|
|
|
||||
|
|
|||||
The data files can be downloaded from http://rem.ph.ucla.edu/~rob/mld/data.html .Table 12.1, page 367.
*Complete data;
proc mixed data = missingdata1 method = reml noinfo noclprint noitprint;
class idno;
model weight = week/ solution notest;
random intercept/ subject = idno type = un;
run;
The Mixed Procedure
Covariance Parameter Estimates
Cov Parm Subject Estimate
UN(1,1) idno 714.01
Residual 10.1683
Fit Statistics
-2 Res Log Likelihood 735.4
AIC (smaller is better) 739.4
AICC (smaller is better) 739.5
BIC (smaller is better) 741.4
Null Model Likelihood Ratio Test
DF Chi-Square Pr > ChiSq
1 384.48 <.0001
Solution for Fixed Effects
Standard
Effect Estimate Error DF t Value Pr > |t|
Intercept 196.46 5.9972 19 32.76 <.0001
week 0.1414 0.08522 99 1.66 0.1002
*MAR;
proc mixed data = missingdata2 method = reml noclprint noitprint noinfo;
class idno;
model weight = week/ solution notest;
random intercept/ subject = idno type =un;
run;
The Mixed Procedure
Covariance Parameter Estimates
Cov Parm Subject Estimate
UN(1,1) idno 700.56
Residual 7.9755
Fit Statistics
-2 Res Log Likelihood 560.5
AIC (smaller is better) 564.5
AICC (smaller is better) 564.6
BIC (smaller is better) 566.5
Null Model Likelihood Ratio Test
DF Chi-Square Pr > ChiSq
1 301.54 <.0001
Solution for Fixed Effects
Standard
Effect Estimate Error DF t Value Pr > |t|
Intercept 196.84 5.9377 19 33.15 <.0001
week -0.05619 0.09671 70 -0.58 0.5631
*MNAR;
proc mixed data = missingdata3 method = reml noclprint noitprint noinfo;
class idno;
model weight = week/ solution notest;
random intercept/ subject = idno type =un;
run;
The Mixed Procedure
Covariance Parameter Estimates
Cov Parm Subject Estimate
UN(1,1) idno 707.88
Residual 6.0963
Fit Statistics
-2 Res Log Likelihood 490.2
AIC (smaller is better) 494.2
AICC (smaller is better) 494.4
BIC (smaller is better) 496.2
Null Model Likelihood Ratio Test
DF Chi-Square Pr > ChiSq
1 280.88 <.0001
Solution for Fixed Effects
Standard
Effect Estimate Error DF t Value Pr > |t|
Intercept 196.29 5.9666 19 32.90 <.0001
week -0.2007 0.08905 60 -2.25 0.0279
Table 12.2, page 370.
proc sql;
create table schiz as
select *,
(max(week) ne 6) as drop_out,
sqrt(week) as sweek
from schizophrenia
group by id
order by drop_out, id;
quit;
*Pooled;
proc mixed data = schiz method = ml noitprint noclprint noinfo;
class id ;
model severity = sweek drug sweek*drug / solution notest;
random intercept sweek/ subject=id type = un;
run;
The Mixed Procedure
Covariance Parameter Estimates
Cov Parm Subject Estimate
UN(1,1) ID 0.3687
UN(2,1) ID 0.02085
UN(2,2) ID 0.2420
Residual 0.5778
Fit Statistics
-2 Log Likelihood 4649.0
AIC (smaller is better) 4665.0
AICC (smaller is better) 4665.1
BIC (smaller is better) 4697.6
Null Model Likelihood Ratio Test
DF Chi-Square Pr > ChiSq
3 542.43 <.0001
Solution for Fixed Effects
Standard
Effect Estimate Error DF t Value Pr > |t|
Intercept 5.3480 0.08790 435 60.84 <.0001
sweek -0.3361 0.06794 435 -4.95 <.0001
drug 0.04634 0.1011 729 0.46 0.6469
sweek*drug -0.6405 0.07752 729 -8.26 <.0001
*By dropout;
proc mixed data = schiz method = ml noitprint noclprint noinfo;
by drop_out;
class id ;
model severity = sweek drug sweek*drug / solution notest;
random intercept sweek/ subject=id type = un;
run;
drop_out=0
The Mixed Procedure
Covariance Parameter Estimates
Cov Parm Subject Estimate
UN(1,1) ID 0.3984
UN(2,1) ID -0.01110
UN(2,2) ID 0.2048
Residual 0.5600
Fit Statistics
-2 Log Likelihood 3782.1
AIC (smaller is better) 3798.1
AICC (smaller is better) 3798.2
BIC (smaller is better) 3828.6
Null Model Likelihood Ratio Test
DF Chi-Square Pr > ChiSq
3 468.38 <.0001
Solution for Fixed Effects
Standard
Effect Estimate Error DF t Value Pr > |t|
Intercept 5.2212 0.1091 333 47.84 <.0001
sweek -0.3934 0.07345 333 -5.36 <.0001
