opmatch 1.0.1

The package provides the following matching methods:

• optimal match
  • one-to-one match
  • one-to-many match
  • variable ratio match
  • full match (not implemented yet)
• greedy match
  • not implemented yet

All matching methods support covariate and propensity score matching.

To obtain matches you can simply run:

from opmatch.matcher import Matcher
case_control_dict = Matcher(
        df, matching_ratio, 
        min_mr, max_mr, n_controls, 
        metric, matching_type,
        case_col, var_cols,  ps_col,
        ).match()

Parameters

• matching_ratio number of controls per case if matching ratio is constant

• min_mr: minimum number of controls per case

• max_mr: maximum number of controls per case

• n_controls: number of controls to match

• metric: PS or one of the metrics accepted by scipy.spatial.distances.cdist

• matching_type: constant or variable matching ratio

• case_col: boolean column where cases are 1s and controls 0s

• var_cols: columns containing relevatn patient variables if metric!=PS: var_cols used for matching if metric==PS but ps_col is not specified: var_cols used to compute PS using logistic regression

• ps_col: column containing the propensity score

• case_col: column name of case column, should contain 1s and 0s