opmatch 1.0.1

Description:

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