The GLIMMIX Procedure

Example 41.3 Smoothing Disease Rates; Standardized Mortality Ratios

Clayton and Kaldor (1987, Table 1) present data on observed and expected cases of lip cancer in the 56 counties of Scotland between 1975 and 1980. The expected number of cases was determined by a separate multiplicative model that accounted for the age distribution in the counties. The goal of the analysis is to estimate the county-specific log-relative risks, also known as standardized mortality ratios (SMR).

If $Y_ i$ is the number of incident cases in county i and $E_ i$ is the expected number of incident cases, then the ratio of observed to expected counts, $Y_ i/E_ i$, is the standardized mortality ratio. Clayton and Kaldor (1987) assume there exists a relative risk $\lambda _ i$ that is specific to each county and is a random variable. Conditional on $\lambda _ i$, the observed counts are independent Poisson variables with mean $E_ i\lambda _ i$.

An elementary mixed model for $\lambda _ i$ specifies only a random intercept for each county, in addition to a fixed intercept. Breslow and Clayton (1993), in their analysis of these data, also provide a covariate that measures the percentage of employees in agriculture, fishing, and forestry. The expanded model for the region-specific relative risk in Breslow and Clayton (1993) is

\[  \lambda _ i = \exp \left\{  \beta _0 + \beta _1 x_ i/10 + \gamma _ i \right\}  , \quad i=1,\cdots , 56  \]

where $\beta _0$ and $\beta _1$ are fixed effects, and the $\gamma _ i$ are county random effects.

The following DATA step creates the data set lipcancer. The expected number of cases is based on the observed standardized mortality ratio for counties with lip cancer cases, and based on the expected counts reported by Clayton and Kaldor (1987, Table 1) for the counties without cases. The sum of the expected counts then equals the sum of the observed counts.

data lipcancer;
   input county observed expected employment SMR;
   if (observed > 0) then expCount = 100*observed/SMR;
   else expCount = expected;
   datalines;
 1  9  1.4 16 652.2
 2 39  8.7 16 450.3
 3 11  3.0 10 361.8
 4  9  2.5 24 355.7
 5 15  4.3 10 352.1
 6  8  2.4 24 333.3
 7 26  8.1 10 320.6
 8  7  2.3  7 304.3
 9  6  2.0  7 303.0
10 20  6.6 16 301.7
11 13  4.4  7 295.5
12  5  1.8 16 279.3
13  3  1.1 10 277.8
14  8  3.3 24 241.7
15 17  7.8  7 216.8
16  9  4.6 16 197.8
17  2  1.1 10 186.9
18  7  4.2  7 167.5
19  9  5.5  7 162.7
20  7  4.4 10 157.7
21 16 10.5  7 153.0
22 31 22.7 16 136.7
23 11  8.8 10 125.4
24  7  5.6  7 124.6
25 19 15.5  1 122.8
26 15 12.5  1 120.1
27  7  6.0  7 115.9
28 10  9.0  7 111.6
29 16 14.4 10 111.3
30 11 10.2 10 107.8
31  5  4.8  7 105.3
32  3  2.9 24 104.2
33  7  7.0 10  99.6
34  8  8.5  7  93.8
35 11 12.3  7  89.3
36  9 10.1  0  89.1
37 11 12.7 10  86.8
38  8  9.4  1  85.6
39  6  7.2 16  83.3
40  4  5.3  0  75.9
41 10 18.8  1  53.3
42  8 15.8 16  50.7
43  2  4.3 16  46.3
44  6 14.6  0  41.0
45 19 50.7  1  37.5
46  3  8.2  7  36.6
47  2  5.6  1  35.8
48  3  9.3  1  32.1
49 28 88.7  0  31.6
50  6 19.6  1  30.6
51  1  3.4  1  29.1
52  1  3.6  0  27.6
53  1  5.7  1  17.4
54  1  7.0  1  14.2
55  0  4.2 16   0.0
56  0  1.8 10   0.0
;

Because the mean of the Poisson variates, conditional on the random effects, is $\mu _ i = E_ i \lambda _ i$, applying a log link yields

\[  \log \{ \mu _ i\}  = \log \{ E_ i\}  + \beta _0 + \beta _1 x_ i/10 + \gamma _ i  \]

The term $\log \{ E_ i\} $ is an offset, a regressor variable whose coefficient is known to be one. Note that it is assumed that the $E_ i$ are known; they are not treated as random variables.

The following statements fit this model by residual pseudo-likelihood:

proc glimmix data=lipcancer;
   class county;
   x    = employment / 10;
   logn = log(expCount);
   model observed = x / dist=poisson offset=logn
                        solution ddfm=none;
   random county;
   SMR_pred = 100*exp(_zgamma_ + _xbeta_);
   id employment SMR SMR_pred;
   output out=glimmixout;
run;

The offset is created with the assignment statement

logn = log(expCount);

and is associated with the linear predictor through the OFFSET= option in the MODEL statement. The statement

x = employment / 10;

transforms the covariate measuring percentage of employment in agriculture, fisheries, and forestry to agree with the analysis of Breslow and Clayton (1993). The DDFM=NONE option in the MODEL statement requests chi-square tests and z tests instead of the default F tests and t tests by setting the denominator degrees of freedom in tests of fixed effects to $\infty $.

