2 papers
stat.ME2026
Comparing Missing Data Methods for Estimating Average Treatment Effects Under Time-Varying Confounding: A Simulation Study
Ben Swallow, Lars Brestrich, Victor Velasco-Pardo
Missing data and confounding are common in real-world statistical applications, yet few studies have examined how imputation methods perform under time-varying confounding in binar…
stat.ME2026
Landmarking with Latent Class Mixed Models for Dynamic Prediction of Time-to-event Data with Heterogeneous Biomarker Trajectories
Víctor Velasco-Pardo, Nathan Constantine-Cooke, Charlie W. Lees +1
The increasing ability to securely access electronic health records (EHR) has created unprecedented opportunities to monitor the health trajectories of large heterogeneous patient…