Date: 2026-11-06
Time: 15:30-16:30 (Montreal time)
Location: Online via Zoom
https://mcgill.zoom.us/j/83483745684
Meeting ID: 834 8374 5684
Abstract:
Randomized clinical trials are widely regarded as the gold standard for causal inference. However, intercurrent events (ICEs) occurring after treatment initiation can complicate both the definition and estimation of treatment effects. Common examples include treatment noncompliance, where the treatment received differs from the treatment assigned, and the use of additional medications.
In the first part of this talk, I will focus on treatment noncompliance in HIV clinical trials. Following the principal stratum strategy, we define the target estimand within a latent subpopulation called always-compliers. We propose an instrumented hidden Markov model to characterize longitudinal compliance trajectories and event hazards conditional on the current compliance state. We establish nonparametric identification results under the proposed model, develop a maximum likelihood estimator, and extend the framework to accommodate two-stage sampling designs.
In the second part of this talk, I will focus on the use of additional medications in pain clinical trials. Following the hypothetical strategy, we define the target estimand in terms of the controlled direct effect from the causal mediation analysis literature. We then employ structural mean models to identify and estimate this effect, allowing us to evaluate treatment effects under hypothetical interventions on additional medication use.