Hello Everyone,
I've been doing a little reading about Monotone Data Augmentation using the Monotone Data MCMC Method in SAS Proc MI. I understand that the procedure involves imputing enough just enough values for certain variables to produce a monotone pattern of missingness and then using a regression method for monotone missing data to impute the remaining missing values. My sense is that there are also other methods of implementing this two step process. According to the SAS Online Documentation for PROC MI, the Monotone Data MCMC Method: "... is useful especially when a data set is close to having a monotone missing pattern. In this case, the method only needs to impute a few missing values to the data set to have a monotone missing pattern in the imputed data set. Compared to a full data imputation that imputes all missing values, the monotone data MCMC method imputes fewer missing values in each iteration and achieves approximate stationarity in fewer iterations." So it seems clear that the approach works well when the data approximate a monotone pattern. I have reasons for using the approach that go beyond the normal consideration of whether the data approximate the monotone pattern though. So I was wondering if there is any reason to believe that a Monotone Data Augmentation approach will work poorly when the data deviate substantially from the pattern. And if so, what would constitute a substantial deviation? Thanks, Paul Paul J. Miller, Ph.D. Research Scientist and Statistician Ontario HIV Treatment Network 1300 Yonge St., Suite 308 Toronto, Ontario M4T 1X3 Phone: (416) 642-6486 ext 232 Fax: (416) 640-4245 -------------- next part -------------- An HTML attachment was scrubbed... URL: http://lists.utsouthwestern.edu/pipermail/impute/attachments/20060905/9d6ec170/attachment.htm From pmiller <@t> ohtn.on.ca Tue Sep 5 13:31:41 2006 From: pmiller <@t> ohtn.on.ca (Paul Miller) Date: Tue Sep 5 13:34:38 2006 Subject: [Impute] Specifying diiferent maximum and minumum values on a variable for different groups Message-ID: <[email protected]> Hi Everyone, I was wondering if it's possible to specify different minimum and maximum values on a variable for different groups in a dataset using IVEware or SAS Proc MI or some other program. For example, I'm imputing start and stop dates for HIV antiretroviral drugs and would like to impose one set of restrictions for cases where the start is unknown, the stop is unknown, and the range of possible start and stop dates overlap, and another set of restrictions for all remaining cases. Thanks, Paul Paul J. Miller, Ph.D. Research Scientist and Statistician Ontario HIV Treatment Network 1300 Yonge St., Suite 308 Toronto, Ontario M4T 1X3 Phone: (416) 642-6486 ext 232 Fax: (416) 640-4245 -------------- next part -------------- An HTML attachment was scrubbed... URL: http://lists.utsouthwestern.edu/pipermail/impute/attachments/20060905/c2304461/attachment.htm From allison <@t> soc.upenn.edu Tue Sep 5 14:32:21 2006 From: allison <@t> soc.upenn.edu (Paul Allison) Date: Tue Sep 5 14:32:28 2006 Subject: [Impute] Missing Data in NYC, Nov. 10-11 In-Reply-To: <[email protected]> Message-ID: <[email protected]> On Nov. 10-11 in New York City, I will be presenting my 2-day course on Missing Data. This course provides an in-depth look at modern methods for handling missing data, with particular emphasis on maximum likelihood and multiple imputation. While the course is applications oriented, I also explain the conceptual underpinnings of these new methods in some detail. Maximum likelihood is illustrated with two programs, Amos and LEM. Multiple imputation is demonstrated with two SAS procedures (MI and MIANALYZE) and two Stata commands (ICE and MICOMBINE). The course will be held at the Club Quarters Hotel, 40 West 45th St., in midtown Manhattan, just a couple blocks from Times Square and the theater district. Guest rooms are available at the hotel at a special rate. You can get more detailed information at www.StatisticalHorizons.com Or if you have specific questions, just reply to this e-mail. ----------------------------------------------------------------- Paul D. Allison, Professor and Chair Department of Sociology University of Pennsylvania 3718 Locust Walk Philadelphia, PA 19104-6299 215-898-6712, 215-898-6717 215-573-2081 (fax) http://www.ssc.upenn.edu/~allison
