Category Archives: Imputation based approaches

Imputation based approaches

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 … Continue reading

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 … Continue reading

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 … Continue reading

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 … Continue reading

Reference-based MI for Negative Binomial discrete data – SAS macros

Updated 22 January 2020 and update corrected 6 February 2020 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 … Continue reading