Bayesian Meta-analysis Approach Research Articles

Bayesian Meta-Analysis: Impacts of Eating Habits and Habitats on Omega-3 Long-Chain Polyunsaturated Fatty Acid Composition and Growth in Cultured Fish.

Omega-3 long-chain polyunsaturated fatty acids (n-3 LC-PUFAs) such as eicosapentaenoic acid (20:5n-3, EPA) and docosahexaenoic acid (22:6n-3, DHA) offer protective benefits against various pathological conditions, including atherosclerosis, obesity, inflammation, and autoimmune diseases. Marine fish and seafood are the primary sources of n-3 LC-PUFAs in the human diet. However, the inclusion of fish oil in aquafeeds is declining due to limited availability, fluctuating prices, sustainability concerns, and replacement with vegetable oils. While comprehensive narrative reviews on the impact of substituting fish oil with vegetable oil in aquafeeds exist, quantitative studies are relatively scarce and mainly focused on comparing the source of vegetable oils. Herein, we employed, for the first time, a Bayesian meta-analysis approach, collecting research data from 81 articles to quantitatively analyze the effects of dietary n-3 LC-PUFA levels on the n-3 LC-PUFA composition and growth performance in cultured fish. Our findings indicate that with the exception of herbivorous fish, dietary n-3 LC-PUFA levels significantly affect the EPA and DHA levels in the livers and muscles of carnivorous, omnivorous, freshwater, and marine fish. Additionally, the growths of freshwater and herbivorous fish were less affected by changes in dietary n-3 LC-PUFA levels compared to that of carnivorous and marine fish.

Role of urban vegetation in air phytoremediation: differences between scientific research and environmental management perspectives

Air pollutant removal by urban vegetation is perceived to be a key ecosystem service for mitigating air pollution. However, the effectiveness of air phytoremediation in cities requires more synthesis to inform environmental management. A Bayesian meta-analysis approach was used to quantify the effectiveness of the removal of typical air pollutants—particle matter (PM), nitrogen oxide (NOx), sulfur dioxide (SO2) and ozone (O3)—by synthesizing global field measurement studies. The results revealed that urban vegetation can mitigate the growth of air pollutant concentrations, with reduction rates of 16.5~26.7% for PM, 13.9~36.2% for NOx, and 20.5~47.8% for SO2. However, they failed to significantly mitigate ground-level O3, corresponding to an increase of 5.1~25.9%. The variability in effect sizes was mainly influenced by the distance to nearest highway, ambient concentration, relative humidity, and green coverage. A questionnaire survey conducted in Shenzhen city (China) showed that most environmental managers supposed positive impacts of urban vegetation on all four air pollutants, which was at odds with our findings with respect to O3. This study can inform the lessening of discrepancies between scientific research and environmental managers’ perceptions on how to improve air phytoremediation for urban air pollution mitigation in China and elsewhere.

Bayesian meta-analysis models for cross cancer genomic investigation of pleiotropic effects using group structure.

An increasing number of genome-wide association studies (GWAS) summary statistics is made available to the scientific community. Exploiting these results from multiple phenotypes would permit identification of novel pleiotropic associations. In addition, incorporating prior biological information in GWAS such as group structure information (gene or pathway) has shown some success in classical GWAS approaches. However, this has not been widely explored in the context of pleiotropy. We propose a Bayesian meta-analysis approach (termed GCPBayes) that uses summary-level GWAS data across multiple phenotypes to detect pleiotropy at both group-level (gene or pathway) and within group (eg, at the SNP level). We consider both continuous and Dirac spike and slab priors for group selection. We also use a Bayesian sparse group selection approach with hierarchical spike and slab priors that enables us to select important variables both at the group level and within group. GCPBayes uses a Bayesian statistical framework based on Markov chain Monte Carlo (MCMC) Gibbs sampling. It can be applied to multiple types of phenotypes for studies with overlapping or nonoverlapping subjects, and takes into account heterogeneity in the effect size and allows for the opposite direction of the genetic effects across traits. Simulations show that the proposed methods outperform benchmark approaches such as ASSET and CPBayes in the ability to retrieve pleiotropic associations at both SNP and gene-levels. To illustrate the GCPBayes method, we investigate the shared genetic effects between thyroid cancer and breast cancer in candidate pathways.

