Package 'EvidenceSynthesis' reference manual

Title:	Synthesizing Causal Evidence in a Distributed Research Network
Description:	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.
Authors:	Martijn Schuemie [aut, cre], Marc A. Suchard [aut], Fan Bu [aut], Observational Health Data Science and Informatics [cph]
Maintainer:	Martijn Schuemie <[email protected]>
License:	Apache License 2.0
Version:	0.5.0
Built:	2024-09-28 07:27:33 UTC
Source:	CRAN

Approximate Bayesian posterior for hierarchical Normal model

Description

Approximate a Bayesian posterior from a set ofCyclops likelihood profiles under a hierarchical normal model using the Markov chain Monte Carlo engine BEAST.

Usage

approximateHierarchicalNormalPosterior(
  likelihoodProfiles,
  chainLength = 1100000,
  burnIn = 1e+05,
  subSampleFrequency = 100,
  effectPriorMean = 0,
  effectPriorSd = 0.5,
  nu0 = 1,
  sigma0 = 1,
  effectStartingValue = 0,
  precisionStartingValue = 1,
  seed = 1
)
approximateHierarchicalNormalPosterior(
  likelihoodProfiles,
  chainLength = 1100000,
  burnIn = 1e+05,
  subSampleFrequency = 100,
  effectPriorMean = 0,
  effectPriorSd = 0.5,
  nu0 = 1,
  sigma0 = 1,
  effectStartingValue = 0,
  precisionStartingValue = 1,
  seed = 1
)

Arguments

`likelihoodProfiles`	List of grid likelihoods profiled with `Cyclops`.
`chainLength`	Number of MCMC iterations.
`burnIn`	Number of MCMC iterations to consider as burn in.
`subSampleFrequency`	Subsample frequency for the MCMC.
`effectPriorMean`	Prior mean for global parameter
`effectPriorSd`	Prior standard deviation for the global parameter
`nu0`	Prior "sample size" for precision (with precision ~ gamma(nu0/2, nu0*sigma0/2))
`sigma0`	Prior "variance" for precision (with precision ~ gamma(nu0/2, nu0*sigma0/2))
`effectStartingValue`	Initial value for global & local parameter
`precisionStartingValue`	Initial value for the precision
`seed`	Seed for the random number generator.

Value

A data frame with the point estimates and 95% credible intervals for the the global and local parameter, as well as the global precision. Attributes of the data frame contain the MCMC trace for diagnostics.

Examples

# TBD

# TBD

Approximate a likelihood function

Description

Approximate the likelihood function using a parametric (normal, skew-normal, or custom parametric), or grid approximation. The approximation does not reveal person-level information, and can therefore be shared among data sites. When counts are low, a normal approximation might not be appropriate.

Usage

approximateLikelihood(
  cyclopsFit,
  parameter = 1,
  approximation = "custom",
  bounds = c(log(0.1), log(10))
)
approximateLikelihood(
  cyclopsFit,
  parameter = 1,
  approximation = "custom",
  bounds = c(log(0.1), log(10))
)

Arguments

`cyclopsFit`	A model fitted using the `Cyclops::fitCyclopsModel()` function.
`parameter`	The parameter in the `cyclopsFit` object to profile.
`approximation`	The type of approximation. Valid options are `'normal'`, `'skew normal'`, `'custom'`, `'grid'`, or `'adaptive grid'`.
`bounds`	The bounds on the effect size used to fit the approximation.

Value

A vector of parameters of the likelihood approximation.

Examples

# Simulate some data for this example:
populations <- simulatePopulations()

cyclopsData <- Cyclops::createCyclopsData(Surv(time, y) ~ x + strata(stratumId),
  data = populations[[1]],
  modelType = "cox"
)
cyclopsFit <- Cyclops::fitCyclopsModel(cyclopsData)
approximation <- approximateLikelihood(cyclopsFit, "x")
approximation

# (Estimates in this example will vary due to the random simulation)

# Simulate some data for this example:
populations <- simulatePopulations()

cyclopsData <- Cyclops::createCyclopsData(Surv(time, y) ~ x + strata(stratumId),
  data = populations[[1]],
  modelType = "cox"
)
cyclopsFit <- Cyclops::fitCyclopsModel(cyclopsData)
approximation <- approximateLikelihood(cyclopsFit, "x")
approximation

# (Estimates in this example will vary due to the random simulation)

Approximate simple Bayesian posterior

Description

Approximate a Bayesian posterior from a Cyclops likelihood profile and normal prior using the Markov chain Monte Carlo engine BEAST.

Usage

approximateSimplePosterior(
  likelihoodProfile,
  chainLength = 1100000,
  burnIn = 1e+05,
  subSampleFrequency = 100,
  priorMean = 0,
  priorSd = 0.5,
  startingValue = 0,
  seed = 1
)
approximateSimplePosterior(
  likelihoodProfile,
  chainLength = 1100000,
  burnIn = 1e+05,
  subSampleFrequency = 100,
  priorMean = 0,
  priorSd = 0.5,
  startingValue = 0,
  seed = 1
)

Arguments

`likelihoodProfile`	Named vector containing grid likelihood data from `Cyclops`.
`chainLength`	Number of MCMC iterations.
`burnIn`	Number of MCMC iterations to consider as burn in.
`subSampleFrequency`	Subsample frequency for the MCMC.
`priorMean`	Prior mean for the regression parameter
`priorSd`	Prior standard deviation for the regression parameter
`startingValue`	Initial state for regression parameter
`seed`	Seed for the random number generator.

Value

A data frame with the point estimates and 95% credible intervals for the regression parameter. Attributes of the data frame contain the MCMC trace for diagnostics.

Examples

# Simulate some data for this example:
population <- simulatePopulations(createSimulationSettings(nSites = 1))[[1]]

# Fit a Cox regression at each data site, and approximate likelihood function:
cyclopsData <- Cyclops::createCyclopsData(Surv(time, y) ~ x + strata(stratumId),
  data = population,
  modelType = "cox"
)
cyclopsFit <- Cyclops::fitCyclopsModel(cyclopsData)
likelihoodProfile <- approximateLikelihood(cyclopsFit, parameter = "x", approximation = "grid")

# Run MCMC
mcmcTraces <- approximateSimplePosterior(
  likelihoodProfile = likelihoodProfile,
  priorMean = 0, priorSd = 100
)

# Report posterior expectation
mean(mcmcTraces$theta)

# (Estimates in this example will vary due to the random simulation)

# Simulate some data for this example:
population <- simulatePopulations(createSimulationSettings(nSites = 1))[[1]]

# Fit a Cox regression at each data site, and approximate likelihood function:
cyclopsData <- Cyclops::createCyclopsData(Surv(time, y) ~ x + strata(stratumId),
  data = population,
  modelType = "cox"
)
cyclopsFit <- Cyclops::fitCyclopsModel(cyclopsData)
likelihoodProfile <- approximateLikelihood(cyclopsFit, parameter = "x", approximation = "grid")

# Run MCMC
mcmcTraces <- approximateSimplePosterior(
  likelihoodProfile = likelihoodProfile,
  priorMean = 0, priorSd = 100
)

# Report posterior expectation
mean(mcmcTraces$theta)

# (Estimates in this example will vary due to the random simulation)

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,
  ...
)
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 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.

