Category Archives: Imputation based approaches

Imputation based approaches

Reference-based MI for Negative Binomial discrete data – R package dejaVu

The R package dejaVu, now available on CRAN, implements controlled based multiple imputation for count data, as proposed by Keene, Oliver N., et al. “Missing data sensitivity analysis for recurrent event data using controlled imputation.” Pharmaceutical Statistics 13:4 (2014): 258-264. … Continue reading

Vansteelandt et al’s 2012 doubly robust method

The zip file linked to here contains SAS macros implementing the doubly robust approach described in: Vansteelandt S, Carpenter J, Kenward M (2012), Analysis of incomplete data using inverse probability weighting and doubly robust estimators, Methodology: European Journal of Research … Continue reading

Multiple imputation for informatively censored time to event data – the InformativeCensoring R package

The R package InformativeCensoring, available on CRAN, can be used to perform multiple imputation for a time to event outcome when it is believed censoring may be informative. Two methods are implemented. The first, based on Jackson et al 2014, … Continue reading

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Reference-based MI for Negative Binomial discrete data – SAS macros

Quick summary Statistical analyses of recurrent event data have typically been based on the missing at random assumption (MAR) along with constant event rate. These treat the number of events as having a Negative Binomial distribution with an offset term … Continue reading

Stepwise imputation for marginal model based on previous residuals

Quick summary The macro MIStep duplicates many of the facilities in MONOTONE REG statement in proc MI, but adds the facility to regress on previous residuals rather than previous absolute values. This allows it to fit marginal methods such as … Continue reading

Reference-based MI via Multivariate Normal RM (the “five macros” and MIWithD)

Quick summary The “five macros” fit a Bayesian Normal RM model and then impute post withdrawal data under a series of possible post-withdrawal profiles including J2R, CIR and CR as described by Carpenter et al [Carpenter, J. R., Roger, J., … Continue reading

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