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:
```python
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
Raw data
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