We propose a novel Bayesian (meta‐)regression model for binary outcomes on the additive risk scale. The model allows treatment effects, covariate effects, interactions and variance parameters to be estimated directly on the linear scale of clinical interest. We compared effect estimates from this ...
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, ...
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 ...
Strategy We applied bayesian meta-regression methods10 to multiple data sources with the goal of producing estimates of the prevalence and uncertainty interval of visual acuity loss or blindness, stratified by age group, sex, race/ethnicity, and state (50 US states and Washington, DC) for the ...
The deviation of the SUCRAs or P﹕cores from their theoretical values was mostly comparable over the methods, but differed depending on the heterogeneity and the geometry of the investigated network. Multivariate meta‐regression or Bayesian estimation using a half‐normal prior scaled to 0.5 seem ...
Accurate prediction of an individual's phenotype from their DNA sequence is one of the great promises of genomics and precision medicine. We extend a powerful individual-level data Bayesian multiple regression model (BayesR) to one that utilises summary statistics from genome-wide association studies...
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...
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” ...
Causal Estimation methods for Machine learning and Data Science Part III – Instrument Variable Analysis 1.0 Introduction In the past two blogs of this series we’ve been discussing causal estimation, a very important subject in data science, we delved into causal estimation using regression method ...
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....