The statement

SMR_pred = 100*exp(_zgamma_ + _xbeta_);

calculates the fitted standardized mortality rate. Note that the offset variable does not contribute to the exponentiated term.

The OUTPUT statement saves results of the calculations to the output data set glimmixout. The ID statement specifies that only the listed variables are written to the output data set.

Output 41.3.1: Model Information in Poisson GLMM

The GLIMMIX Procedure

Model Information
Data Set WORK.LIPCANCER
Response Variable observed
Response Distribution Poisson
Link Function Log
Variance Function Default
Offset Variable logn = log(expCount);
Variance Matrix Not blocked
Estimation Technique Residual PL
Degrees of Freedom Method None

Class Level Information
Class Levels Values
county 56 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56

Number of Observations Read 56
Number of Observations Used 56

Dimensions
G-side Cov. Parameters 1
Columns in X 2
Columns in Z 56
Subjects (Blocks in V) 1
Max Obs per Subject 56


The GLIMMIX procedure displays in the Model Information table that the offset variable was computed with programming statements and the final assignment statement from your GLIMMIX statements (Output 41.3.1). There are two columns in the $\bX $ matrix, corresponding to the intercept and the regressor $x/10$. There are 56 columns in the $\bZ $ matrix, however, one for each observation in the data set (Output 41.3.1).

The optimization involves only a single covariance parameter, the variance of the county effect (Output 41.3.2). Because this parameter is a variance, the GLIMMIX procedure imposes a lower boundary constraint; the solution for the variance is bounded by zero from below.

Output 41.3.2: Optimization Information in Poisson GLMM

Optimization Information
Optimization Technique Dual Quasi-Newton
Parameters in Optimization 1
Lower Boundaries 1
Upper Boundaries 0
Fixed Effects Profiled
Starting From Data


Following the initial creation of pseudo-data and the fit of a linear mixed model, the procedure goes through five more updates of the pseudo-data, each associated with a separate optimization (Output 41.3.3). Although the objective function in each optimization is the negative of twice the restricted maximum likelihood for that pseudo-data, there is no guarantee that across the outer iterations the objective function decreases in subsequent optimizations. In this example, minus twice the residual maximum likelihood at convergence takes on its smallest value at the initial optimization and increases in subsequent optimizations.

Output 41.3.3: Iteration History in Poisson GLMM

Iteration History
Iteration Restarts Subiterations Objective
Function
Change Max
Gradient
0 0 4 123.64113992 0.20997891 3.848E-8
1 0 3 127.05866018 0.03393332 0.000048
2 0 2 127.48839749 0.00223427 5.753E-6
3 0 1 127.50502469 0.00006946 1.938E-7
4 0 1 127.50528068 0.00000118 1.09E-7
5 0 0 127.50528481 0.00000000 1.299E-6

Convergence criterion (PCONV=1.11022E-8) satisfied.


The Covariance Parameter Estimates table in Output 41.3.4 shows the estimate of the variance of the region-specific log-relative risks. There is significant county-to-county heterogeneity in risks. If the covariate were removed from the analysis, as in Clayton and Kaldor (1987), the heterogeneity in county-specific risks would increase. (The fitted SMRs in Table 6 of Breslow and Clayton (1993) were obtained without the covariate x in the model.)

Output 41.3.4: Estimated Covariance Parameters in Poisson GLMM

Covariance Parameter Estimates
Cov Parm Estimate Standard Error
county 0.3567 0.09869


The Solutions for Fixed Effects table displays the estimates of $\beta _0$ and $\beta _1$ along with their standard errors and test statistics (Output 41.3.5). Because of the DDFM=NONE option in the MODEL statement, PROC GLIMMIX assumes that the degrees of freedom for the t tests of $H_0\colon \beta _ j = 0$ are infinite. The p-values correspond to probabilities under a standard normal distribution. The covariate measuring employment percentages in agriculture, fisheries, and forestry is significant. This covariate might be a surrogate for the exposure to sunlight, an important risk factor for lip cancer.

