exmWt.2ipd function

Exact matching for two IPD's

exmWt.2ipd(
  ipd1,
  ipd2,
  vars_to_match = NULL,
  cat_vars_to_01 = NULL,
  mean.constrained = FALSE
)

Arguments

• ipd1: a dataframe with n row and p column, where n is number of subjects and p is the number of variables used in matching.
• ipd2: the other IPD with the same number of columns
• vars_to_match: variables used for matching. if NULL, use all variables.
• cat_vars_to_01: a list of variable names for the categorical variables that need to be converted to indicator variables.
• mean.constrained: whether to restrict the weighted means to be within the ranges of observed means. Default is FALSE. When it is TRUE, there is a higher chance of not having a solution.

Returns

• ipd1: re-scaled weights of the exact matching by maximizing ESS for IPD 1, and the input IPD 1 data with categorical variables converted to 0-1 indicators

• ipd2: re-scaled weights of the exact matching by maximizing ESS for IPD 2, and the input IPD 2 data with categorical variables converted to 0-1 indicators

• wtd.summ: ESS for IPD 1, ESS for IPD 2, and weighted means of the matching variables

Details

If dummy variables are already created for the categorical variables in the data set, and are present in ipd1 and ipd2, then cat_vars_to_01 should be left as NULL.

Examples

## Not run:

ipd1 <- sim110[sim110$study == 'IPD A',]
ipd2 <- sim110[sim110$study == 'IPD B',]
x <- exmWt.2ipd(ipd1, ipd2, vars_to_match = paste0('X', 1:5), 
cat_vars_to_01 = paste0('X', 1:3), mean.constrained = FALSE) 
## End(Not run)

Author(s)

Lillian Yau