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.

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

# 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

Compute a Bayesian random-effects meta-analysis

Description

Compute a Bayesian meta-analysis using the Markov chain Monte Carlo (MCMC) engine BEAST. A normal and half-normal prior are used for the mu and tau parameters, respectively, with standard deviations as defined by the priorSd argument.

Usage

computeBayesianMetaAnalysis(
  data,
  chainLength = 1100000,
  burnIn = 1e+05,
  subSampleFrequency = 100,
  priorSd = c(2, 0.5),
  alpha = 0.05,
  robust = FALSE,
  df = 4,
  seed = 1
)
computeBayesianMetaAnalysis(
  data,
  chainLength = 1100000,
  burnIn = 1e+05,
  subSampleFrequency = 100,
  priorSd = c(2, 0.5),
  alpha = 0.05,
  robust = FALSE,
  df = 4,
  seed = 1
)

Arguments

`data`	A data frame containing either normal, skew-normal, custom parametric, or grid likelihood data, with one row per database.
`chainLength`	Number of MCMC iterations.
`burnIn`	Number of MCMC iterations to consider as burn in.
`subSampleFrequency`	Subsample frequency for the MCMC.
`priorSd`	A two-dimensional vector with the standard deviation of the prior for mu and tau, respectively.
`alpha`	The alpha (expected type I error) used for the credible intervals.
`robust`	Whether or not to use a t-distribution model; default: FALSE.
`df`	Degrees of freedom for the t-model, only used if robust is TRUE.
`seed`	The seed for the random number generator.

Value

A data frame with the point estimates and 95% credible intervals for the mu and tau parameters (the mean and standard deviation of the distribution from which the per-site effect sizes are drawn). Attributes of the data frame contain the MCMC trace and the detected approximation type.

Examples

# Simulate some data for this example:
populations <- simulatePopulations()

# Fit a Cox regression at each data site, and approximate likelihood function:
fitModelInDatabase <- function(population) {
  cyclopsData <- Cyclops::createCyclopsData(Surv(time, y) ~ x + strata(stratumId),
    data = population,
    modelType = "cox"
  )
  cyclopsFit <- Cyclops::fitCyclopsModel(cyclopsData)
  approximation <- approximateLikelihood(cyclopsFit, parameter = "x", approximation = "custom")
  return(approximation)
}
approximations <- lapply(populations, fitModelInDatabase)
approximations <- do.call("rbind", approximations)

# At study coordinating center, perform meta-analysis using per-site approximations:
estimate <- computeBayesianMetaAnalysis(approximations)
estimate

# (Estimates in this example will vary due to the random simulation)

# Simulate some data for this example:
populations <- simulatePopulations()

# Fit a Cox regression at each data site, and approximate likelihood function:
fitModelInDatabase <- function(population) {
  cyclopsData <- Cyclops::createCyclopsData(Surv(time, y) ~ x + strata(stratumId),
    data = population,
    modelType = "cox"
  )
  cyclopsFit <- Cyclops::fitCyclopsModel(cyclopsData)
  approximation <- approximateLikelihood(cyclopsFit, parameter = "x", approximation = "custom")
  return(approximation)
}
approximations <- lapply(populations, fitModelInDatabase)
approximations <- do.call("rbind", approximations)

# At study coordinating center, perform meta-analysis using per-site approximations:
estimate <- computeBayesianMetaAnalysis(approximations)
estimate

# (Estimates in this example will vary due to the random simulation)

Compute the point estimate and confidence interval given a likelihood function approximation

Description

Compute the point estimate and confidence interval given a likelihood function approximation

Usage

computeConfidenceInterval(approximation, alpha = 0.05)
computeConfidenceInterval(approximation, alpha = 0.05)

Arguments

`approximation`	An approximation of the likelihood function as fitted using the `approximateLikelihood()` function.
`alpha`	The alpha (expected type I error).

Details

Compute the point estimate and confidence interval given a likelihood function approximation.

Value

A data frame containing the point estimate, and upper and lower bound of the confidence interval.

Examples

# Simulate some data for this example:
populations <- simulatePopulations()

cyclopsData <- Cyclops::createCyclopsData(Surv(time, y) ~ x + strata(stratumId),
  data = populations[[1]],
  modelType = "cox"
)
cyclopsFit <- Cyclops::fitCyclopsModel(cyclopsData)
approximation <- approximateLikelihood(cyclopsFit, "x")
computeConfidenceInterval(approximation)

# Simulate some data for this example:
populations <- simulatePopulations()

cyclopsData <- Cyclops::createCyclopsData(Surv(time, y) ~ x + strata(stratumId),
  data = populations[[1]],
  modelType = "cox"
)
cyclopsFit <- Cyclops::fitCyclopsModel(cyclopsData)
approximation <- approximateLikelihood(cyclopsFit, "x")
computeConfidenceInterval(approximation)

Compute a fixed-effect meta-analysis

Description

Compute a fixed-effect meta-analysis using a choice of various likelihood approximations.

Usage

computeFixedEffectMetaAnalysis(data, alpha = 0.05)
computeFixedEffectMetaAnalysis(data, alpha = 0.05)

Arguments

`data`	A data frame containing either normal, skew-normal, custom parametric, or grid likelihood data. One row per database.
`alpha`	The alpha (expected type I error) used for the confidence intervals.

Value

The meta-analytic estimate, expressed as the point estimate hazard ratio (rr), its 95 percent confidence interval (lb, ub), as well as the log of the point estimate (logRr), and the standard error (seLogRr).

Examples

# Simulate some data for this example:
populations <- simulatePopulations()

# Fit a Cox regression at each data site, and approximate likelihood function:
fitModelInDatabase <- function(population) {
  cyclopsData <- Cyclops::createCyclopsData(Surv(time, y) ~ x + strata(stratumId),
    data = population,
    modelType = "cox"
  )
  cyclopsFit <- Cyclops::fitCyclopsModel(cyclopsData)
  approximation <- approximateLikelihood(cyclopsFit, parameter = "x", approximation = "custom")
  return(approximation)
}
approximations <- lapply(populations, fitModelInDatabase)
approximations <- do.call("rbind", approximations)

# At study coordinating center, perform meta-analysis using per-site approximations:
computeFixedEffectMetaAnalysis(approximations)

# (Estimates in this example will vary due to the random simulation)

# Simulate some data for this example:
populations <- simulatePopulations()

# Fit a Cox regression at each data site, and approximate likelihood function:
fitModelInDatabase <- function(population) {
  cyclopsData <- Cyclops::createCyclopsData(Surv(time, y) ~ x + strata(stratumId),
    data = population,
    modelType = "cox"
  )
  cyclopsFit <- Cyclops::fitCyclopsModel(cyclopsData)
  approximation <- approximateLikelihood(cyclopsFit, parameter = "x", approximation = "custom")
  return(approximation)
}
approximations <- lapply(populations, fitModelInDatabase)
approximations <- do.call("rbind", approximations)

# At study coordinating center, perform meta-analysis using per-site approximations:
computeFixedEffectMetaAnalysis(approximations)

# (Estimates in this example will vary due to the random simulation)

Create simulation settings

Description

Create an object specifying a simulation. Currently only Cox proportional hazard models are supported.

