Match persons by propensity score — matchOnPs • CohortMethod

Use the provided propensity scores to match target to comparator persons.

Usage

matchOnPs(
  population,
  matchOnPsArgs = createMatchOnPsArgs(),
  cohortMethodData = NULL
)

Arguments

population: A data frame with the three columns described below.
matchOnPsArgs: An object of type MatchOnPsArgs as created by the createMatchOnPsArgs() function.
cohortMethodData: An object of type CohortMethodData as generated using getDbCohortMethodData(). Needed when additionally matching on covariate IDs.

Value

Returns a date frame with the same columns as the input data plus one extra column: stratumId. Any rows that could not be matched are removed

Details

The 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.

The default caliper (0.2 on the standardized logit scale) is the one recommended by Austin (2011).

References

Rassen JA, Shelat AA, Myers J, Glynn RJ, Rothman KJ, Schneeweiss S. (2012) One-to-many propensity score matching in cohort studies, Pharmacoepidemiology and Drug Safety, May, 21 Suppl 2:69-80.

Austin, PC. (2011) Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies, Pharmaceutical statistics, March, 10(2):150-161.

Examples

rowId <- 1:5
treatment <- c(1, 0, 1, 0, 1)
propensityScore <- c(0, 0.1, 0.3, 0.4, 1)
age_group <- c(1, 1, 1, 1, 1)
data <- data.frame(
  rowId = rowId,
  treatment = treatment,
  propensityScore = propensityScore,
  age_group = age_group
)
result <- matchOnPs(data, createMatchOnPsArgs(
  caliper = 0,
  maxRatio = 1,
  matchColumns = "age_group")
)
#> Population size after matching is 4