27 citations · 38 across the 5 of their papers we have counts for
5 papers
Sensitivity analysis for incomplete data via unmeasured confounding
Heng Chen, Daniel F. Heitjan
We present a method to analyze sensitivity of frequentist inferences to potential nonignorability of the missingness mechanism. Rather than starting from the selection model, as is…
UQ-ARMED: Uncertainty quantification of adversarially-regularized mixed effects deep learning for clustered non-iid data
Alex Treacher, Kevin Nguyen, Dylan Owens +2
This work demonstrates the ability to produce readily interpretable statistical metrics for model fit, fixed effects covariance coefficients, and prediction confidence. Importantly…
PSA-Net: Deep Learning based Physician Style-Aware Segmentation Network for Post-Operative Prostate Cancer Clinical Target Volume
Anjali Balagopal, Howard Morgan, Michael Dohopoloski +9
Automatic segmentation of medical images with DL algorithms has proven to be highly successful. With most of these algorithms, inter-observer variation is an acknowledged problem,…
Evaluating costs with unmeasured confounding: A sensitivity analysis for the treatment effect
Elizabeth A. Handorf, Justin E. Bekelman, Daniel F. Heitjan +1
Estimates of the effects of treatment on cost from observational studies are subject to bias if there are unmeasured confounders. It is therefore advisable in practice to assess th…
Truth and memory: Linking instantaneous and retrospective self-reported cigarette consumption
Hao Wang, Saul Shiffman, Sandra D. Griffith +1
Studies of smoking behavior commonly use the time-line follow-back (TLFB) method, or periodic retrospective recall, to gather data on daily cigarette consumption. TLFB is considere…