Greetings,
I am using SAS proc surveylogistic to estimate four nested models. I've
presented simplified versions of each model below
Model1 Model2 Model3 Model4
Predictor
Age x x x x
Gender x x x x
WorkingStatus x x x
Region x x x
HealthVisits x x
RXuse x x
Disease1 x
Disease2 x
I would like to test the joint effect of adding each additional set of
variables to the predictors entered in previous models. I would normally
calculate the Likelihood Ratio (LR) test by multiplying -2 by the
difference in the log transformed likelihoods as shown below:
LR = -2*(lnL1-lnL2)
Where ln is the log transformation, L1 is likelihood for Model1 and L2
is likelihood for Model 2 with the LR value distributed as Chi-Square
and having degrees of freedom equal to the difference in number of
predictors between the two models.
However, my question arises from using multiple imputation (proc mi) to
impute missing values in 10 different imputed datasets and then using
proc mianalyze to combine the results from these ten datasets and obtain
the correct test statistics. I'm not sure how to deal with the LR test
since there are 10 different values for the log-likelihoods for each
model. One simple strategy would be to average the log-likelihoods
across the 10 models and use the averaged results. However, I can't find
any literature that supports this approach. I've included below the
basic code that I'm using to run one of the models.
**----------------------------------------------------------------------
-------**;
**-- Create Multiple Imputations Model with all predictors--**;
**----------------------------------------------------------------------
-------**;
proc mi data=nhis.nhis_aa_recode3 seed=21355417 nimpute=10 out
= nhis_aa_recode3_imp;
mcmc chain=multiple displayinit initial=em(itprint);
var Age Gender WorkingStatus Region HealthVisits RXuse
Disease1 Disease2 ;
run;
ods output close;
**----------------------------------------------------------------------
-------**;
**-- Run Model 1 predictors--**;
**----------------------------------------------------------------------
-------**;
proc surveylogistic data=nhis_aa_recode3_imp ;
cluster h_psu;
strata h_stratum;
WEIGHT h_WTFA_SA;
model dependent(descending) = Age Gender / COVB expb;
by _imputation_;
ods output Parameterestimates=gmparms1 COVB=COVMAT1;
title3 'Survey logistic results for Model 1';
run;
**----------------------------------------------------------------------
-------**;
**-- Combine Results for Model 1 predictors--**;
**----------------------------------------------------------------------
-------**;
proc mianalyze parms=gmparms1 COVB=COVMAT1 mult;
modeleffects Age Gender ;
title3 'Proc MIanalyze results for Model 1';
run;
Any feedback on how to accomplish this test would be greatly appreciate!
Any examples showing how to do so in SAS code would be doubly
appreciated!!
With best regards,
Tom
Tom Bohman, Ph.D.
Research Scientist
Addiction Research Institute/GCATTC
Center for Social Work Research
University of Texas at Austin
1 University Station R5000
Austin, TX 78712
(512) 232-0605
[email protected]
-------------- next part --------------
An HTML attachment was scrubbed...
URL:
http://lists.utsouthwestern.edu/pipermail/impute/attachments/20070329/5e94c3ac/attachment-0001.htm
From newgardc <@t> ohsu.edu Thu Mar 29 19:39:31 2007
From: newgardc <@t> ohsu.edu (Craig Newgard)
Date: Thu Mar 29 19:40:07 2007
Subject: [Impute] How to calculate likelihood ratio test for
multiply-imputed data using proc surveylogis
Message-ID: <[email protected]>
Tom,
I have struggled with a similar issue recently. It appears that you're working
with complex survey data (clusters, strata, weights), which adds complexity
both to the MI model and analysis. For the MI model, you should include
features important to the sampling design (ie, clusters and strata), in order
to minimize bias in the MI results (Raghunathan has a paper on this).
For the analysis, the LR test can't be used with complex survey data. Instead,
the Wald statistic can be used. I'd suggest running your MI model, then
analyze each of the MI models, saving the covariance matrices and point
estimates (I have used Stata for this part, as it was easier than in SAS),
which can then be used to compute the joint significance of multiple terms
(NORM has a fairly easy mechanism for employing this). One other option is to
use the "test" command in Stata for each model (whichever joint terms you'd
like to test) - this will give you some idea as to joint significance, though
Stata micombine is not yet able to handle multiply imputed complex survey data.
