This example analyzes the data from Beitler and Landis (1985), which represent results from a multicenter clinical trial investigating the effectiveness of two topical cream treatments (active drug, control) in curing an infection. For each of eight clinics, the number of trials and favorable cures are recorded for each treatment. The SAS data set is as follows.
data infection; input clinic t x n; datalines; 1 1 11 36 1 0 10 37 2 1 16 20 2 0 22 32 3 1 14 19 3 0 7 19 4 1 2 16 4 0 1 17 5 1 6 17 5 0 0 12 6 1 1 11 6 0 0 10 7 1 1 5 7 0 1 9 8 1 4 6 8 0 6 7 ;
Suppose denotes the number of trials for the ith clinic and the jth treatment (), and denotes the corresponding number of favorable cures. Then a reasonable model for the preceding data is the following logistic model with random effects:

and

The notation indicates the jth treatment, and the are assumed to be iid .
The PROC NLMIXED statements to fit this model are as follows:
proc nlmixed data=infection; parms beta0=1 beta1=1 s2u=2; eta = beta0 + beta1*t + u; expeta = exp(eta); p = expeta/(1+expeta); model x ~ binomial(n,p); random u ~ normal(0,s2u) subject=clinic; predict eta out=eta; estimate '1/beta1' 1/beta1; run;
The PROC NLMIXED statement invokes the procedure, and the PARMS statement defines the parameters and their starting values. The next three statements define , and the MODEL statement defines the conditional distribution of to be binomial. The RANDOM statement defines u
to be the random effect with subjects defined by the clinic
variable.
The PREDICT statement constructs predictions for each observation in the input data set. For this example, predictions of and approximate standard errors of prediction are output to a data set named eta
. These predictions include empirical Bayes estimates of the random effects .
The ESTIMATE statement requests an estimate of the reciprocal of .
The output for this model is as follows.
Figure 64.7: Model Information and Dimensions for LogisticNormal Model
Specifications  

Data Set  WORK.INFECTION 
Dependent Variable  x 
Distribution for Dependent Variable  Binomial 
Random Effects  u 
Distribution for Random Effects  Normal 
Subject Variable  clinic 
Optimization Technique  Dual QuasiNewton 
Integration Method  Adaptive Gaussian Quadrature 
Dimensions  

Observations Used  16 
Observations Not Used  0 
Total Observations  16 
Subjects  8 
Max Obs Per Subject  2 
Parameters  3 
Quadrature Points  5 
The “Specifications” table provides basic information about the nonlinear mixed model (Figure 64.7). For example, the distribution of the response variable, conditional on normally distributed random effects, is binomial. The “Dimensions” table provides counts of various variables. You should check this table to make sure the data set and model have been entered properly. PROC NLMIXED selects five quadrature points to achieve the default accuracy in the likelihood calculations.
Figure 64.8: Starting Values of Parameter Estimates
Parameters  

beta0  beta1  s2u  NegLogLike 
1  1  2  37.5945925 
The “Parameters” table lists the starting point of the optimization and the negative log likelihood at the starting values (Figure 64.8).
Figure 64.9: Iteration History and Fit Statistics for LogisticNormal Model
Iteration History  

Iter  Calls  NegLogLike  Diff  MaxGrad  Slope  
1  2  37.3622692  0.232323  2.882077  19.3762  
2  3  37.1460375  0.216232  0.921926  0.82852  
3  5  37.0300936  0.115944  0.315897  0.59175  
4  6  37.0223017  0.007792  0.01906  0.01615  
5  7  37.0222472  0.000054  0.001743  0.00011  
6  9  37.0222466  6.57E7  0.000091  1.28E6  
7  11  37.0222466  5.38E10  2.078E6  1.1E9 
NOTE: GCONV convergence criterion satisfied. 
Fit Statistics  

2 Log Likelihood  74.0 
AIC (smaller is better)  80.0 
AICC (smaller is better)  82.0 
BIC (smaller is better)  80.3 
The “Iteration History” table indicates successful convergence in seven iterations (Figure 64.9). The “Fit Statistics” table lists some useful statistics based on the maximized value of the log likelihood.
Figure 64.10: Parameter Estimates for LogisticNormal Model
Parameter Estimates  

Parameter  Estimate  Standard Error  DF  t Value  Pr > t  Alpha  Lower  Upper  Gradient 
beta0  1.1974  0.5561  7  2.15  0.0683  0.05  2.5123  0.1175  3.1E7 
beta1  0.7385  0.3004  7  2.46  0.0436  0.05  0.02806  1.4488  2.08E6 
s2u  1.9591  1.1903  7  1.65  0.1438  0.05  0.8554  4.7736  2.48E7 
The “Parameter Estimates” table indicates marginal significance of the two fixedeffects parameters (Figure 64.10). The positive value of the estimate of indicates that the treatment significantly increases the chance of a favorable cure.
Figure 64.11: Table of Additional Estimates
Additional Estimates  

Label  Estimate  Standard Error  DF  t Value  Pr > t  Alpha  Lower  Upper 
1/beta1  1.3542  0.5509  7  2.46  0.0436  0.05  0.05146  2.6569 
The “Additional Estimates” table displays results from the ESTIMATE statement (Figure 64.11). The estimate of equals and its standard error equals by the delta method (Billingsley, 1986; Cox, 1998). Note that this particular approximation produces a tstatistic identical to that for the estimate of .
Not shown is the eta
data set, which contains the original 16 observations and predictions of the .