Usage

createSimulationSettings(
  nSites = 5,
  n = 10000,
  treatedFraction = 0.2,
  nStrata = 10,
  minBackgroundHazard = 2e-07,
  maxBackgroundHazard = 2e-05,
  hazardRatio = 2,
  randomEffectSd = 0
)
createSimulationSettings(
  nSites = 5,
  n = 10000,
  treatedFraction = 0.2,
  nStrata = 10,
  minBackgroundHazard = 2e-07,
  maxBackgroundHazard = 2e-05,
  hazardRatio = 2,
  randomEffectSd = 0
)

Arguments

`nSites`	Number of database sites to simulate.
`n`	Number of subjects per site. Either a single number, or a vector of length nSites.
`treatedFraction`	Fraction of subjects that is treated. Either a single number, or a vector of length nSites.
`nStrata`	Number of strata per site. Either a single number, or a vector of length nSites.
`minBackgroundHazard`	Minimum background hazard. Either a single number, or a vector of length nSites.
`maxBackgroundHazard`	Maximum background hazard. Either a single number, or a vector of length nSites.
`hazardRatio`	Hazard ratio.
`randomEffectSd`	Standard deviation of the log(hazardRatio). Fixed effect if equal to 0.

Value

An object of type simulationSettings, to be used in the simulatePopulations() function.

Examples

settings <- createSimulationSettings(nSites = 1, hazardRatio = 2)
populations <- simulatePopulations(settings)

# Fit a Cox regression for the simulated data site:
cyclopsData <- Cyclops::createCyclopsData(Surv(time, y) ~ x + strata(stratumId),
  data = populations[[1]],
  modelType = "cox"
)
cyclopsFit <- Cyclops::fitCyclopsModel(cyclopsData)
coef(cyclopsFit)

# (Estimates in this example will vary due to the random simulation)

settings <- createSimulationSettings(nSites = 1, hazardRatio = 2)
populations <- simulatePopulations(settings)

# Fit a Cox regression for the simulated data site:
cyclopsData <- Cyclops::createCyclopsData(Surv(time, y) ~ x + strata(stratumId),
  data = populations[[1]],
  modelType = "cox"
)
cyclopsFit <- Cyclops::fitCyclopsModel(cyclopsData)
coef(cyclopsFit)

# (Estimates in this example will vary due to the random simulation)

A custom function to approximate a log likelihood function

Description

A custom function to approximate a log likelihood function

Usage

customFunction(x, mu, sigma, gamma)
customFunction(x, mu, sigma, gamma)

Arguments

`x`	The log(hazard ratio) for which to approximate the log likelihood.
`mu`	The position parameter.
`sigma`	The scale parameter.
`gamma`	The skew parameter.

Details

A custom parametric function designed to approximate the shape of the Cox log likelihood function. When gamma = 0 this function is the normal distribution.

Value

The approximate log likelihood for the given x.

Examples

customFunction(x = 0:3, mu = 0, sigma = 1, gamma = 0)

customFunction(x = 0:3, mu = 0, sigma = 1, gamma = 0)

Detect the type of likelihood approximation based on the data format

Description

Detect the type of likelihood approximation based on the data format

Usage

detectApproximationType(data, verbose = TRUE)
detectApproximationType(data, verbose = TRUE)

Arguments

`data`	The approximation data. Can be a single approximation, or approximations from multiple sites.
`verbose`	Should the detected type be communicated to the user?

Value

A character vector with one of the following values: "normal", "custom", "skew normal", "pooled", "grid", or "adaptive grid".

Examples

detectApproximationType(data.frame(logRr = 1, seLogRr = 0.1))

detectApproximationType(data.frame(logRr = 1, seLogRr = 0.1))

Fit Bias Distribution

Description

Learn an empirical distribution on estimation bias by simultaneously analyzing a large set of negative control outcomes by a Bayesian hierarchical model through MCMC. Analysis is based on a list of extracted likelihood profiles.

Usage

fitBiasDistribution(
  likelihoodProfiles,
  priorSds = c(2, 0.5),
  numsamps = 10000,
  thin = 10,
  minNCs = 5,
  robust = FALSE,
  df = 4,
  seed = 1
)
fitBiasDistribution(
  likelihoodProfiles,
  priorSds = c(2, 0.5),
  numsamps = 10000,
  thin = 10,
  minNCs = 5,
  robust = FALSE,
  df = 4,
  seed = 1
)

Arguments

`likelihoodProfiles`	A list of grid profile likelihoods regarding negative controls.
`priorSds`	A two-dimensional vector with the standard deviation of the prior for the average bias and the sd/scale parameter, respectively.
`numsamps`	Total number of MCMC samples needed.
`thin`	Thinning frequency: how many iterations before another sample is obtained?
`minNCs`	Minimum number of negative controls needed to fit a bias distribution; default (also recommended): 5.
`robust`	Whether or not to use a t-distribution model; default: FALSE.
`df`	Degrees of freedom for the t-model, only used if robust is TRUE.
`seed`	Seed for the random number generator.

Value

A dataframe with three columns and numsamps number of rows. Column mean includes MCMC samples for the average bias, scale for the sd/scale parameter, and bias for predictive samples of the bias.

Examples

# load example data
data("ncLikelihoods")

# fit a bias distributions by analyzing a set of negative control outcomes
# for example, for the 5th analysis period, and using the t model
# NOT RUN
# biasDistribution = fitBiasDistribution(ncLikelihoods[[5]], robust = TRUE)

# load example data
data("ncLikelihoods")

# fit a bias distributions by analyzing a set of negative control outcomes
# for example, for the 5th analysis period, and using the t model
# NOT RUN
# biasDistribution = fitBiasDistribution(ncLikelihoods[[5]], robust = TRUE)

Example profile likelihoods for negative control outcomes

Description

A list that contain profile likelihoods a large set of negative control outcomes. They are extracted from a real-world observational healthcare database, with the likelihoods profiled using adaptive grids using the Cyclops package.

Usage

ncLikelihoods
ncLikelihoods

Format

An object of class list containing 12 lists, where each list includes several dataframes ith column point and value for adaptive grid profile likelihoods.

References

Schuemie et al. (2022). Vaccine safety surveillance using routinely collected healthcare data—an empirical evaluation of epidemiological designs. Frontiers in Pharmacology.

Examples

data("ncLikelihoods")
ncLikEx <- ncLikelihoods[["5"]][[1]]

plot(value ~ point, data = ncLikEx)

data("ncLikelihoods")
ncLikEx <- ncLikelihoods[["5"]][[1]]

plot(value ~ point, data = ncLikEx)

Example profile likelihoods for a synthetic outcome of interest

Description

A list that contain profile likelihoods for a synthetic outcome of interest. They are extracted from a real-world observational healthcare database, with the likelihoods profiled using adaptive grids using the Cyclops package.

