126 citations · 155 across the 6 of their papers we have counts for
6 papers
Applying the estimands framework to non-inferiority trials: guidance on choice of hypothetical estimands for non-adherence and comparison of estimation methods
Katy E Morgan, Ian R White, Clémence Leyrat +2
A common concern in non-inferiority (NI) trials is that non adherence due, for example, to poor study conduct can make treatment arms artificially similar. Because intention to tre…
MatchThem:: Matching and Weighting after Multiple Imputation
Farhad Pishgar, Noah Greifer, Clémence Leyrat +1
Balancing the distributions of the confounders across the exposure levels in an observational study through matching or weighting is an accepted method to control for confounding d…
G-computation and doubly robust standardisation for continuous-time data: a comparison with inverse probability weighting
A. Chatton, F. Le Borgne, C. Leyrat +1
In time-to-event settings, g-computation and doubly robust estimators are based on discrete-time data. However, many biological processes are evolving continuously over time. In th…
A review and evaluation of standard methods to handle missing data on time-varying confounders in marginal structural models
Clemence Leyrat, James R Carpenter, Sebastien Bailly +1
Marginal structural models (MSMs) are commonly used to estimate causal intervention effects in longitudinal non-randomised studies. A common issue when analysing data from observat…
Propensity scores using missingness pattern information: a practical guide
Helen A. Blake, Clemence Leyrat, Kathryn E. Mansfield +4
Electronic health records are a valuable data source for investigating health-related questions, and propensity score analysis has become an increasingly popular approach to addres…
Propensity score analysis with partially observed confounders: how should multiple imputation be used?
Clemence Leyrat, Shaun R. Seaman, Ian R. White +6
Inverse probability of treatment weighting (IPTW) is a popular propensity score (PS)-based approach to estimate causal effects in observational studies at risk of confounding bias.…