To overcome these challenges, we introduce Bayesian Regularized Meta‐Analysis (BRMA), which selects relevant moderators from a larger set of candidates by shrinking small regression coefficients towards zero with regularizing (LASSO or horseshoe) priors. This method is suitable when there are many ...
The proposed meta-regression is formulated by jointly modeling the association parameters and the functional meta-predictors using Dirichlet process (DP) or local DP mixtures. In doing so, the functional meta-predictors are represented parsimoniously by the coefficients of the orthonormal basis. The ...
Surrogate endpoints, such as those of interest in chronic kidney disease (CKD), are often evaluated using Bayesian meta-regression. Trials used for the analysis can evaluate a variety of interventions for different sub-classifications of disease, which can introduce two additional goals in the analys...
Linear regression, as a versatile tool, empowers professionals in marketing, finance, healthcare, retail, to name a few, to make data-driven decisions, optimize strategies, and improve overall performance. The significance of causal estimation lies in its ability to guide interventions, enhance ...
Usefully, such an approach can be used to recover an effect size merely from repeated p-values as has been done in meta-analysis26. Fig. 2: A probability distribution for p-values. Describing the likelihood of p-values from repeated, independent tests of an effect with size \(\delta\),...
We adopted a meta-regression approach to integrate the information on individual consumption patterns extracted from the survey datasets with aggregate data on production, import and export from administrative records. We first pre-processed individual level data to calculate, independently for each survey...
Time to benefit for colorectal cancer screening: survival meta-analysis of flexible sigmoidoscopy trials. BMJ ▶ Vittinghoff E, Glidden DV, Shiboski SC, McCulloch CE (2012). Regression methods in biostatistics: linear, logistic, survival, and repeated measures models. Springer ▶ Wei Y, ...
values are parameters to be optimized. Hence, a modification of Bayesian networks in order to handle continuous variables is an important problem in the gene network estimation problem. A possible solution of this problem is given by using the nonparametric regression introduced in the next section....
Meta-analysis of continuous and binary outcomes Both full and aggregate data sets can be used Summaries and plots specific to meta-analysis, typical diagnostic plots Meta-regression / fixed effects modelling Compatibility with rstan and bayesplot features Automatic choice of priors or “plain-text” ...
A Bayesian network meta-analysis was performed to assess postoperative pain management, with subgroup analyses and meta-regression conducted to examine key factors influencing outcomes, such as the risk of bias, continuous catheter analgesia, and patient-controlled analgesia (PCA). Results The results ...