mvmr_egger_rjags: Bayesian implementation of the MVMR-Egger model with choice...

In mrbayes: Bayesian Summary Data Models for Mendelian Randomization Studies

Bayesian implementation of the MVMR-Egger model with choice of prior distributions fitted using JAGS.

Description

Bayesian implementation of the MVMR-Egger model with choice of prior distributions fitted using JAGS.

Usage

mvmr_egger_rjags(
  object,
  prior = "default",
  betaprior = "",
  sigmaprior = "",
  orientate = 1,
  n.chains = 3,
  n.burn = 1000,
  n.iter = 5000,
  seed = NULL,
  rho = 0.5,
  ...
)

Arguments

object	A data object of class mvmr_format.
prior	A character string for selecting the prior distributions; "default" selects a non-informative set of priors; "weak" selects weakly informative priors; "pseudo" selects a pseudo-horseshoe prior on the causal effect;
betaprior	A character string in JAGS syntax to allow a user defined prior for the causal effect.
sigmaprior	A character string in JAGS syntax to allow a user defined prior for the residual standard deviation.
orientate	Numeric value to indicate the oriented exposure
n.chains	Numeric indicating the number of chains used in the MCMC estimation, the default is 3 chains.
n.burn	Numeric indicating the burn-in period of the Bayesian MCMC estimation. The default is 1000 samples.
n.iter	Numeric indicating the number of iterations in the Bayesian MCMC estimation. The default is 5000 iterations.
seed	Numeric indicating the random number seed. The default is the rjags default.
rho	Numeric indicating the correlation coefficient input into the joint prior distribution. The default value is 0.5.
...	Additional arguments passed through to rjags::jags.model().

Value

An object of class mveggerjags containing the following components:

AvgPleio

The mean of the simulated pleiotropic effect

CausalEffect

The mean of the simulated causal effect

StandardError

Standard deviation of the simulated causal effect

sigma

The value of the residual standard deviation

CredibleInterval

The credible interval for the causal effect, which includes the lower (2.5%), median (50%) and upper intervals (97.5%)

samples

Output of the Bayesian MCMC samples

Priors

The specified priors

References

Bowden et. al., Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. International Journal of Epidemiology 2015. 44(2): p. 512-525. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1093/ije/dyv080")}

Examples

if (requireNamespace("rjags", quietly = TRUE)) {
## Not run: 
dat <- mvmr_format(
  rsid = dodata$rsid,
  xbeta = cbind(dodata$ldlcbeta,dodata$hdlcbeta,dodata$tgbeta),
  ybeta = dodata$chdbeta,
  xse = cbind(dodata$ldlcse,dodata$hdlcse,dodata$tgse),
  yse = dodata$chdse
)

fit <- mvmr_egger_rjags(dat)
summary(fit)
plot(fit$samples)
# 90% credible interval
fitdf <- do.call(rbind.data.frame, fit$samples)
cri90 <- sapply(fitdf, quantile, probs = c(0.05, 0.95))
print(cri90)

## End(Not run)
}

mrbayes documentation built on Sept. 11, 2024, 6:45 p.m.