177 citations · 179 across the 3 of their papers we have counts for
3 papers
Clustering with missing data: which imputation model for which cluster analysis method?
Vincent Audigier, Ndèye Niang, Matthieu Resche-Rigon
Multiple imputation (MI) is a popular method for dealing with missing values. One main advantage of MI is to separate the imputation phase and the analysis one. However, both are r…
Multiple imputation for multilevel data with continuous and binary variables
Vincent Audigier, Ian R. White, Shahab Jolani +5
We present and compare multiple imputation methods for multilevel continuous and binary data where variables are systematically and sporadically missing. The methods are compared f…
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.…