Usage

ooiLikelihoods
ooiLikelihoods

Format

An objects of class list; the list contains 12 lists, where each list includes several dataframes with column point and value for adaptive grid profile likelihoods.

References

Schuemie et al. (2022). Vaccine safety surveillance using routinely collected healthcare data—an empirical evaluation of epidemiological designs. Frontiers in Pharmacology.

Examples

data("ooiLikelihoods")
ooiLikEx <- ooiLikelihoods[["5"]][[1]]

plot(value ~ point, data = ooiLikEx)

data("ooiLikelihoods")
ooiLikEx <- ooiLikelihoods[["5"]][[1]]

plot(value ~ point, data = ooiLikEx)

Plot bias correction inference

Description

Plot bias correction inference

Usage

plotBiasCorrectionInference(
  bbcResult,
  type = "raw",
  ids = bbcResult$Id,
  limits = c(-3, 3),
  logScale = FALSE,
  numericId = TRUE,
  fileName = NULL
)
plotBiasCorrectionInference(
  bbcResult,
  type = "raw",
  ids = bbcResult$Id,
  limits = c(-3, 3),
  logScale = FALSE,
  numericId = TRUE,
  fileName = NULL
)

Arguments

`bbcResult`	A (sequential) analysis object generated by the `biasCorrectionInference()` function.
`type`	The type of plot. Must be one of `c("corrected", "raw", "compare")`.
`ids`	IDs of the periods/groups to plot result for; default is all IDs.
`limits`	The limits on log RR for plotting.
`logScale`	Whether or not to show bias in log-RR; default FALSE (shown in RR).
`numericId`	Whether or not to treat `Id` as a numeric variable; default: TRUE.
`fileName`	Name of the file where the plot should be saved, for example 'plot.png'. See the function ggplot2::ggsave in the ggplot2 package for supported file formats.

Details

Plot empirical bias distributions learned from analyzing negative controls.

Value

A ggplot object. Use the ggplot2::ggsave function to save to file.

Examples

# Perform sequential analysis using Bayesian bias correction for this example:
data("ncLikelihoods")
data("ooiLikelihoods")
# NOT RUN
# bbcSequential = biasCorrectionInference(ooiLikelihoods, ncLikelihoodProfiles = ncLikelihoods)

# Plot it
# NOT RUN
# plotBiasCorrectionInference(bbcSequential, type = "corrected")

# Perform sequential analysis using Bayesian bias correction for this example:
data("ncLikelihoods")
data("ooiLikelihoods")
# NOT RUN
# bbcSequential = biasCorrectionInference(ooiLikelihoods, ncLikelihoodProfiles = ncLikelihoods)

# Plot it
# NOT RUN
# plotBiasCorrectionInference(bbcSequential, type = "corrected")

Plot bias distributions

Description

Plot bias distributions

Usage

plotBiasDistribution(
  biasDist,
  limits = c(-2, 2),
  logScale = FALSE,
  numericId = TRUE,
  fileName = NULL
)
plotBiasDistribution(
  biasDist,
  limits = c(-2, 2),
  logScale = FALSE,
  numericId = TRUE,
  fileName = NULL
)

Arguments

`biasDist`	A bias distribution object generated by the `fitBiasDistribution()` or `sequentialFitBiasDistribution()` function.
`limits`	The lower and upper limits in log-RR to plot.
`logScale`	Whether or not to show bias in log-RR; default FALSE (shown in RR).
`numericId`	(For sequential or group case only) whether or not to treat `Id` as a numeric variable; default: TRUE.
`fileName`	Name of the file where the plot should be saved, for example 'plot.png'. See the function ggplot2::ggsave in the ggplot2 package for supported file formats.

Details

Plot empirical bias distributions learned from analyzing negative controls.

Value

A ggplot object. Use the ggplot2::ggsave function to save to file.

Examples

# Fit a bias distribution for this example:
data("ncLikelihoods")
# NOT RUN
# singleBiasDist = fitBiasDistribution(ncLikelihoods[[5]], seed = 1)

# Plot it
# NOT RUN
# plotBiasDistribution(singleBiasDist)

# Fit a bias distribution for this example:
data("ncLikelihoods")
# NOT RUN
# singleBiasDist = fitBiasDistribution(ncLikelihoods[[5]], seed = 1)

# Plot it
# NOT RUN
# plotBiasDistribution(singleBiasDist)

Plot covariate balances

Description

Plots the covariate balance before and after matching for multiple data sources.

Usage

plotCovariateBalances(
  balances,
  labels,
  threshold = 0,
  beforeLabel = "Before matching",
  afterLabel = "After matching",
  fileName = NULL
)
plotCovariateBalances(
  balances,
  labels,
  threshold = 0,
  beforeLabel = "Before matching",
  afterLabel = "After matching",
  fileName = NULL
)

Arguments

`balances`	A list of covariate balance objects as created using the `computeCovariateBalance()` function in the `CohortMethod` package. Each balance object is expected to be a data frame with at least these two columns: `beforeMatchingStdDiff` and `afterMatchingStdDiff`.
`labels`	A vector containing the labels for the various sources.
`threshold`	Show a threshold value for the standardized difference.
`beforeLabel`	Label for before matching / stratification / trimming.
`afterLabel`	Label for after matching / stratification / trimming.
`fileName`	Name of the file where the plot should be saved, for example 'plot.png'. See the function ggplot2::ggsave for supported file formats.

Details

Creates a plot showing the covariate balance before and after matching. Balance distributions are displayed as box plots combined with scatterplots.

Value

A Ggplot object. Use the ggplot2::ggsave.

Examples

# Some example data:
balance1 <- data.frame(
  beforeMatchingStdDiff = rnorm(1000, 0.1, 0.1),
  afterMatchingStdDiff = rnorm(1000, 0, 0.01)
)
balance2 <- data.frame(
  beforeMatchingStdDiff = rnorm(1000, 0.2, 0.1),
  afterMatchingStdDiff = rnorm(1000, 0, 0.05)
)
balance3 <- data.frame(
  beforeMatchingStdDiff = rnorm(1000, 0, 0.1),
  afterMatchingStdDiff = rnorm(1000, 0, 0.03)
)
plotCovariateBalances(
  balances = list(balance1, balance2, balance3),
  labels = c("Site A", "Site B", "Site C")
)

# Some example data:
balance1 <- data.frame(
  beforeMatchingStdDiff = rnorm(1000, 0.1, 0.1),
  afterMatchingStdDiff = rnorm(1000, 0, 0.01)
)
balance2 <- data.frame(
  beforeMatchingStdDiff = rnorm(1000, 0.2, 0.1),
  afterMatchingStdDiff = rnorm(1000, 0, 0.05)
)
balance3 <- data.frame(
  beforeMatchingStdDiff = rnorm(1000, 0, 0.1),
  afterMatchingStdDiff = rnorm(1000, 0, 0.03)
)
plotCovariateBalances(
  balances = list(balance1, balance2, balance3),
  labels = c("Site A", "Site B", "Site C")
)

Plot empirical null distributions

Description

Plot the empirical null distribution for multiple data sources.

