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") )
A population object generated by
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
Use inverse probability of treatment weighting (IPTW)? See details.
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.
The prior used to fit the model. See
The control object used to control the cross-validation used to
determine the hyperparameters of the prior (if applicable). See
An object of class
OutcomeModel. Generic function
confint are available.
IPTW estimates the average treatment effect using stabilized inverse propensity scores (Xu et al. 2010).
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