2 citations · 4 across the 4 of their papers we have counts for
4 papers
Using directed acyclic graphs to determine whether multiple imputation or subsample multiple imputation estimates of an exposure-outcome association are unbiased
Paul Madley-Dowd, Rachael A. Hughes, Maya B. Mathur +2
Missing data is a pervasive problem in epidemiology, with multiple imputation (MI) a commonly used analysis method. MI is valid when data are missing at random (MAR). However, defi…
Relationship between Collider Bias and Interactions on the Log-Additive Scale
Apostolos Gkatzionis, Shaun R. Seaman, Rachael A. Hughes +1
Collider bias occurs when conditioning on a common effect (collider) of two variables . In this manuscript, we quantify the collider bias in the estimated association between…
Bootstrap Inference for Multiple Imputation under Uncongeniality and Misspecification
Jonathan W. Bartlett, Rachael A. Hughes
Multiple imputation has become one of the most popular approaches for handling missing data in statistical analyses. Part of this success is due to Rubin's simple combination rules…
Sample-constrained partial identification with application to selection bias
Matthew Tudball, Rachael Hughes, Kate Tilling +2
Many partial identification problems can be characterized by the optimal value of a function over a set where both the function and set need to be estimated by empirical data. Desp…