activity
20042026
most citedStatistical modeling of causal effects in continuous time

34 citations · 55 across the 10 of their papers we have counts for

collaborators

13 papers

stat.AP2026

Regression Not-to-the-Mean: An Oddity of Regression, Illustrated with the Risk of Overdose Deaths

Kelly C. Kung, Natasha K. Martin, Judith J. Lok

Recent works in econometrics have shown that there can be issues with applying a constant treatment effect model in longitudinal settings with staggered treatment and heterogeneous…

stat.AP2025

Causal indirect effect of an HIV curative treatment: mediators subject to an assay limit and measurement error

Vindyani Herath, Ronald J. Bosch, Judith J. Lok

Causal mediation analysis decomposes the total effect of a treatment on an outcome into the indirect effect, operating through the mediator, and the direct effect, operating throug…

stat.ME2023★ 2 cited

Learn-As-you-GO (LAGO) Trials: Optimizing Treatments and Preventing Trial Failure Through Ongoing Learning

Ante Bing, Donna Spiegelman, Daniel Nevo +1

It is well known that changing the intervention package while a trial is ongoing does not lead to valid inference using standard statistical methods. However, it is often necessary…

math.ST2021★ 2 cited

How estimating nuisance parameters can reduce the variance (with consistent variance estimation)

Judith J. Lok

We often estimate a parameter of interest psi when the identifying conditions involve a nuisance parameter theta. Examples from causal inference are Inverse Probability Weighting,…

stat.ME2021

Causal mediation analysis with mediator values below an assay limit

Ariel Chernofsky, Ronald J. Bosch, Judith J. Lok

Causal indirect and direct effects provide an interpretable method for decomposing the total effect of an exposure on an outcome into the effect through a mediator and the effect t…

math.ST2021★ 1 cited

Optimal estimation of coarse structural nested mean models with application to initiating ART in HIV infected patients

Judith J. Lok, Department of Mathematics, Statistics +1

Coarse structural nested mean models are used to estimate treatment effects from longitudinal observational data. Coarse structural nested mean models lead to a large class of esti…