{"doi":"10.1002/sim.8983","title":"Bayesian network meta‐regression hierarchical models using heavy‐tailed multivariate random effects with covariate‐dependent variances","abstract":"Network meta-analysis (NMA) is gaining popularity in evidence synthesis and network meta-regression allows us to incorporate potentially important covariates into network meta-analysis. In this article, we propose a Bayesian network meta-regression hierarchical model and assume a general multivariate t distribution for the random treatment effects. The multivariate t distribution is desired for heavy-tailed random effects and converges to the multivariate normal distribution when the degrees of freedom go to infinity. Moreover, in NMA, some treatments are compared only in a single study. To overcome such sparsity, we propose a log-linear regression model for the variances of the random effects and incorporate aggregate covariates into modeling the variance components. We develop a Markov chain Monte Carlo sampling algorithm to sample from the posterior distribution via the collapsed Gibbs technique. We further use the deviance information criterion and the logarithm of the pseudo-marginal likelihood for model comparison. A simulation study is conducted and a detailed analysis from our motivating case study is carried out to further demonstrate the proposed methodology.","journal":"Statistics in Medicine","year":2021,"id":203405,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":5,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.951,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":784615,"name":"Daeyoung Lim","orcid":"0000-0003-1715-894X","position":1,"is_corresponding":false},{"id":444249,"name":"Ming‐Hui Chen","orcid":"0000-0003-1935-2447","position":2,"is_corresponding":false},{"id":390426,"name":"Joseph G. Ibrahim","orcid":"0000-0003-2428-6552","position":3,"is_corresponding":false},{"id":521958,"name":"Sungduk Kim","orcid":"0000-0002-7985-7204","position":4,"is_corresponding":false},{"id":521959,"name":"Arvind Shah","orcid":"0000-0001-7373-5858","position":5,"is_corresponding":false},{"id":548592,"name":"Jianxin Lin","orcid":"0009-0006-7093-0969","position":6,"is_corresponding":false},{"id":784614,"name":"Hao Li","orcid":"0000-0001-9743-6983","position":0,"is_corresponding":true}],"reference_count":34,"raw_metadata":null,"created_at":"2026-07-18T23:51:18.050175Z","pmid":"33846992","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}