to assess generalizability we compare the distribution of covariates before and after any (propensity score) adjustments. We compute the standardized difference of mean as our metric of generalizability. (Lipton et al., 2017)
Depending on our target estimand, we need to consider a different base population for generalizability. For example, if we aim to estimate the average treatment effect in thetreated (ATT), our base population should be the target population, meaning we should consider the covariate distribution before and after PS adjustment in the target population only. By default this function will attempt to select the right base population based on what operations have been performed on the population. For example, if PS matching has been performed we assume the target estimand is the ATT, and the target population is selected as base.
Requires running computeCovariateBalance()` first.
getGeneralizabilityTable(balance, baseSelection = "auto")Arguments
- balance
A data frame created by the
computeCovariateBalancefunction.- baseSelection
The selection of the population to consider for generalizability. Options are "auto", "target", "comparator", and "both". The "auto" option will attempt to use the balance meta-data to pick the most appropriate population based on the target estimator.
Value
A tibble with the following columns:
covariateId: The ID of the covariate. Can be linked to the
covariatesandcovariateReftables in theCohortMethodDataobject.covariateName: The name of the covariate.
beforeMatchingMean: The mean covariate value before any (propensity score) adjustment.
afterMatchingMean: The mean covariate value after any (propensity score) adjustment.
stdDiff: The standardized difference of means between before and after adjustment.
The tibble also has a 'baseSelection' attribute, documenting the base population used to assess generalizability.
References
Tipton E, Hallberg K, Hedges LV, Chan W (2017) Implications of Small Samples for Generalization: Adjustments and Rules of Thumb, Eval Rev. Oct;41(5):472-505.