Hope this helps.
Craig
Craig D. Newgard, MD, MPH
Assistant Professor
Department of Emergency Medicine
Department of Public Health and Preventive Medicine
Center for Policy and Research in Emergency Medicine
Oregon Health & Science University
3181 SW Sam Jackson Park Road
Mail Code CR-114
Portland, Oregon 97239-3098
phone (503) 494-1668
fax (503) 494-4640
[email protected]
------------------------------------------------------------------------------------------------------------
Confidential: In accordance with ORS 41.675.
The information contained in this EMAIL message is confidential and protected
by law. The information is intended only for the person or business identified
in the document. If you are not the intended recipient, a sharing, printing,
storing or copying of the information will result in a violation of the law. If
you have received this EMAIL by mistake, please notify the sender of this EMAIL
and copy the Office of Information Privacy and Security at [email protected] .
>>> "Bohman, Thomas M" <[email protected]> 03/29/07 10:08 AM >>>
Greetings,
I am using SAS proc surveylogistic to estimate four nested models. I've
presented simplified versions of each model below
Model1 Model2 Model3 Model4
Predictor
Age x x x x
Gender x x x x
WorkingStatus x x x
Region x x x
HealthVisits x x
RXuse x x
Disease1 x
Disease2 x
I would like to test the joint effect of adding each additional set of
variables to the predictors entered in previous models. I would normally
calculate the Likelihood Ratio (LR) test by multiplying -2 by the
difference in the log transformed likelihoods as shown below:
LR = -2*(lnL1-lnL2)
Where ln is the log transformation, L1 is likelihood for Model1 and L2
is likelihood for Model 2 with the LR value distributed as Chi-Square
and having degrees of freedom equal to the difference in number of
predictors between the two models.
However, my question arises from using multiple imputation (proc mi) to
impute missing values in 10 different imputed datasets and then using
proc mianalyze to combine the results from these ten datasets and obtain
the correct test statistics. I'm not sure how to deal with the LR test
since there are 10 different values for the log-likelihoods for each
model. One simple strategy would be to average the log-likelihoods
across the 10 models and use the averaged results. However, I can't find
any literature that supports this approach. I've included below the
basic code that I'm using to run one of the models.
**----------------------------------------------------------------------
-------**;
**-- Create Multiple Imputations Model with all predictors--**;
**----------------------------------------------------------------------
-------**;
proc mi data=nhis.nhis_aa_recode3 seed=21355417 nimpute=10 out
= nhis_aa_recode3_imp;
mcmc chain=multiple displayinit initial=em(itprint);
var Age Gender WorkingStatus Region HealthVisits RXuse
Disease1 Disease2 ;
run;
ods output close;
**----------------------------------------------------------------------
-------**;
**-- Run Model 1 predictors--**;
**----------------------------------------------------------------------
-------**;
proc surveylogistic data=nhis_aa_recode3_imp ;
cluster h_psu;
strata h_stratum;
WEIGHT h_WTFA_SA;
model dependent(descending) = Age Gender / COVB expb;
by _imputation_;
ods output Parameterestimates=gmparms1 COVB=COVMAT1;
title3 'Survey logistic results for Model 1';
run;
**----------------------------------------------------------------------
-------**;
**-- Combine Results for Model 1 predictors--**;
**----------------------------------------------------------------------
-------**;
proc mianalyze parms=gmparms1 COVB=COVMAT1 mult;
modeleffects Age Gender ;
title3 'Proc MIanalyze results for Model 1';
run;
Any feedback on how to accomplish this test would be greatly appreciate!
Any examples showing how to do so in SAS code would be doubly
appreciated!!
With best regards,
Tom
Tom Bohman, Ph.D.
Research Scientist
Addiction Research Institute/GCATTC
Center for Social Work Research
University of Texas at Austin
1 University Station R5000
Austin, TX 78712
(512) 232-0605
[email protected]
