psmpy 0.2.9

Propensity score matching for python and graphical plots

PsmPy

Matching techniques for epidemiological observational studies as carried out in Python. Propensity score matching is a statistical matching technique used with observational data that attempts to ascertain the validity of concluding there is a potential causal link between a treatment or intervention and an outcome(s) of interest. It does so by accounting for a set of covariates between a binary treatment state (as would occur in a randomized control trial, either received the intervention or not), and control for potential confounding (covariates) in outcome measures between the treatment and control groups such as death, or length of stay etc. It is using this technique on observational data that we gain an insight into the effects or lack thereof of an interventional state.

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Citing this work:

A. Kline, Y. Luo, PsmPy: A Package for Retrospective Cohort Matching in Python, (under review at EMBC 2022)

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• Integration with Jupyter Notebooks
• Additional plotting functionality to assess balance before and after
• A more modular, user-specified matching process
• Ability to define 1:1 or 1:many matching

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Installation

Install the package through pip:

$ pip install psmpy

• Installation
• Data Preparation
• Predict Scores
• Matching algorithm
• Graphical Outputs
• Extra Attributes
• Conclusion

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Data Prep

# import relevant libraries
sns.set(rc={'figure.figsize':(10,8)}, font_scale = 1.3)

# read in your data
data = pd.read_csv(path)

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Import psmpy class and functions

from psmpy import PsmPy
from psmpy.functions import cohenD
from psmpy.plotting import *

Initialize PsmPy Class

Initialize the PsmPy class:

psm = PsmPy(df, treatment='treatment', indx='pat_id', exclude = [])

Note:

• PsmPy - The class. It will use all covariates in the dataset unless formally excluded in the exclude argument.
• df - the dataframe being passed to the class
• exclude - (optional) parameter and will ignore any covariates (columns) passed to the it during the model fitting process. This will be a list of strings. Note, it is not necessary to pass the unique index column here. That process will be taken care of within the code after specifying your index column.
• indx - required parameter that references a unique ID number for each case in the dataset.

Predict Scores

Calculate logistic propensity scores/logits:

psm.logistic_ps(balance = True)

Note:

• balance - Whether the logistic regression will run in a balanced fashion, default = True.

There often exists a significant Class Imbalance in the data. This will be detected automatically in the software where the majority group has more records than the minority group. We account for this by setting balance=True when calling psm.logistic_ps(). This tells PsmPy to sample from the majority group when fitting the logistic regression model so that the groups are of equal size. This process is repeated until all the entries of the major class have been regressed on the minor class in equal paritions. This calculates both the logistic propensity scores and logits for each entry.

Review values in dataframe:

psm.predicted_data

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Matching algorithm

Perform KNN matching

psm.knn_matched(matcher='propensity_logit', replacement=False, caliper=None)

Note:

• matcher - propensity_logit (default) and generated inprevious step alternative option is propensity_score, specifies the argument on which matching will proceed
• replacement - False (default), determines whethermacthing will happen with or without replacement,when replacement is false matching happens 1:1
• caliper - None (default), user can specify caliper size relative to std. dev of the control sample, restricting neighbors eligible to match within a certain distance.

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Graphical Outputs

Plot the propensity score or propensity logits

Plot the distribution of the propensity scores (or logits) for the two groups side by side.

psm.plot_match(Title='Side by side matched controls', Ylabel='Number ofpatients', Xlabel= 'Propensity logit',names = ['treatment', 'control'],save=True)

Note:

• title - 'Side by side matched controls' (default),creates plot title
• Ylabel - 'Number of patients' (default), string, labelfor y-axis
• Xlabel - 'Propensity logit' (default), string, label for x-axis
• names - ['treatment', 'control'] (default), list of strings for legend
• save - False (default), saves the figure generated to current working directory if True

Plot the effect sizes

psm.effect_size_plot(save=False)

Note:

• save - False (default), saves the figure generated tocurrent working directory if True

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Extra Attributes

Other attributes available to user:

Matched IDs

psm.matched_ids

• matched_ids - returns a dataframe of indicies from the minor class and their associated matched indice from the major class psm.

Major_ID	Minor_ID
6781	9432
3264	7624

Note: That not all matches will be unique if replacement=False

Effect sizes per variable

psm.effect_size

• effect_size - returns dataframe with columns 'variable', 'matching' (before or after), and 'effect_size'

variable	matching	effect_size
hypertension	before	0.5
hypertension	after	0.01
age	7624	9432
age	7624	9432
sex	7624	9432

Note: The thresholds for a small, medium and large effect size were characterizedby Cohen in: J. Cohen, "A Power Primer", Quantitative Methods in Psychology, vol.111, no. 1, pp. 155-159, 1992

Relative Size	Effect Size
small	≤ 0.2
medium	≤ 0.5
large	≤0.8

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Conclusion

This package offers a user friendly propensity score matching protocol created for a Python environment. In this we have tried to capture automatic figure generation, contextualization of the results and flexibility in the matching and modeling protocol to serve a wide base.