Create an outcome model, and computes the relative risk

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

Arguments

population

A population object generated by createStudyPopulation(), potentially filtered by other functions.

cohortMethodData

An object of type CohortMethodData as generated using getDbCohortMethodData(). Can be omitted if not using covariates and not using interaction terms.

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)? See details.

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.

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.

Value

An object of class OutcomeModel. Generic function print, coef, and confint are available.

Details

IPTW estimates the average treatment effect using stabilized inverse propensity scores (Xu et al. 2010).

References

Xu S, Ross C, Raebel MA, Shetterly S, Blanchette C, Smith D. Use of stabilized inverse propensity scores as weights to directly estimate relative risk and its confidence intervals. Value Health. 2010;13(2):273-277. doi:10.1111/j.1524-4733.2009.00671.x