| Title |
Propensity Score Analysis in Observational Studies with Confounders and Missing Data |
|
Authors |
Batocael, Margaret A. and Tubo, Bernadette F. |
|
Publication date |
2025/03-04 |
|
Journal |
International Journal for Multidisciplinary Research (IJFMR) |
| Volume |
7 |
| Issue |
2 |
| Pages |
1-8 |
| Publisher |
Sky Research Publication and Journals |
| Abstract |
This study explores the use of propensity score matching to reduce bias in estimating treatment effects from observational data. Specifically, it evaluates the performance of logistic regression and machine learning-based methods for propensity score estimation under conditions involving missing data and complex confounding structures. Simulation studies were conducted using both complete and imputed data sets across varying levels of missingness, unmeasured confounding, and nonlinearity in the true propensity score. Logistic regression (LR), Generalized boosting models (GBM), and Bayesian additive regression trees (BART) are compared based on estimation accuracy and covariate balance. Performance are assessed using root mean square error (RMSE), mean absolute error (MAE), R-squared, absolute standardized mean differences (ASMD), and Kolmogorov-Smirnov (KS) statistics. The results highlight trade-offs in model robustness, particularly between predictive accuracy and covariate balance, offering practical insights for selecting appropriate propensity score models in complex observational settings. |
| Index terms / Keywords |
Propensity score, Observational study, Missing data, Unmeasured Confounding |
| DOI |
doi.org/10.36948/ijfmr.2025.v07i02.43209 |
| URL |
|
Mindanao State
University - Iligan Institute of Technology