biasCorrectionInference {EvidenceSynthesis}  R Documentation 
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 uncorrected results can be obtained.
Usage
biasCorrectionInference(
likelihoodProfiles,
ncLikelihoodProfiles = NULL,
biasDistributions = NULL,
priorMean = 0,
priorSd = 1,
numsamps = 10000,
thin = 10,
doCorrection = TRUE,
seed = 1,
...
)
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 length1 list, with the first item as a list all outcomewise profile likelihoods. 
biasDistributions 
Presaved bias distribution(s), formatted as the output
from 
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 
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 ifdoCorrection = TRUE
.
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