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Biometrics|November 28, 2020
Joint calibrated estimation of inverse probability of treatment and censoring weights for marginal structural modelsSean Yiu, Li Su
Biometrika|April 30, 2019
Covariate association eliminating weights: a unified weighting framework for causal effect estimationSean Yiu, Li Su
Journal of Biopharmaceutical Statistics|June 19, 2026
Simulated treatment comparisons with jackknife pseudo values for estimating population-adjusted marginal treatment effectsSean Yiu, Kirsty Rhodes
Statistics in Medicine|October 19, 2016
A joint modelling approach for multistate processes subject to resolution and under intermittent observationsSean Yiu, Brian Tom
Statistical Methods in Medical Research|May 25, 2017
Two-part models with stochastic processes for modelling longitudinal semicontinuous data: Computationally efficient inference and modelling the overall marginal meanSean Yiu, Brian Dm Tom
Statistical Methods in Medical Research|April 12, 2022
Sensitivity analysis for calibrated inverse probability-of-censoring weighted estimators under non-ignorable dropoutLi Su, Shaun R Seaman, Sean Yiu
Journal of the Royal Statistical Society. Series C, Applied Statistics|January 27, 2018
Clustered multistate models with observation level random effects, mover-stayer effects and dynamic covariates: modelling transition intensities and sojourn times in a study of psoriatic arthritisSean Yiu, Vernon T Farewell, Brian D M Tom
Statistics in Medicine|August 9, 2016
Trivariate mover-stayer counting process models for investigating joint damage in psoriatic arthritisSean Yiu, Brian D M Tom, Vernon T Farewell
Journal of the Royal Statistical Society. Series C, Applied Statistics|July 15, 2017
Exploring the existence of a stayer population with mover-stayer counting process models: application to joint damage in psoriatic arthritisSean Yiu, Vernon T Farewell, Brian D M Tom
Pharmaceutical Statistics|April 17, 2024
Rejoinder to the letter: "Standard and reference-based conditional mean imputation: Regulators and trial statisticians be aware!"Marcel Wolbers, Alessandro Noci, Paul Delmar, et al.
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