29: Application of Inverse Probability Weighting for Dealing with Missing Data in Causal Inference Study

Music City Center 

Inverse probability treatment weighting (IPTW) is a widely used method in causal inference to address confounding bias; however missing data frequently arises in such studies, potentially impacting the validity of causal estimates. One approach to handling missing data is to specify a missingness model and estimate the probability that an individual is a complete case and derive a corresponding missingness weight (wm). Under this approach, we use the missingness weight to estimate the treatment weight (wt), by fitting a weighted propensity score model for treatment. We conduct a simulation study to evaluate the optimal approach for incorporating missing data weights within the IPTW framework. Specifically, we compare whether using the product of missing data weights and treatment weights (wm x wt) in the final analysis model yields more accurate causal effect estimates than using treatment weights (wt) alone. Our findings will provide guidance on the optimal implementation of inverse probability weighting to address both missing data and confounding bias, ultimately strengthening the robustness of causal inference in observational research.

Causal inference study

missing data

propensity score model

IPTW