92 citations · 110 across the 23 of their papers we have counts for
25 papers
Difference-in-Differences for Heterogeneous Effects of General Treatment Strategies Accounting for Time-Varying Covariates
Oliver Dukes, Zach Shahn, James Robins +1
We introduce a class of marginal Structural Nested Mean Models (mSNMMs) that enable modeling time-varying effect modification by a subset of covariates required as adjustment varia…
Order Dependence in Regression by Composition: Discussion on "Regression by Composition'' by Farewell, Daniel, Stensrud, and Huitfeldt
Mei Dong, Linbo Wang, Lin Liu +1
We discuss the regression-by-composition framework of Farewell, Daniel, Stensrud and Huitfeldt, highlighting a key consequence of its sequential construction: order dependence. Reo…
A Parameter-Centric View on Regression
Jingxin Yan, Lin Liu, Oliver Dukes +2
Discussion on ``Regression by Composition'' by Farewell, Daniel, Stensrud, and Huitfeldt
Toward Variation-Independent Regression by Composition
Ruixuan Zhao, Oliver Dukes, Linbo Wang +1
Discussion on "Regression by Composition" by Farewell, Daniel, Stensrud, and Huitfeldt.
Efficient estimation of cumulative incidence curves via data fusion with surrogates: application to integrated analysis of vaccine trial and immunobridging data
Pan Zhao, Peter B. Gilbert, Oliver Dukes +1
Refined vaccine regimens containing variant-matched inserts are often authorized based on historical phase 3 efficacy trials together with immunobridging studies. Phase 3 trials ar…
On estimands in target trial emulation
Edoardo Efrem Gervasoni, Liesbet De Bus, Stijn Vansteelandt +1
The target trial framework enables causal inference from longitudinal observational data by emulating randomized trials initiated at multiple time points. Precision is often improv…
Using causal diagrams to assess parallel trends in difference-in-differences studies
Audrey Renson, Oliver Dukes, Zach Shahn
Difference-in-differences (DID) is popular because it can allow for unmeasured confounding when the key assumption of parallel trends holds. However, there exists little guidance o…
Nonparametric tests of treatment effect homogeneity for policy-makers
Oliver Dukes, Mats J. Stensrud, Riccardo Brioschi +1
Recent work has focused on nonparametric estimation of conditional treatment effects, but inference has remained relatively unexplored. We propose a class of nonparametric tests fo…
Doubly-robust inference and optimality in structure-agnostic models with smoothness
Matteo Bonvini, Edward H. Kennedy, Oliver Dukes +1
We study the problem of constructing an estimator of the average treatment effect (ATE) with observational data. The celebrated doubly-robust, augmented-IPW (AIPW) estimator genera…
Nested Instrumental Variables Analysis: Switcher Average Treatment Effect, Identification, Efficient Estimation and Generalizability
Rui Wang, Ying-Qi Zhao, Oliver Dukes +1
Instrumental variables (IVs) are widely used to estimate causal effects from non-randomized data. A canonical example is a randomized trial with noncompliance, in which the randomi…
The Use of Covariate Adjustment in Randomized Controlled Trials: An Overview
Kelly Van Lancker, Frank Bretz, Oliver Dukes
There has been a growing interest in covariate adjustment in the analysis of randomized controlled trials in past years. For instance, the U.S. Food and Drug Administration recentl…
Augmented balancing weights as linear regression
David Bruns-Smith, Oliver Dukes, Avi Feller +1
We provide a novel characterization of augmented balancing weights, also known as automatic debiased machine learning (AutoDML). These popular doubly robust or de-biased machine le…
Generalizing the intention-to-treat effect of an active control against placebo from historical placebo-controlled trials to an active-controlled trial: A case study of the efficacy of daily oral TDF/FTC in the HPTN 084 study
Qijia He, Fei Gao, Oliver Dukes +2
In many clinical settings, an active-controlled trial design (e.g., a non-inferiority or superiority design) is often used to compare an experimental medicine to an active control…
Reconciling model-X and doubly robust approaches to conditional independence testing
Ziang Niu, Abhinav Chakraborty, Oliver Dukes +1
Model-X approaches to testing conditional independence between a predictor and an outcome variable given a vector of covariates usually assume exact knowledge of the conditional di…
Alternative approaches for analysing repeated measures data that are missing not at random
Oliver Dukes, David Richardson, Eric Tchetgen Tchetgen
We consider studies where multiple measures on an outcome variable are collected over time, but some subjects drop out before the end of follow up. Analyses of such data often proc…
Structural Nested Mean Models Under Parallel Trends Assumptions
Zach Shahn, Oliver Dukes, Meghana Shamsunder +3
We link and extend two approaches to estimating time-varying treatment effects on repeated continuous outcomes--time-varying Difference in Differences (DiD; see Roth et al. (2023)…
Ensuring valid inference for hazard ratios after variable selection
Kelly Van Lancker, Oliver Dukes, Stijn Vansteelandt
The problem of how to best select variables for confounding adjustment forms one of the key challenges in the evaluation of exposure effects in observational studies, and has been…
Translating questions to estimands in randomized clinical trials with intercurrent events
Mats J. Stensrud, Oliver Dukes
Intercurrent (post-treatment) events occur frequently in randomized trials, and investigators often express interest in treatment effects that suitably take account of these events…
Proximal mediation analysis
Oliver Dukes, Ilya Shpitser, Eric J. Tchetgen Tchetgen
A common concern when trying to draw causal inferences from observational data is that the measured covariates are insufficiently rich to account for all sources of confounding. In…
On Doubly Robust Inference for Double Machine Learning in Semiparametric Regression
Oliver Dukes, Stijn Vansteelandt, David Whitney
Due to concerns about parametric model misspecification, there is interest in using machine learning to adjust for confounding when evaluating the causal effect of an exposure on a…
Demystifying statistical learning based on efficient influence functions
Oliver Hines, Oliver Dukes, Karla Diaz-Ordaz +1
Evaluation of treatment effects and more general estimands is typically achieved via parametric modelling, which is unsatisfactory since model misspecification is likely. Data-adap…
Principled Selection of Baseline Covariates to Account for Censoring in Randomized Trials with a Survival Endpoint
Kelly Van Lancker, Oliver Dukes, Stijn Vansteelandt
The analysis of randomized trials with time-to-event endpoints is nearly always plagued by the problem of censoring. As the censoring mechanism is usually unknown, analyses typical…
Assumption-lean inference for generalised linear model parameters
Stijn Vansteelandt, Oliver Dukes
Inference for the parameters indexing generalised linear models is routinely based on the assumption that the model is correct and a priori specified. This is unsatisfactory becaus…
Uniformly valid confidence intervals for conditional treatment effects in misspecified high-dimensional models
Oliver Dukes, Stijn Vansteelandt
Eliminating the effect of confounding in observational studies typically involves fitting a model for an outcome adjusted for covariates. When, as often, these covariates are high-…