Implementing Estimands in Trials: Detailed Clinical Objectives – James Bell, 3rd June 2019

Powerpoint slides from presentation by James Bell ‘Implementing Estimands in Trials: Detailed Clinical Objectives’, 3rd June 2019, PSI conference…

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SAS macro for imputation under generalized linear mixed model

Multiple Imputation requires the modelling of incomplete data under formal assumptions about the combined model for observed and unobserved data (the imputation model).

Generalized Linear Mixed Models provide a natural framework for modelling repeated observations, especially for non-Gaussian outcomes. The new BGLIMM procedure in SAS/Stat 15.1 fits…

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Multiple imputation for time to event data under Kaplan-Meier, Cox or piecewise-exponential frameworks – SAS macros

Multiple imputation (MI) and analysis of imputed time-to-event data is implemented in a collection of SAS macros based on the methodology described in the following publications:

[1] Lipkovich I, Ratitch B, O’Kelly M (2016) Sensitivity to censored-at-random assumption in the analysis of time-to-event…

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Imputation for Gaussian Repeated Measures with time changing covariates.

A Gaussian repeated measures model with one or several unstructured covariance matrices is fitted using proc MCMC sampling directly based on conjugate priors. Any missing values for subject visits with no response are imputed and directly available in the imputed data set.

Main restriction is that every subject uses the…

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

Latest update 13 February 2019
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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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 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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