2 citations · 4 across the 5 of their papers we have counts for
5 papers · 1 filter
Optimizing Dynamic Predictions from Joint Models using Super Learning
Dimitris Rizopoulos, Jeremy M. G. Taylor
Joint models for longitudinal and time-to-event data are often employed to calculate dynamic individualized predictions used in numerous applications of precision medicine. Two com…
A Continuous-Time Dynamic Factor Model for Intensive Longitudinal Data Arising from Mobile Health Studies
Madeline R. Abbott, Walter H. Dempsey, Inbal Nahum-Shani +3
Intensive longitudinal data (ILD) collected in mobile health (mHealth) studies contain rich information on multiple outcomes measured frequently over time that have the potential t…
Surrogacy Validation for Time-to-Event Outcomes with Illness-Death Frailty Models
Emily K. Roberts, Michael R. Elliott, Jeremy M. G. Taylor
A common practice in clinical trials is to evaluate a treatment effect on an intermediate endpoint when the true outcome of interest would be difficult or costly to measure. We con…
A meta-inference framework to integrate multiple external models into a current study
Tian Gu, Jeremy M. G. Taylor, Bhramar Mukherjee
It is becoming increasingly common for researchers to consider incorporating external information from large studies to improve the accuracy of statistical inference instead of rel…
Inference after black box selection
Jelena Markovic, Jonathan Taylor, Jeremy Taylor
We consider the problem of inference for parameters selected to report only after some algorithm, the canonical example being inference for model parameters after a model selection…