23 citations · 44 across the 14 of their papers we have counts for
15 papers
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…
Variants of Romanoff's theorem
Artyom Radomskii
Let and be two sequences of positive integers (not necessarily distinct). Under some restrictions on $…
Accounting for Missing Data in Public Health Research Using a Synthesis of Statistical and Mathematical Models
Paul N Zivich, Bonnie E Shook-Sa, Stephen R Cole +2
Introduction: Accounting for missing data by imputing or weighting conditional on covariates relies on the variable with missingness being observed at least some of the time for al…
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…
Finite sample performance of optimal treatment rule estimators with right-censored outcomes
Michael Jetsupphasuk, Michael G. Hudgens, Jessie K. Edwards +1
Patient care may be improved by recommending treatments based on patient characteristics when there is treatment effect heterogeneity. Recently, there has been a great deal of atte…
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…
The Metropolis algorithm: A useful tool for epidemiologists
Alexander P Keil, Jessie K Edwards, Ashley I Naimi +1
The Metropolis algorithm is a Markov chain Monte Carlo (MCMC) algorithm used to simulate from parameter distributions of interest, such as generalized linear model parameters. The…
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-…
A Causal Inference Framework for Leveraging External Controls in Hybrid Trials
Michael Valancius, Herb Pang, Jiawen Zhu +3
We consider the challenges associated with causal inference in settings where data from a randomized trial is augmented with control data from an external source to improve efficie…
Fusing Trial Data for Treatment Comparisons: Single versus Multi-Span Bridging
Bonnie E. Shook-Sa, Paul N. Zivich, Samuel P. Rosin +4
While randomized controlled trials (RCTs) are critical for establishing the efficacy of new therapies, there are limitations regarding what comparisons can be made directly from tr…
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…
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…