Sensitivity analysis after multiple imputation under missing at random: a weighting approach

By James R. Carpenter, Michael G. Kenward, Ian R. White. 2007

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Citations in the corpus

Informing sequential clinical decision-making through reinforcement learning: an empirical study (2010, background): “There is a large literature on testing the validity of the assumptions made in multiple imputation, as well as a variety of sensitivity analyses (Little and Rubin 1987; Robins et al. 1999; Scharfstein et al. 1999; Gelman et al. 2005; Carpenter et al. 2007; NAP 2010).”
A randomised controlled trial of dietary improvement for adults with major depression (the ‘SMILES’ trial) (2017, methodology): “To test departures from missing at random (MAR), a weighted sensitivity analysis using the Selection Model Approach was applied to the main outcome findings [43, 44].”
Multiple imputation using chained equations: Issues and guidance for practice (2011, methodology): “First, data can be imputed under MAR and the imputed data sets can be weighted to reflect their plausibility under specified MNAR mechanisms [5].”
Strategy for Modelling Nonrandom Missing Data Mechanisms in Observational Studies Using Bayesian Methods (2012, methodology): “(1999) provide an early example, and Carpenter et al. (2007) obtain an overall MNAR estimate by weighting imputations generated under MAR according to the assumed degree of departure from MAR.”
How should variable selection be performed with multiply imputed data? (2008, background): “This is the same missing data pattern as for MAR (below) and enables a clear comparison of variable selection approaches between MCAR and MAR assumptions.”
The proportion of missing data should not be used to guide decisions on multiple imputation (2019, background): “Thus, where conclusions are being drawn from a small number of complete cases, we recommend sensitivity analyses to explore a range of plausible analysis and imputation models, as well as the impact of deviations from MAR [9,43].”