of twelve studies comparing the effectiveness of two different types of flouride, in preventing cavities. Clinical informative prior is assumed. Summaries and plots of model parameters are analyzed to address questions of interest.

• Clinical and Experimental Pediatrics
• /
• v.64 no.5
• /
• pp.208-222
• /
• 2021

The publication of genetic epidemiology meta-analyses has increased rapidly, but it has been suggested that many of the statistically significant results are false positive. In addition, most such meta-analyses have been redundant, duplicate, and erroneous, leading to research waste. In addition, since most claimed candidate gene associations were false-positives, correctly interpreting the published results is important. In this review, we emphasize the importance of interpreting the results of genetic epidemiology meta-analyses using Bayesian statistics and gene network analysis, which could be applied in other diseases.

• 대한예방의학회:학술대회논문집
• /
• 2005.10a
• /
• pp.300-300
• /
• 2005

• Pediatric Infection and Vaccine
• /
• v.31 no.1
• /
• pp.55-63
• /
• 2024

Purpose: A 2-dose varicella vaccination strategy has been introduced in many countries worldwide, aiming to increase vaccine effectiveness (VE) against varicella infection. In this network meta-analysis, we aimed to provide a comprehensive evaluation and an overall estimated effect of varicella vaccination strategies, via a Bayesian model. Methods: For each eligible study, we collected trial characteristics, such as: 1-dose vs. 2-dose, demographic characteristics, and outcomes of interest. For studies involving different doses, we aggregated the data for the same number of doses delivered into one arm. The preventive effect of 1-dose vs. 2-dose of varicella vaccine were evaluated in terms of the odds ratio (OR) and corresponding equal-tailed 95% confidence interval (95% CI). Results: A total of 903 studies were retrieved during our literature search, and 25 interventional or observational studies were selected for the Bayesian network meta-analysis. A total of 49,265 observed individuals were included in this network meta-analysis. Compared to the 0-dose control group, the OR of all varicella infections were 0.087 (95% CI, 0.046-0.164) and 0.310 (95% CI, 0.198-0.484) for 2-doses and one-dose, respectively, which corresponded to VE of 69.0% (95% CI, 51.6-81.2) and VE of 91.3% (95% CI, 83.6-95.4) for 1- and 2-doses, respectively. Conclusions: A 2-dose vaccine strategy was able to significantly reduce varicella burden. The effectiveness of 2-dose vaccination on reducing the risk of infection was demonstrated by sound statistical evidence, which highlights the public health need for a 2-dose vaccine recommendation.

• Communications for Statistical Applications and Methods
• /
• v.9 no.1
• /
• pp.115-128
• /
• 2002

Hierarchical models are widely used for inference on correlated parameters as a compromise between underfitting and overfilling problems. In this paper, we take a Bayesian approach to analyzing hierarchical models and suggest a Markov chain Monte Carlo methods to get around computational difficulties in Bayesian analysis of the hierarchical models. We apply the method to a real data on smoking and lung cancer which are collected from cities in China.

• The Korean Journal of Applied Statistics
• /
• v.26 no.3
• /
• pp.453-470
• /
• 2013

In this paper, we consider a Bayesian analysis of the dose-effect relationship of cadmium to evaluate a benchmark dose(BMD). For this purpose, two dose-response curves commonly used in the toxicity study are fitted based on Bayesian methods to the data collected from the scientific literature on cadmium toxicity. Specifically, Bayesian meta-analysis and hierarchical modeling build an overall dose-effect relationship that use a piecewise linear model and Hill model, where the inter-study heterogeneity and inter-individual variability of dose and effect such as gender, age and ethnicity are accounted. Estimation of the unknown parameters is made by using a Markov chain Monte Carlo algorithm based user-friendly software WinBUGS. Benchmark dose estimates are evaluated for various cut-offs and compared with different tested subpopulations with with gender, age and ethnicity based on these two Bayesian hierarchical models.