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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Example data set from an antidepressant clinical trial

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This data set is one of only a few publicly available data sets that can be used to demonstrate methods for handling missing data where a continuous outcome is measured repeatedly.

Original data are from an antidepressant clinical trial with four treatments; two doses of an experimental medication…

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Plot and compare up to 2 control-based approaches.

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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.

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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

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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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