This approach would also work for the relative risk with all of the CL= options other than EXACT. ... View more

Unfortunately, PROC FREQ doesn’t provide RISKDIFF analysis for two-way tables when there is a zero-frequency column(both treatment arms have no positive responses). In the meantime the work-around to assign very small weights to the zero-frequency cells in your two-way table is the only option. When you use this work-around, the following confidence limit types are available for the risk difference: Agresti-Caffo, Hauck-Anderson, Miettinen-Nurminen (score), and Newcombe. Of course the traditional/Wald confidence limits are also available, but equal (0,0) in the case of a zero-frequency column where the estimate of the risk difference is 0.    ... View more

You could try the suggestions in the usage note below. 24475 - Handling the ERROR: "An imputed variable value is not in the specified range after 100 tries" (sas.com) ... View more

There is a known issue in Proc MI that will be fixed in an upcoming release that requires you also include the MODELOBS= variable in the MIN/MAX= list.  It appends it to the beginning of the variable list on the VAR statement.  Try this code instead: proc mi data=widegrp1_2 seed=93538001 nimpute=50 out=newgrp1_2 noprint minimum= . . 0 0 maximum=. . 14 14 minmaxiter=600 ; class TRT01PN CSIRSTR ; fcs nbiter=50 reg(BASE = CSIRSTR); fcs nbiter=50 reg(W2 = CSIRSTR BASE); /*[MESURE]='DD'*/ MNAR model(W2/ modelobs=(TRT01PN = '3')); var CSIRSTR BASE W2; run;   ... View more

I suggest that you combine the LsMeans and standard errors from MIXED in MIANALYZE.  You could then save the p-values from the combined estimates to a SAS data set and feed them into MULTTEST.  You would have to use a different adjustment method than Tukey like Bonferroni since MULTTEST will not compute Tukey p-values from a data set of raw p-values. ... View more

The standard errors that I was referring to were associated with the point estimates themselves, not the combined estimates.  In other words, do you have a formula that reflects the standard errors that you will eventually average in the first half of Rubin's methodology? ... View more

In order to combine any statistics from multiply imputed data you need the point estimate and a standard error.  Do you know how the standard errors for standardized mean differences and synthetic Ns would be calculated? ... View more

PROC SURVEYREG uses elementwise regression to compute the regression coefficient estimators by generalized least squares estimation. The procedure assumes that the regression coefficients are the same across strata and primary sampling units (PSUs). To estimate the variance-covariance matrix for the regression coefficients, PROC SURVEYREG uses either the Taylor series (linearization) method or replication (like BRR) methods to estimate sampling errors of estimators, based on complex sample designs. ... View more

Proc MI does not have a specific option for dealing with count (poisson) data.  To circumvent this you could use the REGPMM method on a MONOTONE statement.  This will lead to only integer values being selected as imputed values. As far as the value of K= that you should select, there is not really an ideal value, but instead there is a tradeoff of sorts.  A smaller K= value tends to increase the correlation among the multiple imputations for the missing observation and results in a higher variability of point estimators in repeated sampling. On the other hand, a larger K= value tends to lessen the effect from the imputation model and results in biased estimators.  There is a good discussion in  Schenker and Taylor (1996 ) on  p. 430. ... View more

The theory behind combining the multiply imputed estimates requires that there be a point estimate and standard error.  Since the Supremum test only reports the maximum and a p-value, there is no easy way to get combined estimates. ... View more

Unfortunately there is not an option within Proc MI to subset the imputation models based on a group variable that also has missing values.  There isn't necessarily a good way to handle your situation based on this and I do not know of any references that discuss how it should be approached. ... View more

You could also use the NAMELEN= option on the PHREG statement to keep it from truncating the names of the effects. ... View more