Usage

plotEmpiricalNulls(
  logRr,
  seLogRr,
  labels,
  xLabel = "Relative risk",
  limits = c(0.1, 10),
  showCis = TRUE,
  fileName = NULL
)
plotEmpiricalNulls(
  logRr,
  seLogRr,
  labels,
  xLabel = "Relative risk",
  limits = c(0.1, 10),
  showCis = TRUE,
  fileName = NULL
)

Arguments

`logRr`	A numeric vector of effect estimates for the negative controls on the log scale.
`seLogRr`	The standard error of the log of the effect estimates. Hint: often the standard error = (log(lower bound 95 percent confidence interval) - l og(effect estimate))/qnorm(0.025).
`labels`	A vector containing the labels for the various sources. Should be of equal length as `logRr` and `seLogRr`.
`xLabel`	The label on the x-axis: the name of the effect estimate.
`limits`	The limits of the effect size axis.
`showCis`	Show the 95 percent confidence intervals on the null distribution and distribution parameter estimates?
`fileName`	Name of the file where the plot should be saved, for example 'plot.png'. See the function `ggplot2::ggsave()` for supported file formats.

Details

Creates a plot showing the empirical null distributions. Distributions are shown as mean plus minus one standard deviation, as well as a distribution plot.

Value

A Ggplot object. Use the ggplot2::ggsave() function to save to file.

Examples

# Some example data:
site1 <- EmpiricalCalibration::simulateControls(n = 50, mean = 0, sd = 0.1, trueLogRr = 0)
site1$label <- "Site 1"
site2 <- EmpiricalCalibration::simulateControls(n = 50, mean = 0.1, sd = 0.2, trueLogRr = 0)
site2$label <- "Site 2"
site3 <- EmpiricalCalibration::simulateControls(n = 50, mean = 0.15, sd = 0.25, trueLogRr = 0)
site3$label <- "Site 3"
sites <- rbind(site1, site2, site3)

plotEmpiricalNulls(logRr = sites$logRr, seLogRr = sites$seLogRr, labels = sites$label)

# Some example data:
site1 <- EmpiricalCalibration::simulateControls(n = 50, mean = 0, sd = 0.1, trueLogRr = 0)
site1$label <- "Site 1"
site2 <- EmpiricalCalibration::simulateControls(n = 50, mean = 0.1, sd = 0.2, trueLogRr = 0)
site2$label <- "Site 2"
site3 <- EmpiricalCalibration::simulateControls(n = 50, mean = 0.15, sd = 0.25, trueLogRr = 0)
site3$label <- "Site 3"
sites <- rbind(site1, site2, site3)

plotEmpiricalNulls(logRr = sites$logRr, seLogRr = sites$seLogRr, labels = sites$label)

Plot the likelihood approximation

Description

Plot the likelihood approximation

Usage

plotLikelihoodFit(
  approximation,
  cyclopsFit,
  parameter = "x",
  logScale = TRUE,
  xLabel = "Hazard Ratio",
  limits = c(0.1, 10),
  fileName = NULL
)
plotLikelihoodFit(
  approximation,
  cyclopsFit,
  parameter = "x",
  logScale = TRUE,
  xLabel = "Hazard Ratio",
  limits = c(0.1, 10),
  fileName = NULL
)

Arguments

`approximation`	An approximation of the likelihood function as fitted using the `approximateLikelihood()` function.
`cyclopsFit`	A model fitted using the `Cyclops::fitCyclopsModel()` function.
`parameter`	The parameter in the `cyclopsFit` object to profile.
`logScale`	Show the y-axis on the log scale?
`xLabel`	The title of the x-axis.
`limits`	The limits on the x-axis.
`fileName`	Name of the file where the plot should be saved, for example 'plot.png'. See the function ggplot2::ggsave in the ggplot2 package for supported file formats.

Details

Plots the (log) likelihood and the approximation of the likelihood. Allows for reviewing the approximation.

Value

A Ggplot object. Use the ggplot2::ggsave function to save to file.

Examples

# Simulate a single database population:
population <- simulatePopulations(createSimulationSettings(nSites = 1))[[1]]

# Approximate the likelihood:
cyclopsData <- Cyclops::createCyclopsData(Surv(time, y) ~ x + strata(stratumId),
  data = population,
  modelType = "cox"
)
cyclopsFit <- Cyclops::fitCyclopsModel(cyclopsData)
approximation <- approximateLikelihood(cyclopsFit, parameter = "x", approximation = "custom")

plotLikelihoodFit(approximation, cyclopsFit, parameter = "x")

# Simulate a single database population:
population <- simulatePopulations(createSimulationSettings(nSites = 1))[[1]]

# Approximate the likelihood:
cyclopsData <- Cyclops::createCyclopsData(Surv(time, y) ~ x + strata(stratumId),
  data = population,
  modelType = "cox"
)
cyclopsFit <- Cyclops::fitCyclopsModel(cyclopsData)
approximation <- approximateLikelihood(cyclopsFit, parameter = "x", approximation = "custom")

plotLikelihoodFit(approximation, cyclopsFit, parameter = "x")

Plot MCMC trace

Description

Plot MCMC trace

Usage

plotMcmcTrace(
  estimate,
  showEstimate = TRUE,
  dataCutoff = 0.01,
  fileName = NULL
)
plotMcmcTrace(
  estimate,
  showEstimate = TRUE,
  dataCutoff = 0.01,
  fileName = NULL
)

Arguments

`estimate`	An object as generated using the `computeBayesianMetaAnalysis()` function.
`showEstimate`	Show the parameter estimates (mode) and 95 percent confidence intervals?
`dataCutoff`	This fraction of the data at both tails will be removed.
`fileName`	Name of the file where the plot should be saved, for example 'plot.png'. See the function ggplot2::ggsave in the ggplot2 package for supported file formats.

Details

Plot the samples of the posterior distribution of the mu and tau parameters. Samples are taken using Markov-chain Monte Carlo (MCMC).

Value

A Ggplot object. Use the ggplot2::ggsave function to save to file.

