approximateLikelihood: Approximate a likelihood function

In OHDSI/EvidenceSynthesis: Synthesizing Causal Evidence in a Distributed Research Network

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))
)

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.

See Also

computeConfidenceInterval, computeFixedEffectMetaAnalysis, computeBayesianMetaAnalysis

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)

OHDSI/EvidenceSynthesis documentation built on May 15, 2023, 11:38 a.m.