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 J2R and CIR, and causal using an efficient stepwise algorithm.
The macro also allows imputation under models which include  treatment switching. So can be used for impution based on post-withdrawal experience.
Includes an MIAnalyze macro that fits a univariate ANOVA model and summarises least-squares means and their differences using Rubin’s rules. Multiple calls are appended to form a single dataset for comparing methods.

Limitations

Requires parallel data format, as provided by the BUILD macro supplied with the MyMCMC macro.
Requires previous fixing of any intermediate missing data

Downloads

The following files are contined in a zip file downloaded as MIStep20170118

1) MIStep_explained02.pdf. An introduction to the theory behind using previous residuals, rather than previous absolute values, to generate marginal reference-based imputations such as Jump to Reference (J2R). This also explains possible uses for the macro and results of the example program below.
2) MIStep03.sas. The SAS code for the macro. The header includes a detailed description.
3) MIAnalyze02.sas. A SAS macro that is used the exaples to run an ANCOVA analysis and summarise across imputed data sets using Rubin’s rules.
4) MIStep_demo7.sas and mistep_demo7-results.html. Example of using the macro.s on the DIA working group example data set. This includes standard MAR, J2R, CIR, OLCMCF, Casual with K0=0.5 and also with K1=0.5 and separate correlation (shared variance) as examples.
5) GSKTest5.sas and GSKTest5-results.html. Program code and output for the same examples using the GSK 5 macros showing similar values based of 1000 imputations in both cases.

This page was written by James Roger ( james@livedata.co.uk ).

Comments are closed.