The GLIMMIX Procedure

Analysis as Normal Data with Averaged Endpoints

In order to apply the extended TCTP procedure of Westfall and Tobias (2007) to the problem of comparing response surfaces in the clinical trial, the following convenience macro is helpful to generate the comparisons for the ESTIMATE statement in PROC GLIMMIX:

%macro Contrast(from,to,byA,byT);
   %let nCmp = 0;
   %do age = &from %to &to %by &byA;
      %do t0  =  0 %to  4 %by &byT;
         %let nCmp = %eval(&nCmp+1);
      %end;
   %end;
   %let iCmp = 0;
   %do age = &from %to &to %by &byA;
      %do t0  =  0 %to  4 %by &byT;
        %let iCmp = %eval(&iCmp+1);
        "%trim(%left(&age)) %trim(%left(&t0))"
          drug     1    -1
          drug*age &age -&age
          drug*t0  &t0  -&t0   
        %if (&icmp < &nCmp) %then %do; , %end;
      %end;
   %end;
%mend;

The following GLIMMIX statements fit the model to the data and compute the 105 contrasts that compare the placebo to the active response at 105 points in the two-dimensional regressor space:

proc glimmix data=clinical;
   t = (t3+t4)/2;
   class drug;
   model t = drug t0 age drug*age drug*t0;
   estimate %contrast(10,70,3,1)
              / adjust=simulate(seed=1)
                stepdown(type=logical);
   ods output Estimates=EstStepDown;
run;

Note that only a single ESTIMATE statement is used. Each of the 105 comparisons is one comparison in the multirow statement. The ADJUST option in the ESTIMATE statement requests multiplicity-adjusted p-values. The extended TCTP method is applied by specifying the STEPDOWN(TYPE=LOGICAL) option to compute step-down-adjusted p-values where logical constraints among the hypotheses are taken into account. The results from the ESTIMATE statement are saved to a data set for subsequent processing. Note also that the response, the average of the ratings at the final two visits, is computed with programming statements in PROC GLIMMIX.

The following statements print the 20 most significant estimated differences (Output 40.13.1):

proc sort data=EstStepDown;
   by Probt;
proc print data=EstStepDown(obs=20);
   var Label Estimate StdErr Probt AdjP;
run;

Notice that the adjusted p-values (Adjp) are larger than the unadjusted p-values, as expected. Also notice that several comparisons share the same adjusted p-values. This is a result of the monotonicity of the extended TCTP method.

In order to compare the step-down-adjusted p-values to adjusted p-values that do not use step-down methods, replace the ESTIMATE statement in the previous statements with the following:

   estimate %contrast2(10,70,3,1) / adjust=simulate(seed=1);
   ods output Estimates=EstAdjust;

The following GLIMMIX invocations create output data sets named EstAdjust and EstUnAdjust that contain (non-step-down-) adjusted and unadjusted p-values:

proc glimmix data=clinical;
   t = (t3+t4)/2;
   class drug;
   model t = drug t0 age drug*age drug*t0;
   estimate %contrast(10,70,3,1)
              / adjust=simulate(seed=1);
   ods output Estimates=EstAdjust;
run;
proc glimmix data=clinical;
   t = (t3+t4)/2;
   class drug;
   model t = drug t0 age drug*age drug*t0;
   estimate %contrast(10,70,3,1);
   ods output Estimates=EstUnAdjust;
run;

Output 40.13.2 shows a comparison of the significant comparisons (p < 0.05) based on unadjusted, adjusted, and step-down (TCTP) adjusted p-values. Clearly, the unadjusted results indicate the most significant results, but without protecting the Type I error rate for the group of tests. The adjusted p-values (filled circles) lead to a much smaller region in which the response surfaces between treatment and placebo are significantly different. The increased power of the TCTP procedure (open circles) over the standard multiplicity adjustment—without sacrificing Type I error protection—can be seen in the considerably larger region covered by the open circles.

Output 40.13.2 Comparison of Significance Regions

Ordinal Repeated Measure Analysis

The outcome variable in this clinical trial is an ordinal rating of patients in categories 0=terrible, 1=poor, 2=fair, 3=good, and 4=excellent. Furthermore, the observations from repeat visits for the same patients are likely correlated. The previous analysis removes the repeated measures aspect by defining efficacy as the average score at the final two visits. These averages are not normally distributed, however. The response surfaces for the two study arms can also be compared based on a model for ordinal data that takes correlation into account through random effects. Keeping with the theme of the previous analysis, the focus here for illustrative purposes is on the final two visits, and the pretreatment assessment score serves as a covariate in the model.

The following DATA step rearranges the data from the third and fourth on-treatment visits in univariate form with separate observations for the visits by patient:

data clinical_uv;
   set clinical;
   array time{2} t3-t4;
   do i=1 to 2; rating = time{i}; output; end;
run;

The basic model for the analysis is a proportional odds model with cumulative logit link (McCullagh 1980) and categories. In this model, separate intercepts (cutoffs) are modeled for the first cumulative categories and the intercepts are monotonically increasing. This guarantees ordering of the cumulative probabilities and nonnegative category probabilities. Using the same covariate structure as in the previous analysis, the probability to observe a rating in at most category is

Because only the intercepts are dependent on the category, contrasts comparing regression coefficients can be formulated in standard fashion. To accommodate the random and covariance structure of the repeated measures model, a random intercept is applied to the observations for each patient:

The shared random effect of the two observations creates a marginal correlation. Note that the random effects do not depend on category.

The following GLIMMIX statements fit this ordinal repeated measures model by maximum likelihood via the Laplace approximation and compute TCTP-adjusted p-values for the 105 estimates:

proc glimmix data=clinical_uv method=laplace;
   class center id drug;
   model rating = drug t0 age drug*age drug*t0 /
                  dist=multinomial link=cumlogit;
   random intercept / subject=id(center);
   covtest 0;
   estimate %contrast(10,70,3,1)
              / adjust=simulate(seed=1)
                stepdown(type=logical);
  ods output Estimates=EstStepDownMulti;
run;

The combination of DIST=MULTINOMIAL and LINK=CUMLOGIT requests the proportional odds model. The SUBJECT= effect nests patient IDs within centers, because patient IDs in the data set clinical are not unique within centers. (Specifying SUBJECT=ID*CENTER would have the same effect.) The COVTEST statement requests a likelihood ratio test for the significance of the random patient effect.

The estimate of the variance component for the random patient effect is substantial (Output 40.13.3), but so is its standard error.

The likelihood ratio test provides a better picture of the significance of the variance component. The difference in the log likelihoods is 57.6, highly significant even if one does not apply the Self and Liang (1987) correction that halves the p-value in this instance.

The results for the 20 most significant estimates are requested with the following statements and shown in Output 40.13.4:

proc sort data=EstStepDownMulti;
   by Probt;
proc print data=EstStepDownMulti(obs=20);
   var Label Estimate StdErr Probt AdjP;
run;

The p-values again show the "repeat" pattern corresponding to the monotonicity of the step-down procedure.

As previously, the comparisons were also performed with standard p-value adjustment via simulation. Output 40.13.5 displays the components of the regressor space in which the response surfaces differ significantly () between the two treatment arms. As before, the most significant differences occur with unadjusted p-values at the cost of protecting only the individual Type I error rate. The standard multiplicity adjustment has considerably less power than the TCTP adjustment.

Output 40.13.5 Comparison of Significance Regions, Ordinal Analysis