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- Re: Overal effect test for Categorical variable in PROC MIANALYZE

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Posted 05-24-2020 02:52 PM
(1492 views)

I did multiple imputations by using FCS. Now I have 5 imputed data. In my logistic regression, I have a categorical variable "systolic blood pressure" with 4 levels (0, 1, 2, 3) so I want to check the "Type 3 analysis effect" in PROC MIANALYZE to study the overall effect of systolic blood pressure.

By using PROC MIANALYZE I only get the effect for each level, but not a global one.

I have found someone used the TEST statement like **test systolic_0, systolic_1, systolic_2/multi; But I am not sure whether that is the same as the "type 3 analysis of effect"?**

Thank you for your help!

8 REPLIES 8

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If you make systolic blood pressure to be categorical, then that's what SAS does (in every modeling procedure, not just MIANALYZE), it provides an estimate for each level.

If you want the "global effect", if I understand you properly, then the variable must not be categorical.

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Paige Miller

Paige Miller

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@dandixu wrote:

If I am understanding you properly, I don't think you can do this in PROC MIANALYZE. You could combine PROC MI with PROC GLM or PROC LOGISTIC to get a Type III analysis.

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Paige Miller

Paige Miller

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It would become a 3 step process. PROC MI to do the multiple imputation, PROC GLM (or other analysis procedure) with a BY _imputation_ statement and an OUTPUT statement for the parameter estimates and the X'X inverse matrix, and PROC MIANALYZE to tie all of this together. See

in the PROC MIANALYZE documentation.

SteveDenham

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Hello SAS_Rob,

Thank you for your help. I found that solution one month ago and I tried.

But it didn't work for one of my variables. It didn't give a p-value. And I

am also not clear that what is the difference between this F-test for

global significance and the Wald chi-square test used in "type three

analysis of effect".

I came up with a solution to calculate the combined wald test. After I get

the pooled estimation for each level. I used the equation to calculate the

chi-square: transposed estimation matrix*inverse matrix of covariance

matrix* estimation matrix.

Thank you for your help. I found that solution one month ago and I tried.

But it didn't work for one of my variables. It didn't give a p-value. And I

am also not clear that what is the difference between this F-test for

global significance and the Wald chi-square test used in "type three

analysis of effect".

I came up with a solution to calculate the combined wald test. After I get

the pooled estimation for each level. I used the equation to calculate the

chi-square: transposed estimation matrix*inverse matrix of covariance

matrix* estimation matrix.

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- Bookmark
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