4 papers · 1 filter
Component over Composite: Mitigating Type I Error Inflation when Imputing "Days Alive and at Home"
Mia S. Tackney, Sarah Dawson, Letao Yuan +2
Background: Days Alive and at Home (DAH) over a pre-defined follow-up period is a novel post-intervention composite outcome that combines data from at least three components: (i) i…
Comparison of Parametric versus Machine-learning Multiple Imputation in Clinical Trials with Missing Continuous Outcomes
Mia S. Tackney, Jonathan W. Bartlett, Elizabeth Williamson +1
The use of flexible machine-learning (ML) models to generate imputations of missing data within the framework of Multiple Imputation (MI) has recently gained traction, particularly…
Multiple Imputation Approaches for Epoch-level Accelerometer data in Trials
Mia S. Tackney, Elizabeth Williamson, Derek G. Cook +3
Clinical trials that investigate interventions on physical activity often use accelerometers to measure step count at a very granular level, often in 5-second epochs. Participants…
Nonmyopic and pseudo-nonmyopic approaches to optimal sequential design in the presence of covariates
Mia S. Tackney, David C. Woods, Ilya Shpitser
In sequential experiments, subjects become available for the study over a period of time, and covariates are often measured at the time of arrival. We consider the setting where th…