23 citations · 41 across the 14 of their papers we have counts for
14 papers
Transporting summary measures of relative effects from randomised trials to the treated patient population: an application to breast cancer endocrine therapy
Bonnie E. Shook-Sa, Paul N. Zivich, Carolyn Taylor +4
Randomised trials often report relative treatment effects, such as risk ratios and hazard ratios, for trial populations. Clinical decision-making, however, often benefits from esti…
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…
Assessing Vaccine Effectiveness in Observational Studies via Nested Trial Emulation
Justin B. DeMonte, Bonnie E. Shook-Sa, Michael G. Hudgens
Observational data are frequently used to evaluate real-world vaccine effectiveness (VE). For vaccines such as those developed against COVID-19, VE may vary over calendar time beca…
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-…
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…
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…
Exposure Effects on Count Outcomes with Observational Data, with Application to Incarcerated Women
Bonnie E. Shook-Sa, Michael G. Hudgens, Andrea K. Knittel +14
Causal inference methods can be applied to estimate the effect of a point exposure or treatment on an outcome of interest using data from observational studies. For example, in the…
Estimating SARS-CoV-2 Seroprevalence
Samuel P. Rosin, Bonnie E. Shook-Sa, Stephen R. Cole +1
Governments and public health authorities use seroprevalence studies to guide responses to the COVID-19 pandemic. Seroprevalence surveys estimate the proportion of individuals who…
Power and Sample Size for Marginal Structural Models
Bonnie E. Shook-Sa, Michael G. Hudgens
Marginal structural models fit via inverse probability of treatment weighting are commonly used to control for confounding when estimating causal effects from observational data. W…