Machine learning methods as alternatives to regression modelling in propensity score and disease risk score based methods - Guo, Y (2023)
Background Propensity Score (PS) and Disease Risk Score (DRS) based methods have become increasingly popular in observational studies to reduce confounding bias when estimating treatment effects. Traditionally, logistic regression modelling has been the primary approach to estimating PS and DRS. However, logistic models have limitations, particularly in scenarios with large numbers of confounders, nonlinear relationships, and complex interactions. Machine learning (ML) methods have shown potential as alternatives to regression modelling due to their ability to handle high-dimensional data and capture complex relationships. However, there is a scarcity of data evaluating and comparing these methods to logistic regression modelling in the context of PS and DRS estimation.