2 citations · 4 across the 3 of their papers we have counts for
3 papers
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.…