An Instrumental Variable Approach to Account for Informative Treatment Switching in Real-world Evidence

Music City Center 

Reproducible and generalizable assessment of a treatment decision requires principled handling of subsequent treatment decisions whose patterns may shift across cohorts and over time. Discontinuation and switching of treatment in clinical practice is a dynamic process that may be informative for expected outcomes. However, the information about expected outcomes has not been systematically documented or indicated in real-world health care data.
To effectively account for informative treatment switching in real-world evidence, we propose an instrumental variable approach that deals with the poorly documented expected outcomes as unmeasured confounding. Our proposed method is doubly robust, i. e. providing consistent treatment effect estimation whenever either of baseline propensity models and no-switching outcome models is consistently estimated. A co-training of treatment effect parameter and survival outcome regression model eliminates the requirement of a no-switching subset. We further develop an baseline-survival-corrected cross-fitting approach to incorporate general machine learning models for estimating nuisance models. Numerical results demonstrate the validity of proposed method in a wide range of data generating process while a basket of benchmark solutions producing biased or contradictory results. We apply our method to comparison of high-efficacy vs standard efficacy disease modifying treatment as the second line therapy of multiple sclerosis.

Survival analysis

causal inference

Instrumental variables