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 ...
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 c
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 ...
In line 13, we reshape the data to the required 2D format for scikit-learn’s LinearRegression model. The model estimates the effect of compulsory schooling on education, isolating the variation in education directly caused by the instrument (compulsory schooling) and removing the influence of abil...
In this paper, a Bayesian model is proposed for meta-analysis of treatment effectiveness data which are generally discrete Binomial and sparse. A bivariate class of priors is imposed to accommodate a wide range of heterogeneity between the multicenter clinical trials involved in the study. ...
LASSO used a linear regression model to integrate prior TF-gene interactions and gene expression data and predicted one value for each TF-gene interaction. The NARROMI approach inferred GRNs using gene expression data only without any prior on TF-gene interactions, and also, it made single-...
In addition, the particular structure of the model in[11] makes it difficult to incorporate the effect of the factors on regions that are very strongly conserved. This paper extends the Bayesian hierarchical regression model in[10] by placing a nonparametric prior on the distribution of the ...
The true and false positive rates are transformed so that one may use bilogistic regression to determine the accuracy of the combined tests where the posterior distribution of the parameters of the model are determined. Bayesian inferences are based on the posterior distribution of the SROC curve ...
Advances in the meta-analysis of heterogeneous clinical trials II: the quality effects model. Contemp Clin Trials. 2015;45:123–9. Article Google Scholar Wood SN. Thin plate regression splines. J R Stat Soc Ser B (Stat Methodol). 2003;65(1):95–114. Article Google Scholar Gmel G, ...
Note that we do not take this example to refute the effectiveness of accuracy nudges in general, as this would require a broader reanalysis of all relevant papers (e.g., Martel et al., 2024; Pennycook et al., 2020) using meta-analytic techniques, which is outside the scope of the ...