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 same covariance matrix throughout their series of visits.

Data is input in vertical form just like proc MIXED.

The main application is the modelling of off-treatment data, and other situations where the actual treatment changes across visits. Subsequent analysis will usually be based on multiple imputation techniques. The tools can also be used to fit many of the models usually fitted using the GSK 5 macros.

The implementation is fast (about ten times faster than GSK 5 macros) and leads to chains with very little auto-correlation.

Downloads

The following files are contained in a zip file downloaded as RMConj_19180827.

1) RMConj_Explained1.pdf. A description of the tool and the methods used.

2) RMConj32.sas. The SAS code for the macro. The header includes a detailed description and development history.

3) MIAnalze04.sas. Macro used in examples for combining results multiple imputations.

4) Demo1.sas is an example program file that does MAR, J2R and CIR analyses to the standard DIA example data set chapter15_example.sas7bdat. Results are in file Demo1-results.pdf.

5) RMC_FollowOn1.sas is an example program outlining the analysis including follow-up observations after treatment withdrawal. Data setĀ fudata1.sas7bdat is an expanded version of the DIA data. set based on a J2R model. Results are in the file RMC_FollowOn1-results.pdf.

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