94 citations · 144 across the 20 of their papers we have counts for
14 papers · 1 filter
Novel g-computation algorithms for time-varying actions with recurrent and semi-competing events
Alena Sorensen D'Alessio, Lucas M. Neuroth, Jessie K Edwards +2
Background: A core aspect of epidemiology is determining the impacts of potential public health interventions over time. With long follow-up periods, epidemiologists may need to co…
Confidence Regions for Multiple Outcomes, Effect Modifiers, and Other Multiple Comparisons
Paul N Zivich, Stephen R Cole, Noah Greifer +3
In epidemiology, some have argued that multiple comparison corrections are not necessary as there is rarely interest in the universal null hypothesis. From a parameter estimation p…
Constructing g-computation estimators: two case studies in selection bias
Paul N Zivich, Haidong Lu
G-computation is a useful estimation method that can be adapted to address various biases in epidemiology. However, these adaptations may not be obvious for some complex causal str…
Structural Nested Mean Models Under Parallel Trends with Interference
Zach Shahn, Paul Zivich, Audrey Renson
Despite the common occurrence of interference in Difference-in-Differences (DiD) applications, standard DiD methods rely on an assumption that interference is absent, and comparati…
Double Robust Variance Estimation with Parametric Working Models
Bonnie E. Shook-Sa, Paul N. Zivich, Chanhwa Lee +5
Doubly robust estimators have gained popularity in the field of causal inference due to their ability to provide consistent point estimates when either an outcome or exposure model…
Synthesis estimators for positivity violations with a continuous covariate
Paul N Zivich, Jessie K Edwards, Bonnie E Shook-Sa +3
Studies intended to estimate the effect of a treatment, like randomized trials, may not be sampled from the desired target population. To correct for this discrepancy, estimates ca…
Empirical sandwich variance estimator for iterated conditional expectation g-computation
Paul N Zivich, Rachael K Ross, Bonnie E Shook-Sa +2
Iterated conditional expectation (ICE) g-computation is an estimation approach for addressing time-varying confounding for both longitudinal and time-to-event data. Unlike other g-…
Transportability without positivity: a synthesis of statistical and simulation modeling
Paul N Zivich, Jessie K Edwards, Eric T Lofgren +3
When estimating an effect of an action with a randomized or observational study, that study is often not a random sample of the desired target population. Instead, estimates from t…
Positivity: Identifiability and Estimability
Paul N Zivich, Stephen R Cole, Daniel Westreich
Positivity, the assumption that every unique combination of confounding variables that occurs in a population has a non-zero probability of an action, can be further delineated as…
Identifying and estimating effects of sustained interventions under parallel trends assumptions
Audrey Renson, Michael Hudgens, Alexander Keil +2
Many research questions in public health and medicine concern sustained interventions in populations defined by substantive priorities. Existing methods to answer such questions ty…
Bridged treatment comparisons: an illustrative application in HIV treatment
Paul N Zivich, Stephen R Cole, Jessie K Edwards +3
Comparisons of treatments, interventions, or exposures are of central interest in epidemiology, but direct comparisons are not always possible due to practical or ethical reasons.…
Delicatessen: Automated Estimating Equations in Python
Paul N Zivich, Mark Klose, Stephen R Cole +2
Estimating equation theory provides a unified framework for statistical modeling and inference. Applying estimating equations has historically involved extensive matrix algebra and…
Tutorial: Introduction to computational causal inference using reproducible Stata, R and Python code
Matthew J. Smith, Camille Maringe, Bernard Rachet +4
The purpose of many health studies is to estimate the effect of an exposure on an outcome. It is not always ethical to assign an exposure to individuals in randomised controlled tr…
Machine learning for causal inference: on the use of cross-fit estimators
Paul N Zivich, Alexander Breskin
Modern causal inference methods allow machine learning to be used to weaken parametric modeling assumptions. However, the use of machine learning may result in complications for in…