All posts in Imputation based approaches

Imputation of Recurrent event data for partial observed off-treatment data

Latest update 23 March 2018
Quick summary
In the past, many trials have stopped collection of data following discontinuation of randomised treatment. However, more recently data collection continues after randomised treatment discontinuation, since the occurrence of this event is irrelevant to the calculation of a treatment policy estimand. When all…

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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.

When used to analyse an existing partially observed…

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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 Methods for the Behavioral and Social Sciences, 6, 37-48…

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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, first fits a Cox model to the observed data under…

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Pattern Mixture

Quick Summary

The macro imputes under a series of different methods, analyses data using Repeated measures (not ANOVA at a single visit) and provides summary estimates for least-squares means.

Either complete case missing value restriction (CCMV) or nearest case missing value (NCMV)restriction. [subset cases]
. . . and either non-future…

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Plot and compare up to 2 control-based approaches.

Quick summary

This macro calls other macros downloadable from the Imputation-based approaches section of this  web site using a common interface, and plots the results of up to two analyses.

Download

The package can be downloaded from Macro interface and plotting package 20160216

Several examples output files are available…

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Sequential imputation with tipping point and delta adjustment

Quick summary

Control-based Imputation:  CBI_PMM imputes data at each visit in a separate call to the MI procedure in SAS. Initially the data set has only reference (placebo) subjects. When it reaches a visit where one or more subjects have withdrawn those subjects are added to the data set…

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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 which is the log of the length of time observed…

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Stepwise imputation for marginal model based on previous residuals

Updated 16 June 2018
Warning

There is a mistake in the handling of seeds in version 11 of the MISTEP macro when using the BY= facility. This is not a problem in the previous version 9 and earlier versions. When treatment is used as the BY= variable this can lead…

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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., and Kenward, M.G. Analysis of longitudinal trials with…

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