Control-based Imputation: CBI_PMM imputes data at each visit in a separate call to the MI procedure in SAS. Initially the data set has only reference (placebo) subjects. When it reaches a visit where one or more subjects have withdrawn those subjects are added to the data set. Active subjects are imputed as if they were on placebo when they were in fact on active up to withdrawal. Their response data is ignored apart from appearing as covariates in the sequential regressions after withdrawal. As such treatment never appears in the imputation model.
Delta adjusted MAR: Delta_PMM imputes with delta adjustment on top of an MAR model. It is carried out using a conditional delta algorithm, where delta is embedded within a stepwise regression approach where regression is on previous absolute values (observed or imputed).
Tipping Point analysis: Delta_and_Tip carries out a series of delta adjusted analyses as above to provide a tipping point analysis.
Analysis of each imputed data set either uses RM or univariate ANCOVA and the macros summarize these using Rubin’s rules.
The following macros
cbi_pmm2.sas [Control-based imputation]
delta_and_tip_2.sas [Delta and tipping point]
delta_pmm2.sas [Delta for MAR]
can be downloaded here, PMM Delta Tipping Point and CBI_20150602
The zip file also includes documentation files (docx) for each of the first three and demonstration SAS program files for the first two. It also includes the PharmaSUG2011 paper Implementation of Pattern-Mixture Models Using Standard SAS/STAT Procedures by Ratitch & O’Kelly which explains the methods in detail.