Synthesizing Causal Evidence in a Distributed Research Network
Approximate Bayesian posterior for hierarchical Normal model
Approximate a likelihood function
Approximate simple Bayesian posterior
Bias Correction with Inference
Compute a Bayesian random-effects meta-analysis
Compute the point estimate and confidence interval given a likelihood ...
Compute a fixed-effect meta-analysis
Create simulation settings
A custom function to approximate a log likelihood function
Detect the type of likelihood approximation based on the data format
EvidenceSynthesis: Synthesizing Causal Evidence in a Distributed Resea...
Fit Bias Distribution
Plot bias correction inference
Plot bias distributions
Plot covariate balances
Plot empirical null distributions
Plot the likelihood approximation
Plot MCMC trace
Create a forest plot
Plot MCMC trace for individual databases
Plot posterior density per database
Plot posterior density
Plot the propensity score distribution
Prepare to plot the propensity score distribution
Fit Bias Distribution Sequentially or in Groups
Simulate survival data for multiple databases
The skew normal function to approximate a log likelihood function
Determine if Java virtual machine supports Java
Routines for combining causal effect estimates and study diagnostics across multiple data sites in a distributed study, without sharing patient-level data. Allows for normal and non-normal approximations of the data-site likelihood of the effect parameter.
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