EvidenceSynthesis (version 0.5.0)

biasCorrectionInference: Bias Correction with Inference

Description

Perform Bayesian posterior inference regarding an outcome of interest with bias correction using negative control analysis. There is an option to not perform bias correction so that un-corrected results can be obtained.

Usage

biasCorrectionInference(
  likelihoodProfiles,
  ncLikelihoodProfiles = NULL,
  biasDistributions = NULL,
  priorMean = 0,
  priorSd = 1,
  numsamps = 10000,
  thin = 10,
  doCorrection = TRUE,
  seed = 1,
  ...
)

Value

A dataframe with five columns, including posterior median and mean of log RR effect size estimates, 95% credible intervals (ci95Lb and ci95Ub), posterior probability that log RR > 0 (p1), and the period or group ID (Id).

It is accompanied by the following attributes:

• samplesCorrected: all MCMC samples for the bias corrected log RR effect size estimate.

• samplesRaw: all MCMC samples for log RR effect size estimate, without bias correction.

• biasDistributions: the learned empirical bias distribution from negative control analysis.

• summaryRaw: a summary dataframe (same format as in the main result) without bias correction.

• corrected: a logical flag indicating if bias correction has been performed; = TRUE if doCorrection = TRUE.

Arguments

likelihoodProfiles

A list of grid profile likelihoods for the outcome of interest.

ncLikelihoodProfiles

Likelihood profiles for the negative control outcomes. Must be a list of lists of profile likelihoods; if there is only one analysis period, then this must be a length-1 list, with the first item as a list all outcome-wise profile likelihoods.

biasDistributions

Pre-saved bias distribution(s), formatted as the output from fitBiasDistribution() or sequentialFitBiasDistribution(). If NULL, then ncLikelihoodProfiles must be provided.

priorMean

Prior mean for the effect size (log rate ratio).

priorSd

Prior standard deviation for the effect size (log rate ratio).

numsamps

Total number of MCMC samples needed.

thin

Thinning frequency: how many iterations before another sample is obtained?

doCorrection

Whether or not to perform bias correction; default: TRUE.

seed

Seed for the random number generator.

...

Arguments to be passed to sequentialFitBiasDistribution() to fit bias distributions if biasDistributions is NULL.

See Also

approximateSimplePosterior, fitBiasDistribution

Examples

# load example data
data("ncLikelihoods")
data("ooiLikelihoods")

# perform sequential analysis with bias correction, using the t model
# NOT RUN
# bbcResults = biasCorrectionInference(ooiLikelihoods,
#                                      ncLikelihoodProfiles = ncLikelihoods,
#                                      robust = TRUE,
#                                      seed = 42)

# check out analysis summary
# bbcResults