Some tips It has been tested and runs: (i) Using SPSS v25 with R 3.3.3 and Python >3.10 correctly installed (Check Python in Edit / Options / File locations, and in System/Environment variables). Click in File/Open/Script Python, select it and the script will appear in an IDLE window. Run Module. (ii) Using RStudio with R >4.3 and Python >3.10 as interpreter (Tools / Global options / Python). You only need SPSS just for the path to your dataset and to incorporate covariates to the PSmodel (in capital letters and names, do not use the labels). It tolerates missing values acceptably. However, it is desirable to reduce them as much as possible. It is highly recommended to check if the Python modules are available, especially 'pyreadstat' and 'Python-docx', necessary for code 3. To be sure, install them all (numpy, pandas, logging, os, statmodels, sklearn, matplotlib, seaborn, Python-docx), better from RStudio console: library(reticulate) / py_install("pyreadstat", envname = "~/.virtualenvs/r-reticulate"), than from Powershell. Look for indentation errors if the code does not work. Usage:Refine the code with your current research: - The first choice, sampling with or without replacement. By default, Python modules perform sampling without replacement. If you decide with replacement, use the code 4.- Rename C:\XXXXXXXXXXX with the path to your dataset (.sav)- Rename COVS (covariates intended to estimate PS) - Choose the ratio (1:1, 1:2...) and the caliper - Choose bar colors, adjust the limits of the x-axis and y-axis to the desired range...- Rename C:\XXXXXXXXXX with the path to your folder where the outputs will be storedRun the script.

OVERVIEW / FINAL SUMMARY This repository provides 4 variants of a free, Python-based code for performing propensity score (PS) matching. An initiative of the Camargo Cohort Study (Cantabria, Spain), developed with the aim of sharing the tool and spreading the use of PS matching. The code overcomes compatibility issues with R versions and R packages, and implements (i) logistic regression to compute PS, (ii) 1:N matching using the K-nearest neighbour (KNN) algorithm with a customisable caliper, (iii) sampling with or without replacement, (iv) visualisations to assess matching quality and (v) statistics to evaluate the balance. Outputs: Matched pairs stored as '.csv' file, allowing a Coxreg to be performed ('SET' in SPSS). Diagnostic plots stored in the specified output folder, providing a view of SMD and PS distribution. Statistics for matching validation: SMD, variance ratio (VR), McFadden's pseudo-R², and L1 multivariate imbalance. The code has been developed using information from the Matplotlib, Numpy and Seaborn libraries and with OpenAI's ChatGPT support and refinements. No funding was received for conducting this work and there are no financial or non-financial interests to disclose.

Final comments Given the growing use of PSM and the known compatibility issues between versions of SPSS, R and the R packages on which PSM relies, the primary objective of this initiative was to develop a Python-based script that could be implemented regardless of the version of SPSS and R. The tool should be complete, well-validated and easy to implement, with the intention of making it available to clinicians and researchers. Secondary objectives were to produce a matched sample of identified pairs and a well structured balance report. PSM for SPSS v1.0 - the only version we were able to get running - provides a matched sample, but the pairs are not identified, and this information is crucial for running a COXREG. Regarding the Balance report, after discarding the Overall balance due to the lack of a broad consensus, it encompasses the recommended statistics, and we consider it as an achievement. Finally, an unexpected finding. The colour assignment in the lineplot, based on whether a covariate is included in the PS, has shown that PSM can also indirectly reduce the SMD of covariates not explicitly included in the PS model, likely due to underlying correlations or associations.

Comparison between Python-based code and PSM performed by SPSS (based on R packages) The code has been tested by comparing the results with those of a PSM in SPSS based on R packages (Propensity Score Matching for SPSS v1.0, by Thoemmes F). We selected 5 covariates to estimate the PS, caliper=0.20, ratio 1/1, sampling without replacement, and applied them on the same dataset with both methods. We observed significant discrepancies in the PS values and in the composition of the matched sample. The differences were probably due to several factors -PS estimation, optimisation algorithms, caliper application...- reflecting the different performances offered by Python libraries (matplotlib, sklearn) and R-based packages (MatchIt, RItools, cem). However, as shown in the file, the SMD were virtually identical by using both methods. Given that SMD is the most recognized statistic in terms of balance assessment, this result validates our approach and shows that the Python implementation is reliable.

Assessment of the matching process / Validation The matching quality can be rated by using the following recommended statistics: Standardized mean difference: After matching, between -0.1 and 0.1 Variance ratio: Ater matching, close to 1 McFadden's pseudo-R²: After matching, close to 0 L1 multivariate imbalance: L1 is a measure of multivariate imbalance bounded by 0 (perfect balance) and 1 (complete separation in the cross-tabulation). It is desirable for L1 to be smaller in the matched sample than in the unmatched sample. The four tests examine all the covariates used to estimate the PS as well as the variables defined by the user as additional covariates. When running these statistics, sampling without replacement, ratio 1:1 and caliper=0.20 are applied by default. The results are provided as a diagnostic plot (SMD), as separate files or gathered in a Balance Report (code 3).

CODE REPLACEMENT CUSTOMISABLE RATIO AND CALIPER MATCHED PAIRS PSM ASSESSMENT PS matching code 1 Without Ratio: line 73 Caliper: line 84 .csv file SMD (barplot and lineplot) (.png) PS matching code 2 Without Ratio: line 88 Caliper: line 89 .csv file SMD, VR and pseudo-R² (.csv, .txt) PS matching code 3 Without Ratio: line 163 Caliper: line 168 .csv file Lineplot with improvements (.png) Balance report (SMD, VR, pseudo-R² and L1 imbalance) (.docx) PS matching code 4 With Ratio: line 89 Caliper: line 100 .csv file SMD (barplot and lineplot) (.png)