Bayesian hierarchical dose-response meta-analysis of epidemiological studies: Modeling and target population prediction methods

When assessing the human risks due to exposure to environmental chemicals, traditional dose-response analyses are not straightforward when there are numerous high-quality epidemiological studies of priority cancer and non-cancer health outcomes. Given this wealth of information, selecting a single “best” study on which to base dose-response analyses is difficult and would potentially ignore much of the available data. Therefore, systematic approaches are necessary for the analysis of these rich databases. Examples are meta-analysis (and further, meta-regression), which are well established methods that consider and incorporate information from multiple studies into the estimation of risks due to exposure to environmental contaminants. In this paper, we propose a hierarchical, Bayesian meta-analysis approach for the dose-response analysis of multiple epidemiological studies. This paper is the second of two papers detailing this approach; the first covered “pre-analysis” steps necessary to prepare the data for dose-response modeling. This paper focuses on the hierarchical Bayesian approach to dose-response modeling and extrapolation of risk to populations of interest using the association between bladder cancer and oral inorganic arsenic (iAs) exposure as an illustrative case study. In particular, this paper addresses the modeling of both case-control and cohort studies with a flexible, logistic model in a hierarchical Bayesian framework that estimates study-specific slopes, as well as a pooled slope across all studies. This approach is akin to a random effects model in which no assumption is made a priori that there is a single, common slope for all included studies. Further, this paper also details extrapolation of the estimates of logistic slope to extra risk in a target population using a lifetable analysis and basic assumptions about background iAs exposure levels. In this case, the target population was the general United States population and information on all-cause mortality and incidence and mortality from bladder cancer was used to perform the lifetable analysis. The methods herein were developed for general use in investigating the association between any pollutant and observed health-effects in epidemiological studies. In order to demonstrate these methods, inorganic arsenic was chosen as a case study given the large epidemiological database that exists for this contaminant.

Augmenting disease maps: a Bayesian meta-analysis approach.

Analysis of spatial patterns of disease is a significant field of research. However, access to unit-level disease data can be difficult for privacy and other reasons. As a consequence, estimates of interest are often published at the small area level as disease maps. This motivates the development of methods for analysis of these ecological estimates directly. Such analyses can widen the scope of research by drawing more insights from published disease maps or atlases. The present study proposes a hierarchical Bayesian meta-analysis model that analyses the point and interval estimates from an online atlas. The proposed model is illustrated by modelling the published cancer incidence estimates available as part of the online Australian Cancer Atlas (ACA). The proposed model aims to reveal patterns of cancer incidence for the 20 cancers included in ACA in major cities, regional and remote areas. The model results are validated using the observed areal data created from unit-level data on cancer incidence in each of 2148 small areas. It is found that the meta-analysis models can generate similar patterns of cancer incidence based on urban/rural status of small areas compared with those already known or revealed by the analysis of observed data. The proposed approach can be generalized to other online disease maps and atlases.

A Bayesian Meta-Analysis on the Association Between Beta-Carotene and Bone Mineral Density

The results of epidemiological studies on the association between dietary β-carotene and bone mineral density (BMD) are remaining controversial. This study aimed to investigate the association between dietary β-carotene and BMD using a Bayesian meta-analysis approach. We searched PubMed, Embase, Cochrane Central Register, and Medline databases up to July 2019. Two authors independently screened articles, identified, and assessed the risk of bias. Bayesian hierarchical random effect models were used to synthesize data from individual studies. Five studies that consisted of three cross-sectional studies and two cohort studies involving 12,521 men and women met the inclusion criteria. Overall, dietary β-carotene reduced loss of BMD (risk ratio (RR) 0.89; 95% credible interval (CrI) 0.77–0.99). The probability of that β-carotene dietary reduced loss of BMD was 0.99. Sensitivity analyses indicate that approximately similar findings were observed across different prior information. We suggested that dietary intakes of β-carotene was associated with reduced the loss of bone mineral density for the general population. Further research will be recommended to confirm our findings.

Essential components in natriuretic peptide-guided management of heart failure: an intervention synthesis

AimTo identify the key components of natriuretic peptide (NP)-guided treatment interventions which reduced hospitalisation in patients with heart failure (HF).Methods and resultsWe extracted detailed information on the components of interventions...

An efficient Bayesian meta-analysis approach for studying cross-phenotype genetic associations.

Simultaneous analysis of genetic associations with multiple phenotypes may reveal shared genetic susceptibility across traits (pleiotropy). For a locus exhibiting overall pleiotropy, it is important to identify which specific traits underlie this association. We propose a Bayesian meta-analysis approach (termed CPBayes) that uses summary-level data across multiple phenotypes to simultaneously measure the evidence of aggregate-level pleiotropic association and estimate an optimal subset of traits associated with the risk locus. This method uses a unified Bayesian statistical framework based on a spike and slab prior. CPBayes performs a fully Bayesian analysis by employing the Markov Chain Monte Carlo (MCMC) technique Gibbs sampling. It takes into account heterogeneity in the size and direction of the genetic effects across traits. It can be applied to both cohort data and separate studies of multiple traits having overlapping or non-overlapping subjects. Simulations show that CPBayes can produce higher accuracy in the selection of associated traits underlying a pleiotropic signal than the subset-based meta-analysis ASSET. We used CPBayes to undertake a genome-wide pleiotropic association study of 22 traits in the large Kaiser GERA cohort and detected six independent pleiotropic loci associated with at least two phenotypes. This includes a locus at chromosomal region 1q24.2 which exhibits an association simultaneously with the risk of five different diseases: Dermatophytosis, Hemorrhoids, Iron Deficiency, Osteoporosis and Peripheral Vascular Disease. We provide an R-package ‘CPBayes’ implementing the proposed method.

