computeCovariateBalance: Compute covariate balance before and after matching and...

In escott12/CohortMethod: New-User Cohort Method with Large Scale Propensity and Outcome Models

Description

For every covariate, prevalence in treatment and comparator groups before and after matching/trimming are computed. When variable ratio matching was used the balance score will be corrected according the method described in Austin et al (2008).

Usage

computeCovariateBalance(
  population,
  cohortMethodData,
  subgroupCovariateId = NULL
)

Arguments

population	A data frame containing the people that are remaining after matching and/or trimming.
cohortMethodData	An object of type CohortMethodData as generated using getDbCohortMethodData().
subgroupCovariateId	Optional: a covariate ID of a binary covariate that indicates a subgroup of interest. Both the before and after populations will be restricted to this subgroup before computing covariate balance.

Details

The population data frame should have the following three columns:

• rowId (numeric): A unique identifier for each row (e.g. the person ID).

• treatment (integer): Column indicating whether the person is in the target (1) or comparator (0) group.

• propensityScore (numeric): Propensity score.

Value

Returns a tibble describing the covariate balance before and after matching/trimming.

References

Austin, P.C. (2008) Assessing balance in measured baseline covariates when using many-to-one matching on the propensity-score. Pharmacoepidemiology and Drug Safety, 17: 1218-1225.

escott12/CohortMethod documentation built on Dec. 20, 2021, 6:37 a.m.