drug 0.2016 0.1226 655 1.64 0.1008
sweek*drug -0.5386 0.08255 655 -6.52 <.0001
drop_out=1
Covariance Parameter Estimates
Cov Parm Subject Estimate
UN(1,1) ID 0.2858
UN(2,1) ID 0.02045
UN(2,2) ID 0.6921
Residual 0.5147
Fit Statistics
-2 Log Likelihood 822.9
AIC (smaller is better) 838.9
AICC (smaller is better) 839.5
BIC (smaller is better) 859.9
Null Model Likelihood Ratio Test
DF Chi-Square Pr > ChiSq
3 73.32 <.0001
Solution for Fixed Effects
Standard
Effect Estimate Error DF t Value Pr > |t|
Intercept 5.5337 0.1409 100 39.26 <.0001
sweek -0.1235 0.1773 100 -0.70 0.4877
drug -0.1939 0.1779 74 -1.09 0.2791
sweek*drug -1.1674 0.2239 74 -5.21 <.0001
*Covariate and contrasts;
proc mixed data = schiz method = ml noitprint noclprint noinfo;
class id ;
model severity = sweek drug sweek*drug drop_out drop_out*sweek
drop_out*drug drop_out*sweek*drug / solution notest;
random intercept sweek/ subject=id type = un;
estimate 'int placebo nodrop' intercept 1;
estimate 'int placebo drop' intercept 1 drop_out 1;
estimate 'int drug nodrop' intercept 1 drug 1;
estimate 'int drug drop' intercept 1 drug 1 drop_out 1;
estimate 'slope placebo nodrop' sweek 1;
estimate 'slope placebo drop' sweek 1 drop_out*sweek 1;
estimate 'slope drug nodrop' sweek 1 sweek*drug 1;
estimate 'slope drug drop' sweek 1 sweek*drug 1 drop_out*sweek 1 drop_out*sweek*drug 1;
run;
The Mixed Procedure
Covariance Parameter Estimates
Cov Parm Subject Estimate
UN(1,1) ID 0.3612
UN(2,1) ID 0.01175
UN(2,2) ID 0.2300
Residual 0.5768
Fit Statistics
-2 Log Likelihood 4623.3
AIC (smaller is better) 4647.3
AICC (smaller is better) 4647.5
BIC (smaller is better) 4696.2
Null Model Likelihood Ratio Test
DF Chi-Square Pr > ChiSq
3 523.49 <.0001
Solution for Fixed Effects
Standard
Effect Estimate Error DF t Value Pr > |t|
Intercept 5.2210 0.1075 433 48.55 <.0001
sweek -0.3934 0.07635 433 -5.15 <.0001
drug 0.2017 0.1208 729 1.67 0.0955
sweek*drug -0.5386 0.08580 729 -6.28 <.0001
drop_out 0.3203 0.1864 729 1.72 0.0862
sweek*drop_out 0.2517 0.1593 729 1.58 0.1146
drug*drop_out -0.3987 0.2270 729 -1.76 0.0794
sweek*drug*drop_out -0.6348 0.1960 729 -3.24 0.0013
Estimates
Standard
Label Estimate Error DF t Value Pr > |t|
int placebo nodrop 5.2210 0.1075 433 48.55 <.0001
int placebo drop 5.5413 0.1523 729 36.39 <.0001
int drug nodrop 5.4227 0.05511 729 98.39 <.0001
int drug drop 5.7430 0.1944 729 29.54 <.0001
slope placebo nodrop -0.3934 0.07635 433 -5.15 <.0001
slope placebo drop -0.1417 0.1399 729 -1.01 0.3115
slope drug nodrop -0.9320 0.03916 729 -23.80 <.0001
slope drug drop -1.3150 0.1073 729 -12.26 <.0001
Figure 12.2, page 371.
proc mixed data = schiz method = ml noitprint noclprint noinfo; class id ; model severity = sweek drug sweek*drug drop_out drop_out*sweek drop_out*drug drop_out*sweek*drug / solution notest outpredm=pred_schiz;; random intercept sweek/ subject=id type = un; run; proc sql; create table pred_schiz2 as select mean(pred) as pred, drug, drop_out, week from pred_schiz group by week, drop_out, drug order by drop_out, drug, week; quit; data pred_schiz2; set pred_schiz2; group=compress(drug||drop_out); if group = "01" then group = "placebo drop"; if group = "00" then group = "placebo nodrop"; if group = "11" then group = "drug drop"; if group = "10" then group = "drug nodrop"; run; goptions reset = all; symbol1 value=none interpol=join r=4; axis1 order =(0 to 6 by 1 ) label=(a=0 'Week') minor=none; axis2 order =(2 to 6 by 1) label = (a=90 'Illness severity') minor=none ; proc gplot data = pred_schiz2; plot pred*week=group /haxis=axis1 vaxis = axis2; run; quit;
UCLA Researchers are invited to our Statistical Consulting Services
We recommend others to our list of Other Resources for Statistical Computing Help
These pages are Copyrighted (c) by UCLA Academic Technology Services