Examples

# Simulate some data for this example:
populations <- simulatePopulations()

# Fit a Cox regression at each data site, and approximate likelihood function:
fitModelInDatabase <- function(population) {
  cyclopsData <- Cyclops::createCyclopsData(Surv(time, y) ~ x + strata(stratumId),
    data = population,
    modelType = "cox"
  )
  cyclopsFit <- Cyclops::fitCyclopsModel(cyclopsData)
  approximation <- approximateLikelihood(cyclopsFit, parameter = "x", approximation = "custom")
  return(approximation)
}
approximations <- lapply(populations, fitModelInDatabase)
approximations <- do.call("rbind", approximations)

# At study coordinating center, perform meta-analysis using per-site approximations:
estimate <- computeBayesianMetaAnalysis(approximations)
plotMcmcTrace(estimate)

# Simulate some data for this example:
populations <- simulatePopulations()

# Fit a Cox regression at each data site, and approximate likelihood function:
fitModelInDatabase <- function(population) {
  cyclopsData <- Cyclops::createCyclopsData(Surv(time, y) ~ x + strata(stratumId),
    data = population,
    modelType = "cox"
  )
  cyclopsFit <- Cyclops::fitCyclopsModel(cyclopsData)
  approximation <- approximateLikelihood(cyclopsFit, parameter = "x", approximation = "custom")
  return(approximation)
}
approximations <- lapply(populations, fitModelInDatabase)
approximations <- do.call("rbind", approximations)

# At study coordinating center, perform meta-analysis using per-site approximations:
estimate <- computeBayesianMetaAnalysis(approximations)
plotMcmcTrace(estimate)

Create a forest plot

Description

Creates a forest plot of effect size estimates, including the summary estimate.

Usage

plotMetaAnalysisForest(
  data,
  labels,
  estimate,
  xLabel = "Relative risk",
  summaryLabel = "Summary",
  limits = c(0.1, 10),
  alpha = 0.05,
  showLikelihood = TRUE,
  fileName = NULL
)
plotMetaAnalysisForest(
  data,
  labels,
  estimate,
  xLabel = "Relative risk",
  summaryLabel = "Summary",
  limits = c(0.1, 10),
  alpha = 0.05,
  showLikelihood = TRUE,
  fileName = NULL
)

Arguments

`data`	A data frame containing either normal, skew-normal, custom parametric, or grid likelihood data. One row per database.
`labels`	A vector of labels for the data sources.
`estimate`	The meta-analytic estimate as created using either ['computeFixedEffectMetaAnalysis()`⁠] or [⁠`computeBayesianMetaAnalysis()'] function.
`xLabel`	The label on the x-axis: the name of the effect estimate.
`summaryLabel`	The label for the meta-analytic estimate.
`limits`	The limits of the effect size axis.
`alpha`	The alpha (expected type I error).
`showLikelihood`	Show the likelihood curve for each estimate?
`fileName`	Name of the file where the plot should be saved, for example 'plot.png'. See the function ggplot2::ggsave ifor supported file formats.

Details

Creates a forest plot of effect size estimates, including a meta-analysis estimate.

Value

A Ggplot object. Use the ggplot2::ggsave function to save to file.

Examples

# Simulate some data for this example:
populations <- simulatePopulations()
labels <- paste("Data site", LETTERS[1:length(populations)])

# Fit a Cox regression at each data site, and approximate likelihood function:
fitModelInDatabase <- function(population) {
  cyclopsData <- Cyclops::createCyclopsData(Surv(time, y) ~ x + strata(stratumId),
    data = population,
    modelType = "cox"
  )
  cyclopsFit <- Cyclops::fitCyclopsModel(cyclopsData)
  approximation <- approximateLikelihood(cyclopsFit, parameter = "x", approximation = "custom")
  return(approximation)
}
approximations <- lapply(populations, fitModelInDatabase)
approximations <- do.call("rbind", approximations)

# At study coordinating center, perform meta-analysis using per-site approximations:
estimate <- computeBayesianMetaAnalysis(approximations)
plotMetaAnalysisForest(approximations, labels, estimate)

# (Estimates in this example will vary due to the random simulation)

# Simulate some data for this example:
populations <- simulatePopulations()
labels <- paste("Data site", LETTERS[1:length(populations)])

# Fit a Cox regression at each data site, and approximate likelihood function:
fitModelInDatabase <- function(population) {
  cyclopsData <- Cyclops::createCyclopsData(Surv(time, y) ~ x + strata(stratumId),
    data = population,
    modelType = "cox"
  )
  cyclopsFit <- Cyclops::fitCyclopsModel(cyclopsData)
  approximation <- approximateLikelihood(cyclopsFit, parameter = "x", approximation = "custom")
  return(approximation)
}
approximations <- lapply(populations, fitModelInDatabase)
approximations <- do.call("rbind", approximations)

# At study coordinating center, perform meta-analysis using per-site approximations:
estimate <- computeBayesianMetaAnalysis(approximations)
plotMetaAnalysisForest(approximations, labels, estimate)

# (Estimates in this example will vary due to the random simulation)

Plot MCMC trace for individual databases

Description

Plot MCMC trace for individual databases

Usage

plotPerDbMcmcTrace(
  estimate,
  showEstimate = TRUE,
  dataCutoff = 0.01,
  fileName = NULL
)
plotPerDbMcmcTrace(
  estimate,
  showEstimate = TRUE,
  dataCutoff = 0.01,
  fileName = NULL
)

Arguments

`estimate`	An object as generated using the `computeBayesianMetaAnalysis()` function.
`showEstimate`	Show the parameter estimates (mode) and 95 percent confidence intervals?
`dataCutoff`	This fraction of the data at both tails will be removed.
`fileName`	Name of the file where the plot should be saved, for example 'plot.png'. See the function ggplot2::ggsave in the ggplot2 package for supported file formats.

Details

Plot the samples of the posterior distribution of the theta parameter (the estimated log hazard ratio) at each site. Samples are taken using Markov-chain Monte Carlo (MCMC).

Value

A Ggplot object. Use the ggplot2::ggsave function to save to file.

Examples

# Simulate some data for this example:
populations <- simulatePopulations()

# Fit a Cox regression at each data site, and approximate likelihood function:
fitModelInDatabase <- function(population) {
  cyclopsData <- Cyclops::createCyclopsData(Surv(time, y) ~ x + strata(stratumId),
    data = population,
    modelType = "cox"
  )
  cyclopsFit <- Cyclops::fitCyclopsModel(cyclopsData)
  approximation <- approximateLikelihood(cyclopsFit, parameter = "x", approximation = "custom")
  return(approximation)
}
approximations <- lapply(populations, fitModelInDatabase)
approximations <- do.call("rbind", approximations)

# At study coordinating center, perform meta-analysis using per-site approximations:
estimate <- computeBayesianMetaAnalysis(approximations)
plotPerDbMcmcTrace(estimate)

# Simulate some data for this example:
populations <- simulatePopulations()

# Fit a Cox regression at each data site, and approximate likelihood function:
fitModelInDatabase <- function(population) {
  cyclopsData <- Cyclops::createCyclopsData(Surv(time, y) ~ x + strata(stratumId),
    data = population,
    modelType = "cox"
  )
  cyclopsFit <- Cyclops::fitCyclopsModel(cyclopsData)
  approximation <- approximateLikelihood(cyclopsFit, parameter = "x", approximation = "custom")
  return(approximation)
}
approximations <- lapply(populations, fitModelInDatabase)
approximations <- do.call("rbind", approximations)

# At study coordinating center, perform meta-analysis using per-site approximations:
estimate <- computeBayesianMetaAnalysis(approximations)
plotPerDbMcmcTrace(estimate)

Plot posterior density per database

Description

Plot posterior density per database

Usage

plotPerDbPosterior(
  estimate,
  showEstimate = TRUE,
  dataCutoff = 0.01,
  fileName = NULL
)
plotPerDbPosterior(
  estimate,
  showEstimate = TRUE,
  dataCutoff = 0.01,
  fileName = NULL
)

Arguments

`estimate`	An object as generated using the `computeBayesianMetaAnalysis()` function.
`showEstimate`	Show the parameter estimates (mode) and 95 percent confidence intervals?
`dataCutoff`	This fraction of the data at both tails will be removed.
`fileName`	Name of the file where the plot should be saved, for example 'plot.png'. See the function ggplot2::ggsave in the ggplot2 package for supported file formats.

