Example datasets with low and high dropout

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These two data sets are made publicly available so that they can be used to demonstrate methods for handling missing data where a continuous outcome is measured repeatedly.The purpose is to contrast similar data with a low dropout rate and that with a high dropout rate. For…

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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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Substantive model compatible imputation of missing covariates

Missing covariate data commonly occur in epidemiological and clinical research, and are often dealt with using multiple imputation (MI). The imputation of partially observed covariates is complicated if the model of interest is non-linear (e.g. Cox proportional hazards model), or contains non-linear (e.g. squared) or interaction…

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Selection models

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Selection model is one of the most famous classical statistical methods to handle missing data analyses under MNAR assumption (Diggle and kenward 1994). It is based on factorizations of joint likelihood of both measurement process and missingness process. A marginal density of the measurement process describes the complete…

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When and how to use reference based imputation for missing data (2013)

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Slides presented by Michael O’Kelly & James Roger

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The slides can be downloaded at training_ref_imputation…

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A Practical Guide to Preventing and Treating Missing Data (2013)

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Material presented by Craig Mallinckrodt and Russ Wolfinger in 2013 at the FDA workshop on missing data.

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The pdf file is available in zip format at training_2013FDA_Workshop…

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Shared parameter models

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In contrast to most of the other macros this fits a random coefficient regression model.

The longitudinal measurement process model follows a standard random-coefficient mixed effect model
The dropout mechanism model uses a complementary log-log link or logit link.

These 2 models are linked by latent…

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Pattern Mixture

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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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