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Proc glimmix for logistic regression has an lsmeans option

Thanks J for this info:

Use dist=binary and link=logit for logistic regressino using PROG GLIMMIX.

The lSMEANS statemetn is available from this and it produces probability scores. Use ilink option:

lsmeans &group / ilink ;

For difference in the groups, you use the DIFF option on the LSMEANS statement. The results are also on the logit scale. If you use the OR option, you will get the odds ratios for the group effect --

lsmeans &group /diff or;

Unfortunately, the difference in the probability scale between the groups are not directly available in PROC GLIMMIX. The magnitude of the difference is easy to compute -- you use the results from the ILINK option output, which gives you the estimated probabilities in each group, and compute the difference by hand or by using a data step, however, the appropriate standard errors for these differences are not available in PROC GLIMMIX.

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SAS function, RANUNI creates a flat distribution

randomx=ranuni(1);

The MEANS Procedure

Analysis Variable : randomx

N Mean Std Dev Minimum Maximum
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
3812 0.4995942 0.2896404 0.000147896 0.9997638
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ

The SAS System 19:50 Wednesday, May 29, 2013 3

The UNIVARIATE Procedure

Histogram # Boxplot
0.975+***************************************** 201 |
.*************************************** 193 |
.************************************ 178 |
.*********************************** 173 |
.**************************************** 197 |
.************************************** 186 +-----+
.**************************************** 197 | |
.******************************************** 218 | |
.************************************* 182 | |
.************************************** 188 *-----*
.**************************************** 200 | + |
.************************************* 184 | |
.**************************************** 199 | |
.************************************ 177 | |
.*********************************** 174 | |
.************************************ 176 +-----+
.****************************************** 206 |
.************************************* 183 |
.**************************************** 199 |
0.025+***************************************** 201 |
----+----+----+----+----+----+----+----+----
* may represent up to 5 counts

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T-test for proportions of multiple groups using SAS procedures and datasteps

/*In this example, there is only two groups, but you can run it with multiple groups.*/

data kaz;set sashelp.class;
if age < 12 then THIS_IS_OUTCOME=0; if age > 13 then THIS_IS_OUTCOME=1;

run;

%let group=sex;
%let outcome=THIS_IS_OUTCOME;
%let dataname=kaz;

ods listing;
ods trace on;

proc means data=&dataname;
where &outcome ne .;
class &group;
var &outcome;
ods output summary=kaz_mean;
run;

proc glimmix data=&dataname;
class &group;
model &outcome=&group ;
lsmeans &group /diff ;
ods output Diffs=kaz_t;
run;

data kaz_t2;
set kaz_t;
keep &group _&group estimate;
run;
proc sort;
by &group;run;

data kaz_mean1;
set kaz_mean;
prop1=&outcome._mean;
n1=&outcome._n;
keep &group prop1 n1;
run;
proc sort;by &group;run;

data kaz_mean2;
set kaz_mean;
prop2=&outcome._mean;
n2=&outcome._n;
_&group=&group;
keep _&group prop2 n2;
run;
proc sort;by _&group;run;

data mix1;
merge kaz_t2 kaz_mean1;
by &group;
run;
proc sort;
by _&group;run;

data mix2;
merge mix1 kaz_mean2;
by _&group;
if estimate ne .;
DEG_FD=N1+N2-2;
/*QC’ed
tValue=2.228;
DEG_FD=10;
*/
tValue=(prop1-prop2)/(SQRT((prop1*(1-prop1)/n1 )+prop2*(1-prop2)/ n2));
/*2 tail test*/
P=(1-probt(abs(tValue),DEG_FD))*2;

length _2TAIL_STAT_TEST $ 2;

if P < .05 then _2TAIL_TEST = "*"; group1=&group; group2=_&group; classvar="&group"; dif=estimate; outcome="&outcome"; run; data mix3; retain outcome classvar group1 group2 n1 prop1 n2 prop2 dif p _2TAIL_TEST ; set mix2; keep outcome classvar group1 group2 n1 prop1 n2 prop2 dif p _2TAIL_TEST ; ; run;

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Appling t-test for comparison of proportion in a data step:

 

 

%let var1=female;

%let var2=male;

%let key=ENROLL_RATE;

Z=(&var1._&key-&var2._&key)/(SQRT((&var1._&key*(1-&var1._&key)/&var1._SAMPLE_N )+&var2._&key*(1-&var2._&key)/ &var2._SAMPLE_N ));

 

 

data &schoollevel.&var1;
retain group;
merge ueka1b ueka2b;
by group;

P1=M2_MET_pre_Mean;
P2=M2_MET_post1_Mean;

N1=M2_MET_pre_N;
N2=M2_MET_post1_N;

A=(P1*(1-P1))/N1;
B=(P2*(1-P2))/N2;
STDERR=sqrt(A+B);

Z=abs((P1-P2)/STDERR);
/*two tail 5%*/

P=(1-probnorm(Z))*2;

if P < 0.05 then SIG="*";

drop A B P1 P2 N1 N2;
run;

 

 

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Statistical Citation

Raudenbush, S.W. and Bryk, A.S. (2002). Hierarchical Linear Models (Second Edition). Thousand Oaks: Sage Publications.

Rosenbaum, Paul R.; Rubin, Donald B. (1983). "The central role of the propensity score in observational studies for causal effects".Biometrika 70 (1): 41–55.

Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Boston, MA: Houghton Mifflin

<http://sitemaker.umich.edu/group-based/optimal_design_software>
Software:

Raudenbush, S. W., et al. (2011). Optimal Design Software for Multi-level and Longitudinal Research (Version 3.01) [Software]. Available from www.wtgrantfoundation.org.

Documentation:

Spybrook, J., et al. (2011). Optimal Design for Longitudinal and Multilevel Research: Documentation for the Optimal Design Software Version 3.0. Available from www.wtgrantfoundation.org.

Schochet Z., Peter (2009). Do typical RCTs of Education Interventions Have Sufficient Statistical Power for Linking Impacts on Teacher Practice and Student Achievement Outcomes.  National Center for Education Evaluation and Regional Assistance.   http://ies.ed.gov/ncee/pdf/20094065.pdf

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How to explain HLM (response to an email)

Hello XXX, thanks for your email! I recently taught a premier for HLM, so I hope I can update my website with new material.

This time, I tried to say that HLM is simple, but equations make it difficult to learn (which is also the case for learning music instruments, like piano).

In the attached graph, I tried to compare my intuitive model (mental representation model) and a formal equation. When it is in my mind, coefficients are either random (red font) or fixed (blue). The red coefficients are adjusted for precision/reliability, while the blue ones are not (like OLS coefficients). I tried to show how the equation expresses the same idea, but I feel it quickly gets annoying.

HLM slide sample