Details

Plot the density of the posterior distribution of the theta parameter (the estimated log hazard ratio) at each site.

Value

A Ggplot object. Use the ggplot2::ggsave function to save to file.

Examples

# Simulate some data for this example:
populations <- simulatePopulations()

# Fit a Cox regression at each data site, and approximate likelihood function:
fitModelInDatabase <- function(population) {
  cyclopsData <- Cyclops::createCyclopsData(Surv(time, y) ~ x + strata(stratumId),
    data = population,
    modelType = "cox"
  )
  cyclopsFit <- Cyclops::fitCyclopsModel(cyclopsData)
  approximation <- approximateLikelihood(cyclopsFit, parameter = "x", approximation = "custom")
  return(approximation)
}
approximations <- lapply(populations, fitModelInDatabase)
approximations <- do.call("rbind", approximations)

# At study coordinating center, perform meta-analysis using per-site approximations:
estimate <- computeBayesianMetaAnalysis(approximations)
plotPerDbPosterior(estimate)

# Simulate some data for this example:
populations <- simulatePopulations()

# Fit a Cox regression at each data site, and approximate likelihood function:
fitModelInDatabase <- function(population) {
  cyclopsData <- Cyclops::createCyclopsData(Surv(time, y) ~ x + strata(stratumId),
    data = population,
    modelType = "cox"
  )
  cyclopsFit <- Cyclops::fitCyclopsModel(cyclopsData)
  approximation <- approximateLikelihood(cyclopsFit, parameter = "x", approximation = "custom")
  return(approximation)
}
approximations <- lapply(populations, fitModelInDatabase)
approximations <- do.call("rbind", approximations)

# At study coordinating center, perform meta-analysis using per-site approximations:
estimate <- computeBayesianMetaAnalysis(approximations)
plotPerDbPosterior(estimate)

Plot posterior density

Description

Plot posterior density

Usage

plotPosterior(
  estimate,
  showEstimate = TRUE,
  dataCutoff = 0.01,
  fileName = NULL
)
plotPosterior(
  estimate,
  showEstimate = TRUE,
  dataCutoff = 0.01,
  fileName = NULL
)

Arguments

`estimate`	An object as generated using the `computeBayesianMetaAnalysis()` function.
`showEstimate`	Show the parameter estimates (mode) and 95 percent confidence intervals?
`dataCutoff`	This fraction of the data at both tails will be removed.
`fileName`	Name of the file where the plot should be saved, for example 'plot.png'. See the function ggplot2::ggsave in the ggplot2 package for supported file formats.

Details

Plot the density of the posterior distribution of the mu and tau parameters.

Value

A Ggplot object. Use the ggplot2::ggsave function to save to file.

Examples

# Simulate some data for this example:
populations <- simulatePopulations()

# Fit a Cox regression at each data site, and approximate likelihood function:
fitModelInDatabase <- function(population) {
  cyclopsData <- Cyclops::createCyclopsData(Surv(time, y) ~ x + strata(stratumId),
    data = population,
    modelType = "cox"
  )
  cyclopsFit <- Cyclops::fitCyclopsModel(cyclopsData)
  approximation <- approximateLikelihood(cyclopsFit, parameter = "x", approximation = "custom")
  return(approximation)
}
approximations <- lapply(populations, fitModelInDatabase)
approximations <- do.call("rbind", approximations)

# At study coordinating center, perform meta-analysis using per-site approximations:
estimate <- computeBayesianMetaAnalysis(approximations)
plotPosterior(estimate)

# Simulate some data for this example:
populations <- simulatePopulations()

# Fit a Cox regression at each data site, and approximate likelihood function:
fitModelInDatabase <- function(population) {
  cyclopsData <- Cyclops::createCyclopsData(Surv(time, y) ~ x + strata(stratumId),
    data = population,
    modelType = "cox"
  )
  cyclopsFit <- Cyclops::fitCyclopsModel(cyclopsData)
  approximation <- approximateLikelihood(cyclopsFit, parameter = "x", approximation = "custom")
  return(approximation)
}
approximations <- lapply(populations, fitModelInDatabase)
approximations <- do.call("rbind", approximations)

# At study coordinating center, perform meta-analysis using per-site approximations:
estimate <- computeBayesianMetaAnalysis(approximations)
plotPosterior(estimate)

Plot the propensity score distribution

Description

Plot the propensity score distribution

Usage

plotPreparedPs(
  preparedPsPlots,
  labels,
  treatmentLabel = "Target",
  comparatorLabel = "Comparator",
  fileName = NULL
)
plotPreparedPs(
  preparedPsPlots,
  labels,
  treatmentLabel = "Target",
  comparatorLabel = "Comparator",
  fileName = NULL
)

Arguments

`preparedPsPlots`	list of prepared propensity score data as created by the `preparePsPlot()` function.
`labels`	A vector containing the labels for the various sources.
`treatmentLabel`	A label to us for the treated cohort.
`comparatorLabel`	A label to us for the comparator cohort.
`fileName`	Name of the file where the plot should be saved, for example 'plot.png'. See the function ggplot2::ggsave for supported file formats.

Value

A ggplot object. Use the ggplot2::ggsave function to save to file in a different format.

Examples

# Simulate some data for this example:
treatment <- rep(0:1, each = 100)
propensityScore <- c(rnorm(100, mean = 0.4, sd = 0.25), rnorm(100, mean = 0.6, sd = 0.25))
data <- data.frame(treatment = treatment, propensityScore = propensityScore)
data <- data[data$propensityScore > 0 & data$propensityScore < 1, ]
preparedPlot <- preparePsPlot(data)

# Just reusing the same data three times for demonstration purposes:
preparedPsPlots <- list(preparedPlot, preparedPlot, preparedPlot)
labels <- c("Data site A", "Data site B", "Data site C")

plotPreparedPs(preparedPsPlots, labels)

# Simulate some data for this example:
treatment <- rep(0:1, each = 100)
propensityScore <- c(rnorm(100, mean = 0.4, sd = 0.25), rnorm(100, mean = 0.6, sd = 0.25))
data <- data.frame(treatment = treatment, propensityScore = propensityScore)
data <- data[data$propensityScore > 0 & data$propensityScore < 1, ]
preparedPlot <- preparePsPlot(data)

# Just reusing the same data three times for demonstration purposes:
preparedPsPlots <- list(preparedPlot, preparedPlot, preparedPlot)
labels <- c("Data site A", "Data site B", "Data site C")

plotPreparedPs(preparedPsPlots, labels)

Prepare to plot the propensity score distribution

Description

Prepare to plot the propensity (or preference) score distribution. It computes the distribution, so the output does not contain person-level data.