Output 41.3.5: Fixed-Effects Parameter Estimates in Poisson GLMM

Solutions for Fixed Effects
Effect Estimate Standard Error DF t Value Pr > |t|
Intercept -0.4406 0.1572 Infty -2.80 0.0051
x 0.6799 0.1409 Infty 4.82 <.0001


You can examine the quality of the fit of this model with various residual plots. A panel of studentized residuals is requested with the following statements:

ods graphics on;
ods select StudentPanel;

proc glimmix data=lipcancer plots=studentpanel;
   class county;
   x    = employment / 10;
   logn = log(expCount);
   model observed = x / dist=poisson offset=logn s ddfm=none;
   random county;
run;

ods graphics off;

The graph in the upper-left corner of the panel displays studentized residuals plotted against the linear predictor (Output 41.3.6). The default of the GLIMMIX procedure is to use the estimated BLUPs in the construction of the residuals and to present them on the linear scale, which in this case is the logarithmic scale. You can change the type of the computed residual with the TYPE= suboptions of each paneled display. For example, the option PLOTS=STUDENTPANEL(TYPE=NOBLUP) would request a paneled display of the marginal residuals on the linear scale.

Output 41.3.6: Panel of Studentized Residuals

 Panel of Studentized Residuals


The graph in the upper-right corner of the panel shows a histogram with overlaid normal density. A Q-Q plot and a box plot are shown in the lower cells of the panel.

The following statements produce a graph of the observed and predicted standardized mortality ratios (Output 41.3.7):

proc template;
   define statgraph scatter;
      BeginGraph;
         layout overlayequated / yaxisopts=(label='Predicted SMR')
                                 xaxisopts=(label='Observed SMR')
                                 equatetype=square;
            lineparm y=0 slope=1 x=0 /
                   lineattrs = GraphFit(pattern=dash)
                   extend    = true;
            scatterplot y=SMR_pred x=SMR /
                    markercharacter = employment;
         endlayout;
      EndGraph;
   end;
run;
proc sgrender data=glimmixout template=scatter;
run;

In Output 41.3.7, fitted SMRs tend to be larger than the observed SMRs for counties with small observed SMR and smaller than the observed SMRs for counties with high observed SMR.

Output 41.3.7: Observed and Predicted SMRs; Data Labels Indicate Covariate Values

 Observed and Predicted SMRs; Data Labels Indicate Covariate Values


To demonstrate the impact of the random effects adjustment to the log-relative risks, the following statements fit a Poisson regression model (a GLM) by maximum likelihood:

proc glimmix data=lipcancer;
   x    = employment / 10;
   logn = log(expCount);
   model observed = x / dist=poisson offset=logn
                        solution ddfm=none;
   SMR_pred = 100*exp(_zgamma_ + _xbeta_);
   id employment SMR SMR_pred;
   output out=glimmixout;
run;

The GLIMMIX procedure defaults to maximum likelihood estimation because these statements fit a generalized linear model with nonnormal distribution. As a consequence, the SMRs are county specific only to the extent that the risks vary with the value of the covariate. But risks are no longer adjusted based on county-to-county heterogeneity in the observed incidence count.

Because of the absence of random effects, the GLIMMIX procedure recognizes the model as a generalized linear model and fits it by maximum likelihood (Output 41.3.8). The variance matrix is diagonal because the observations are uncorrelated.

Output 41.3.8: Model Information in Poisson GLM

The GLIMMIX Procedure

Model Information
Data Set WORK.LIPCANCER
Response Variable observed
Response Distribution Poisson
Link Function Log
Variance Function Default
Offset Variable logn = log(expCount);
Variance Matrix Diagonal
Estimation Technique Maximum Likelihood
Degrees of Freedom Method None


The Dimensions table shows that there are no G-side random effects in this model and no R-side scale parameter either (Output 41.3.9).

Output 41.3.9: Model Dimensions Information in Poisson GLM

Dimensions
Columns in X 2
Columns in Z 0
Subjects (Blocks in V) 1
Max Obs per Subject 56


Because this is a GLM, the GLIMMIX procedure defaults to the Newton-Raphson algorithm, and the fixed effects (intercept and slope) comprise the parameters in the optimization (Output 41.3.10). (The default optimization technique for a GLM is the Newton-Raphson method.)

Output 41.3.10: Optimization Information in Poisson GLM

Optimization Information
Optimization Technique Newton-Raphson
Parameters in Optimization 2
Lower Boundaries 0
Upper Boundaries 0
Fixed Effects Not Profiled


The estimates of $\beta _0$ and $\beta _1$ have changed from the previous analysis. In the GLMM, the estimates were $\widehat{\beta }_0 = -0.4406$ and $\widehat{\beta }_1 = 0.6799$ (Output 41.3.11).

Output 41.3.11: Parameter Estimates in Poisson GLM

Parameter Estimates
Effect Estimate Standard Error DF t Value Pr > |t|
Intercept -0.5419 0.06951 Infty -7.80 <.0001
x 0.7374 0.05954 Infty 12.38 <.0001


More importantly, without the county-specific adjustments through the best linear unbiased predictors of the random effects, the predicted SMRs are the same for all counties with the same percentage of employees in agriculture, fisheries, and forestry (Output 41.3.12).

Output 41.3.12: Observed and Predicted SMRs in Poisson GLM

 Observed and Predicted SMRs in Poisson GLM