Create a parameter object for the function fitOutcomeModel

createFitOutcomeModelArgs(
  modelType = "logistic",
  stratified = FALSE,
  useCovariates = FALSE,
  inversePtWeighting = FALSE,
  interactionCovariateIds = c(),
  excludeCovariateIds = c(),
  includeCovariateIds = c(),
  profileGrid = NULL,
  profileBounds = c(log(0.1), log(10)),
  prior = createPrior("laplace", useCrossValidation = TRUE),
  control = createControl(cvType = "auto", seed = 1, resetCoefficients = TRUE,
    startingVariance = 0.01, tolerance = 2e-07, cvRepetitions = 10, noiseLevel = "quiet")
)

Arguments

modelType: The type of outcome model that will be used. Possible values are "logistic", "poisson", or "cox".
stratified: Should the regression be conditioned on the strata defined in the population object (e.g. by matching or stratifying on propensity scores)?
useCovariates: Whether to use the covariates in the cohortMethodData object in the outcome model.
inversePtWeighting: Use inverse probability of treatment weighting (IPTW)
interactionCovariateIds: An optional vector of covariate IDs to use to estimate interactions with the main treatment effect.
excludeCovariateIds: Exclude these covariates from the outcome model.
includeCovariateIds: Include only these covariates in the outcome model.
profileGrid: A one-dimensional grid of points on the log(relative risk) scale where the likelihood for coefficient of variables is sampled. See details.
profileBounds: The bounds (on the log relative risk scale) for the adaptive sampling of the likelihood function. See details.
prior: The prior used to fit the model. See Cyclops::createPrior() for details.
control: The control object used to control the cross-validation used to determine the hyperparameters of the prior (if applicable). See Cyclops::createControl() for details.

Details

Create an object defining the parameter values.