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| From | Clyde B Schechter <clyde.schechter@einstein.yu.edu> |
| To | "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu> |
| Subject | Subject: RE: st: Situation where multiple imputation may be of no use? |
| Date | Sat, 11 Feb 2012 20:25:33 +0000 |
In response to my original query about whether MI is of any use in a situation where only the dependent variable will have missing values, Cameron McIntosh writes: "So why not try FIML? What analytical technique are you using? Cam" The simplified situation I described involves a single continuous outcome variable measured in subjects randomly assigned to two groups. So it's a regression of the outcome against an indicator for treatment group. FIML can be applied to this, but I ran some simulations and in this situation it doesn't perform any differently from complete case analysis. And, from a theoretical perspective, I don't think it should. I don't see how in this situation there is any information in the data set that is not already found in the complete cases. But I'd be delighted to learn that I'm wrong about that. Clyde Schechter Department of Family & Social Medicine Albert Einstein College of Medicine Bronx, NY, USA * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/