`R/Balance.R`

`computeCovariateBalance.Rd`

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).

```
computeCovariateBalance(
population,
cohortMethodData,
subgroupCovariateId = NULL,
maxCohortSize = 250000,
covariateFilter = NULL
)
```

- 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.

- maxCohortSize
If the target or comparator cohort are larger than this number, they will be downsampled before computing covariate balance to save time. Setting this number to 0 means no downsampling will be applied.

- covariateFilter
Determines the covariates for which to compute covariate balance. Either a vector of covariate IDs, or a table 1 specifications object as generated for example using

`FeatureExtraction::getDefaultTable1Specifications()`

. If`covariateFilter = NULL`

, balance will be computed for all variables found in the data.

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

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.

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.