Nonparametric Efficient Estimation of Dynamic Treatment Regimes with Competing Risks

Music City Center 

Osteoporotic fractures, treated via bisphosphonates (BPs), present a major public health burden. However, prolonged BP use may increase risk of uncommon severe outcomes that are difficult to study due to rarity, late onset, and potential death before observation. BP use is often paused for several years to mitigate such risks, but long-term effects and ideal duration of breaks are unknown. Optimal treatment thus requires modeling risks under dynamic regimes, balancing BP use and holiday durations. Methods such as inverse probability weighting and marginal structural models are used to study causal effects of time-varying exposures with semi-competing risks but often rely on strong assumptions and may lack efficiency, which is crucial for rare outcomes. In this work, we construct nonparametric efficient estimators to assess cumulative incidence of such events under various treatment regimes, accounting for mortality as a competing event. We also analyze the asymptotic behavior of our estimators via empirical process theory. Our method allows us to leverage patient health records and claims data to provide straightforward inferential methods for rare outcomes.

dynamic treatment regimes

competing risks

causal inference

observational data analysis

nonparametric methods

efficient estimation 

Biometrics Section