Construction of a gene-gene interaction network with a combined score across multiple approaches.

Recent progress in computational methods for inves-tigating physical and functional gene interactions has provided new insights into the complexity of biological processes. An essential part of these methods is presented visually in the form of gene interaction networks that can be valuable in exploring the mechanisms of disease. Here, a combined network based on gene pairs with an extra layer of re-liability was constructed after converting and combining the gene pair scores using a novel algorithm across multiple approaches. Four groups of kidney cancer data sets from ArrayExpress were downloaded and analyzed to identify differentially expressed genes using a rank prod-ucts analysis tool. Gene co-expression network, protein-protein interac-tion, co-occurrence network and a combined network were constructed using empirical Bayesian meta-analysis approach, Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, an odds ratio formula of the cBioPortal for Cancer Genomics and a novel rank algorithm with combined score, respectively. The topological features of these networks were then compared to evaluate their performances. The results indicated that the gene pairs and their relationship rank-ings were not uniform. The values of topological parameters, such as clustering coefficient and the fitting coefficient R(2) of interaction net-work constructed using our ranked based combination score, were much greater than the other networks. The combined network had a classic small world property which transferred information quickly and displayed great resilience to the dysfunction of low-degree hubs with high-clustering and short average path length. It also followed distinct-ly a scale-free network with a higher reliability.

Safety and efficacy of non-vitamin K oral anticoagulants in non-valvular atrial fibrillation: a Bayesian meta-analysis approach

Introduction: Choosing between different non-vitamin K antagonist oral anticoagulants (NOACs) in non-valvular atrial fibrillation (NVAF) is difficult due to the absence of head to head comparative studies. We performed a Bayesian meta-analysis to explore similarities and differences between different NOACs and to rank treatments overall for safety and efficacy outcomes.Areas covered: Through a systematic literature search we identified randomized controlled Phase III trials of dabigatran, rivaroxaban, apixaban, and edoxaban versus adjusted-dose warfarin in patients with NVAF.Expert opinion: Warfarin ranked worst for all-cause mortality and intracranial bleedings and had a nil probability of ranking first for any outcome. The risk of major bleeding versus warfarin was lower with apixaban, dabigatran 110 mg, and both doses of edoxaban. All agents reduced the risk of intracranial bleeding versus warfarin. Edoxaban 30 mg was the best among the treatments being compared for major and gastrointestinal bleeding. Dabigatran 150 mg was the best for stroke and systemic embolism. This study suggests that NOACs are generally preferable to warfarin in patients with NVAF. However, safety and efficacy differences do exist among NOACs, which might drive their use in specific subsets of AF patients, allowing prescribers to tailor treatment to distinct patient profiles.

Bayesian Meta‐Experimental Design: Evaluating Cardiovascular Risk in New Antidiabetic Therapies to Treat Type 2 Diabetes

Recent guidance from the Food and Drug Administration for the evaluation of new therapies in the treatment of type 2 diabetes (T2DM) calls for a program-wide meta-analysis of cardiovascular (CV) outcomes. In this context, we develop a new Bayesian meta-analysis approach using survival regression models to assess whether the size of a clinical development program is adequate to evaluate a particular safety endpoint. We propose a Bayesian sample size determination methodology for meta-analysis clinical trial design with a focus on controlling the type I error and power. We also propose the partial borrowing power prior to incorporate the historical survival meta data into the statistical design. Various properties of the proposed methodology are examined and an efficient Markov chain Monte Carlo sampling algorithm is developed to sample from the posterior distributions. In addition, we develop a simulation-based algorithm for computing various quantities, such as the power and the type I error in the Bayesian meta-analysis trial design. The proposed methodology is applied to the design of a phase 2/3 development program including a noninferiority clinical trial for CV risk assessment in T2DM studies.

Underlying genetic models of inheritance in established type 2 diabetes associations.

For most associations of common single nucleotide polymorphisms (SNPs) with common diseases, the genetic model of inheritance is unknown. The authors extended and applied a Bayesian meta-analysis approach to data from 19 studies on 17 replicated associations with type 2 diabetes. For 13 SNPs, the data fitted very well to an additive model of inheritance for the diabetes risk allele; for 4 SNPs, the data were consistent with either an additive model or a dominant model; and for 2 SNPs, the data were consistent with an additive or recessive model. Results were robust to the use of different priors and after exclusion of data for which index SNPs had been examined indirectly through proxy markers. The Bayesian meta-analysis model yielded point estimates for the genetic effects that were very similar to those previously reported based on fixed- or random-effects models, but uncertainty about several of the effects was substantially larger. The authors also examined the extent of between-study heterogeneity in the genetic model and found generally small between-study deviation values for the genetic model parameter. Heterosis could not be excluded for 4 SNPs. Information on the genetic model of robustly replicated association signals derived from genome-wide association studies may be useful for predictive modeling and for designing biologic and functional experiments.