Usage

preparePsPlot(data, unfilteredData = NULL, scale = "preference")
preparePsPlot(data, unfilteredData = NULL, scale = "preference")

Arguments

`data`	A data frame with at least the two columns described below
`unfilteredData`	To be used when computing preference scores on data from which subjects have already been removed, e.g. through trimming and/or matching. This data frame should have the same structure as `data`.
`scale`	The scale of the graph. Two scales are supported: `scale = 'propensity'` or `scale = 'preference'`. The preference score scale is defined by Walker et al. (2013).

Details

The data frame should have a least the following two columns:

treatment (integer): Column indicating whether the person is in the treated (1) or comparator (0) group. - propensityScore (numeric): Propensity score.

Value

A data frame describing the propensity score (or preference score) distribution at 100 equally-spaced points.

References

Walker AM, Patrick AR, Lauer MS, Hornbrook MC, Marin MG, Platt R, Roger VL, Stang P, and Schneeweiss S. (2013) A tool for assessing the feasibility of comparative effectiveness research, Comparative Effective Research, 3, 11-20

Examples

# Simulate some data for this example:
treatment <- rep(0:1, each = 100)
propensityScore <- c(rnorm(100, mean = 0.4, sd = 0.25), rnorm(100, mean = 0.6, sd = 0.25))
data <- data.frame(treatment = treatment, propensityScore = propensityScore)
data <- data[data$propensityScore > 0 & data$propensityScore < 1, ]

preparedPlot <- preparePsPlot(data)

# Simulate some data for this example:
treatment <- rep(0:1, each = 100)
propensityScore <- c(rnorm(100, mean = 0.4, sd = 0.25), rnorm(100, mean = 0.6, sd = 0.25))
data <- data.frame(treatment = treatment, propensityScore = propensityScore)
data <- data[data$propensityScore > 0 & data$propensityScore < 1, ]

preparedPlot <- preparePsPlot(data)

Fit Bias Distribution Sequentially or in Groups

Description

Learn empirical bias distributions sequentially or in groups; for each sequential step or analysis group, bias distributions is learned by by simultaneously analyzing a large set of negative control outcomes by a Bayesian hierarchical model through MCMC.

Usage

sequentialFitBiasDistribution(LikelihoodProfileList, ...)
sequentialFitBiasDistribution(LikelihoodProfileList, ...)

Arguments

`LikelihoodProfileList`	A list of lists, each of which is a set of grid profile likelihoods regarding negative controls, indexed by analysis period ID for sequential analyses or group ID for group analyses.
`...`	Arguments passed to the `fitBiasDistribution()` function.

Value

A (long) dataframe with four columns. Column mean includes MCMC samples for the average bias, scale for the sd/scale parameter, bias for predictive samples of the bias, and Id for the period ID or group ID.

Examples

# load example data
data("ncLikelihoods")

# fit bias distributions over analysis periods
# NOT RUN
# biasDistributions = sequentialFitBiasDistribution(ncLikelihoods, seed = 42)

# load example data
data("ncLikelihoods")

# fit bias distributions over analysis periods
# NOT RUN
# biasDistributions = sequentialFitBiasDistribution(ncLikelihoods, seed = 42)

Simulate survival data for multiple databases

Description

Simulate survival data for multiple databases

Usage

simulatePopulations(settings = createSimulationSettings())
simulatePopulations(settings = createSimulationSettings())

Arguments

settings

An object of type simulationSettings, created by the createSimulationSettings() function.

Value

A object of class simulation, which is a list of populations, each a data frame with columns rowId, stratumId, x, time, and y.

Examples

settings <- createSimulationSettings(nSites = 1, hazardRatio = 2)
populations <- simulatePopulations(settings)

# Fit a Cox regression for the simulated data site:
cyclopsData <- Cyclops::createCyclopsData(Surv(time, y) ~ x + strata(stratumId),
  data = populations[[1]],
  modelType = "cox"
)
cyclopsFit <- Cyclops::fitCyclopsModel(cyclopsData)
coef(cyclopsFit)

# (Estimates in this example will vary due to the random simulation)

settings <- createSimulationSettings(nSites = 1, hazardRatio = 2)
populations <- simulatePopulations(settings)

# Fit a Cox regression for the simulated data site:
cyclopsData <- Cyclops::createCyclopsData(Surv(time, y) ~ x + strata(stratumId),
  data = populations[[1]],
  modelType = "cox"
)
cyclopsFit <- Cyclops::fitCyclopsModel(cyclopsData)
coef(cyclopsFit)

# (Estimates in this example will vary due to the random simulation)

The skew normal function to approximate a log likelihood function

Description

The skew normal function to approximate a log likelihood function

Usage

skewNormal(x, mu, sigma, alpha)
skewNormal(x, mu, sigma, alpha)

Arguments

`x`	The log(hazard ratio) for which to approximate the log likelihood.
`mu`	The position parameter.
`sigma`	The scale parameter.
`alpha`	The skew parameter.

Details

The skew normal function. When alpha = 0 this function is the normal distribution.

Value

The approximate log likelihood for the given x.

References

Azzalini, A. (2013). The Skew-Normal and Related Families. Institute of Mathematical Statistics Monographs. Cambridge University Press.

Examples

skewNormal(x = 0:3, mu = 0, sigma = 1, alpha = 0)

skewNormal(x = 0:3, mu = 0, sigma = 1, alpha = 0)

Determine if Java virtual machine supports Java

Description

Tests Java virtual machine (JVM) java.version system property to check if version >= 8.

Usage

supportsJava8()
supportsJava8()

Value

Returns TRUE if JVM supports Java >= 8.

Examples

supportsJava8()

supportsJava8()

Package 'EvidenceSynthesis'

Help Index

Approximate Bayesian posterior for hierarchical Normal model

Description

Usage

Arguments

Value

Examples

Approximate a likelihood function

Description

Usage

Arguments

Value

See Also

Examples

Approximate simple Bayesian posterior

Description

Usage

Arguments

Value

Examples

Bias Correction with Inference

Description

Usage

Arguments

Value

See Also

Examples

Compute a Bayesian random-effects meta-analysis

Description

Usage

Arguments

Value

See Also

Examples

Compute the point estimate and confidence interval given a likelihood function approximation

Description

Usage

Arguments

Details

Value

Examples

Compute a fixed-effect meta-analysis

Description

Usage

Arguments

Value

See Also

Examples

Create simulation settings

Description

Usage

Arguments

Value

See Also

Examples

A custom function to approximate a log likelihood function

Description

Usage

Arguments

Details

Value

Examples

Detect the type of likelihood approximation based on the data format

Description

Usage

Arguments

Value

Examples

Fit Bias Distribution

Description

Usage

Arguments

Value

See Also

Examples

Example profile likelihoods for negative control outcomes

Description

Usage

Format