`R/CreateArgFunctions.R`

`createFitOutcomeModelArgs.Rd`

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")
)
```

- 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.

Create an object defining the parameter values.