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