biasCorrectionInference {EvidenceSynthesis} | R Documentation |
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.
biasCorrectionInference(
likelihoodProfiles,
ncLikelihoodProfiles = NULL,
biasDistributions = NULL,
priorMean = 0,
priorSd = 1,
numsamps = 10000,
thin = 10,
doCorrection = TRUE,
seed = 1,
...
)
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 |
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 |
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
.
approximateSimplePosterior, fitBiasDistribution
# 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