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proc genmod data= ;

class ;

model y = x / dist= link= type3 ;

repeated subject= / corr=  ;

lsmeans x / diff=control ('ref') cl adjust=Dunnett(or tukey);


Parameters estimated with maximum likelihood methods. This is an iterative process, i.e., the computer adjusts the parameter until the log likelihood function is maximised. Repeated statement: specifies the covariance structure of multivariate responses and iterative fitting algorithm for GEE model fitting

DistributionLink functionNote
normalidentitycontinuous dependent variable, mean difference
binomiallogrisk ratio from exponentiation of the parameter estimate 
binomiallogitodds ratio
poissonlogrisk ratio from exponentiation of the parameter estimate; robust error estimates can be requested with the REPEAT statement
categorical response variable with more than two levels 

Sums of squaresEffect typeNote
Type ISequentialThe SS for each factor is the incremental improvement in the error SS as each factor
effect is added to the model. In other words it is the effect as the factor were
considered one at a time into the model, in the order they are entered in the model
Type IIHierarchical or partially sequentialThe SS for each factor is the reduction in residual error obtained by adding that term to a model consisting of all other terms that do not contain the term in question.
Type IIIMarginalThe effect of each variable is evaluated after all other factors have been accounted for.

Adjustment for pairwise comparison

  • Tukey: all pairs
  • Dunnett: paired with control

Generalized Estimating Equations

GEEs are used for analysis of correlated data, e.g., subjects are measured at different points in time, or subjects are clustered, i.e., share a common characteristic. GEE analysis can be performed in GENMOD by specifying a REPEATED statement which provides clustering information and a working correlation matrix. The REPEATED statement requests a GEE analysis

  • Subject: Responses from different subjects are assumed to be statistically independent, and responses within subjects are assumed to be correlated. Variables used in defining the subject-effect must be listed in the CLASS statement. The input data set does not need to be sorted by subject.
  • Corr= specifies the correlation structure: Un unstructured; IND independent.