Author Archives: Jonathan Bartlett

Example datasets with low and high dropout

Quick Summary 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 … Continue reading

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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. … 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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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 … Continue reading

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Stata export and import to Realcom Impute

A Stata program is available for import and export to the Realcom Impute software. The Stata program can be installed by typing the following (one line) in Stata’s command prompt: net install realcomImpute, from (https://raw.githubusercontent.com/jwb133/StataRealcomImpute/master/) replace Please note that we … Continue reading

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Direct likelihood with influence and residual diagnostics

These SAS macros focus on the direct likelihood analysis approach under MAR assumption with influence and residual diagnostics. In many clinical studies such as in the highly controlled scenario of longitudinal confirmatory trials, it is plausible to start with MAR … Continue reading

SAS code for describing and plotting withdrawal rates

These SAS macros provide basic information to characterize withdrawal/dropout information in the data set which can be the preliminary step of the missing data analyses. The descriptive summary statistics outputs provide visitwise percentages of patients with at least one post-baseline … Continue reading

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Example Language for Statistical Analysis Plans and Protocols Describing Some Frequently-Used Methods for Handling Missing Data

Example Language for Statistical Analysis Plans and Protocols Describing Some Frequently-Used Methods for Handling Missing Data

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