exmLP.2ipd function

Checks whether two IPD datasets can be matched with lpSolve::lp

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

Arguments

• ipd1: a dataframe with n1 row and p column, where n1 is number of subjects of the first IPD, and p is the number of variables used in standardization.
• ipd2: a dataframe with n2 row and p column, where n2 is number of subjects of the second IPD, and p is the number of variables used in standardization.
• vars_to_match: variables used for matching. if NULL, use all variables.
• cat_vars_to_01: 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

• lp.check: 0 = OS can be conducted; 2 = OS cannot be conducted

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 <- exmLP.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